From 261c59bb1669c1cd9bd046a95c0dc1fa730da871 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 23 Apr 2015 18:32:00 +0100
Subject: [PATCH 001/117] python C extension

---
 gp_emulator/GaussianProcess.py  |  37 ++-
 gp_emulator/__init__.py         |   1 -
 gp_emulator/gpu/Makefile        |  20 ++
 gp_emulator/gpu/_gpu_predict.c  | 400 ++++++++++++++++++++++++++++++++
 gp_emulator/gpu/_gpu_predict.cu | 395 +++++++++++++++++++++++++++++++
 5 files changed, 845 insertions(+), 8 deletions(-)
 create mode 100644 gp_emulator/gpu/Makefile
 create mode 100644 gp_emulator/gpu/_gpu_predict.c
 create mode 100644 gp_emulator/gpu/_gpu_predict.cu

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index f70c38d..c1656d4 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -3,6 +3,7 @@
 import numpy as np
 import scipy.spatial.distance as dist
 import random
+import _gpu_predict
 
 def k_fold_cross_validation(X, K, randomise = False):
     """
@@ -273,6 +274,27 @@ def hessian ( self, testing ):
                 hess[:, d,d2] = np.dot(cc.T, self.invQt)
         return hess
 
+    def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
+        '''GPU predict function
+        '''
+        ( nn, D ) = testing.shape
+        assert D == self.D
+        print self.inputs.shape
+        a =_gpu_predict.predict(self.D,
+            self.theta,
+            self.inputs,
+            self.invQt,
+            self.invQ,
+            testing)
+        print a
+        #print b
+        #print self.theta
+        
+
+
+        
+
+
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
     np.set_printoptions(precision=2, suppress=True)
@@ -283,7 +305,7 @@ def hessian ( self, testing ):
     #target2 = data[ :,1]
     #target3 = data[ :,2]
     #input_obs = data[ :, 3: ]
-    wheat = np.loadtxt("argentine_wheat.dat")[:, 1:]
+    wheat = np.loadtxt("data/argentine_wheat.dat")[:, 1:]
     yields = wheat[:,0]
     mu = yields.mean()
     sigma = yields.std()
@@ -299,12 +321,13 @@ def hessian ( self, testing ):
         inputs_v = validate [ :, 1:]
         gp = GaussianProcess ( inputs_t, yields_t )
         theta_min= gp.learn_hyperparameters (n_tries=2)
-        pred_mu, pred_var, par_dev = gp.predict ( inputs_v )
-        print "TEST"
-        print inputs_v
-        print pred_mu, pred_var, par_dev
-        r = ( yields_v - pred_mu )**2#/pred_var
-        rmse.append ( [ np.sqrt(r.mean()), theta_min[1] ])
+        #pred_mu, pred_var, par_dev = gp.predict ( inputs_v )
+        gp.gpu_predict ( inputs_v )
+        #print "TEST"
+        #print inputs_v
+        #print pred_mu, pred_var, par_dev
+        #r = ( yields_v - pred_mu )**2#/pred_var
+        #rmse.append ( [ np.sqrt(r.mean()), theta_min[1] ])
         
         
     #gp.theta[2] = 0.
diff --git a/gp_emulator/__init__.py b/gp_emulator/__init__.py
index 9d486dc..0411b6a 100644
--- a/gp_emulator/__init__.py
+++ b/gp_emulator/__init__.py
@@ -1,4 +1,3 @@
-
 from GaussianProcess import GaussianProcess, k_fold_cross_validation
 from multivariate_gp import MultivariateEmulator
 from lhd import lhd
diff --git a/gp_emulator/gpu/Makefile b/gp_emulator/gpu/Makefile
new file mode 100644
index 0000000..92502aa
--- /dev/null
+++ b/gp_emulator/gpu/Makefile
@@ -0,0 +1,20 @@
+
+CC = nvcc
+BUILDPATH= ../../build/lib
+OBJ = _gpu_predict
+INC = -I/usr/include/python2.7 -I/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy
+LIB = -lcuda -lcudart -lcublas
+NVLIB = -L/Developer/NVIDIA/CUDA-7.0/lib/
+
+
+$(BUILDPATH)/$(OBJ).so:  $(BUILDPATH)/$(OBJ).o
+	gcc -bundle -flat_namespace -undefined suppress -o $@ $<  $(LIB) $(NVLIB)
+
+$(BUILDPATH)/$(OBJ).o:  $(OBJ).c
+	nvcc  -c $< -o $@ $(NVLIB) $(LIB) $(INC)
+
+clean:
+	rm -r $(BUILDPATH)/*.o $(BUILDPATH)/*.so #$(BUILDPATH)/gp_emulator
+
+
+
diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
new file mode 100644
index 0000000..d2f09a0
--- /dev/null
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -0,0 +1,400 @@
+/* A file to test imorting C modules for handling arrays to Python */
+
+#include "Python.h"
+#include "arrayobject.h"
+//#include "C_arraytest.h"
+#include <math.h>
+#include <stdio.h>
+// #include <stdlib.h>
+// #include <string.h>
+// #include <time.h>
+/* Includes, cuda */
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cublas_v2.h>
+
+#define N 10000
+#define M 250
+
+
+
+//__global__ 
+//void hello(char *a, int *b) 
+//{
+//    a[threadIdx.x] += b[threadIdx.x];
+//}
+ 
+
+//  __global__ void gpu_cdist(float *res, float *a, float *b)
+// {
+//     // int i;
+//     int ix = threadIdx.x+blockDim.x*blockIdx.x;
+//     // int iy = blockIdx.y * blockDim.y + threadIdx.y;
+//     // //int iz = blockIdx.z * blockDim.y + threadIdx.z;
+//     // int nx = N;
+//     // int ny = M;
+
+//     // for(i = 0; i < ny; i++)
+//     // {
+//     //     res[nx*ix+iy]+=pow((a[ny*ix+i] - b[ny*iy+i]),2);
+//     //     //   res[%(N)s*ix+iy]=sqrt(pow((a[%(M)s*ix]-b[%(M)s*iy]),2) + pow((a[%(M)s*ix+1]-b[%(M)s*iy+1]),2));
+//     // }
+
+//     // res[nx*ix+iy]=sqrt(res[nx*ix+iy]);
+
+// }
+
+
+
+static PyObject *comp();
+
+static PyObject *predict(PyObject *self, PyObject *args);
+
+
+PyArrayObject *pyvector(PyObject *objin);
+double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
+double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
+double **ptrvector(long n);
+
+
+// PyArrayObject *pymatrix(PyObject *objin);
+// double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
+// double **ptrvector(long n);
+// void free_Carrayptrs(double **v);
+// int  not_doublematrix(PyArrayObject *mat);
+
+
+
+/* ==== Set up the methods table ====================== */
+static PyMethodDef gpuMethods[] = {
+	{"comp", comp, METH_VARARGS},
+	{"predict", predict, METH_VARARGS},
+	{NULL, NULL}     /* Sentinel - marks the end of this structure */
+};
+
+/* ==== Initialize the C_test functions ====================== */
+//Module name must be _C_arraytest in compile and linked 
+void init_gpu_predict()  {
+	(void) Py_InitModule("_gpu_predict", gpuMethods);
+	import_array();  // Must be present for NumPy.  Called first after above line.
+}
+
+static PyObject *predict ( PyObject *self, PyObject *args )
+{
+    int D;
+    
+    PyArrayObject *py_theta, *py_inputs, *py_invQt, *py_invQ, *py_testing;
+    
+    double *c_theta;
+    double **c_inputs, **c_invQt, **c_invQ, **c_testing;
+    
+    printf("I'm C\n");
+    PyArg_ParseTuple ( args, "iO!O!O!O!O!", &D,
+        &PyArray_Type, &py_theta,
+        &PyArray_Type, &py_inputs,
+        &PyArray_Type, &py_invQt,
+        &PyArray_Type, &py_invQ,
+        &PyArray_Type, &py_testing);
+
+    
+    c_theta = pyvector_to_Carrayptrs( py_theta );
+    c_inputs = pymatrix_to_Carrayptrs( py_inputs );
+    c_invQt = pymatrix_to_Carrayptrs( py_invQt );
+    c_invQ = pymatrix_to_Carrayptrs( py_invQ );
+    c_testing = pymatrix_to_Carrayptrs( py_testing );
+
+
+    //double **g_theta, **g_inputs, **g_in
+
+
+
+
+
+
+
+
+
+
+
+    //free(c_theta);
+    //free((char*) c_inputs);
+    //return Py_BuildValue ("i",1);
+    return Py_BuildValue ( "O", PyArray_Return ( py_testing ) );
+
+}
+
+
+
+
+
+
+
+
+
+
+/* #### Vector Utility functions ######################### */
+
+/* ==== Make a Python Array Obj. from a PyObject, ================
+     generates a double vector w/ contiguous memory which may be a new allocation if
+     the original was not a double type or contiguous 
+  !! Must DECREF the object returned from this routine unless it is returned to the
+     caller of this routines caller using return PyArray_Return(obj) or
+     PyArray_BuildValue with the "N" construct   !!!
+*/
+PyArrayObject *pyvector(PyObject *objin)  {
+    return (PyArrayObject *) PyArray_ContiguousFromObject(objin,
+        NPY_DOUBLE, 1,1);
+}
+/* ==== Create 1D Carray from PyArray ======================
+    Assumes PyArray is contiguous in memory.             */
+double *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
+    int i,n;
+    
+    n=arrayin->dimensions[0];
+    return (double *) arrayin->data;  /* pointer to arrayin data as double */
+}
+/* ==== Check that PyArrayObject is a double (Float) type and a vector ==============
+    return 1 if an error and raise exception */ 
+int  not_doublevector(PyArrayObject *vec)  {
+    if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  {
+        PyErr_SetString(PyExc_ValueError,
+            "In not_doublevector: array must be of type Float and 1 dimensional (n).");
+        return 1;  }
+    return 0;
+}
+
+
+
+/* ==== Create Carray from PyArray ======================
+    Assumes PyArray is contiguous in memory.
+    Memory is allocated!                                    */
+
+double **ptrvector(long n)  {
+    double **v;
+    v=(double **)malloc((size_t) (n*sizeof(double)));
+    if (!v)   {
+        printf("In **ptrvector. Allocation of memory for double array failed.");
+        exit(0);  }
+    return v;
+}
+
+
+double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
+    double **c, *a;
+    int i,n,m;
+    
+    n=arrayin->dimensions[0];
+    m=arrayin->dimensions[1];
+    c=ptrvector(n);
+    a=(double *) arrayin->data;  /* pointer to arrayin data as double */
+    for ( i=0; i<n; i++)  {
+        c[i]=a+i*m;  }
+    return c;
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* ---------------
+for testing
+------------------*/
+int cpu_dot(const float *x, const float *y, float *z){
+    int i;
+    z[0]=0;
+    for(i=0;i<N;i++){
+        z[0]=z[0]+x[i]*y[i];
+    }
+    return(0);
+}
+
+
+
+int check(cublasStatus_t status){
+    if (status != CUBLAS_STATUS_SUCCESS)
+    {
+    
+        fprintf(stderr, "!!!! kernel execution error.\n");
+        return EXIT_FAILURE;
+    
+    }
+    return(0);
+}
+
+static PyObject *comp()
+{
+
+
+	printf("hello world\n");
+    clock_t start,diff;
+    cublasHandle_t handle;
+    cublasStatus_t status;
+    float *x;
+    float *y;
+    float *z,*zg;
+    float *d_x, *d_y, *d_z;
+    int i;
+
+    status = cublasCreate(&handle);
+    x=(float *)malloc(N * sizeof(x[0]));
+    y=(float *)malloc(N * sizeof(y[0]));
+    z=(float *)malloc(sizeof(float));
+    zg=(float *)malloc(  sizeof(float));
+
+
+    cudaMalloc((void **)&d_x, N * sizeof(float));
+    cudaMalloc((void **)&d_y, N * sizeof(float));
+    cudaMalloc((void **)&d_z, sizeof(float));
+
+
+    /*initialise*/
+    for(i=0;i<N;i++){
+        x[i]=0.001;
+        y[i]=0.001;
+        zg[0]=-9999;
+        z[0]=-9999;
+    }
+
+    
+    /*CPU dot product*/
+    start=clock();
+    cpu_dot(x,y,z);
+    diff=clock()-start;
+    int msec = diff * 1000 / CLOCKS_PER_SEC;
+    printf("Time taken (CPU) %d seconds %d milliseconds\n", msec/1000, msec%1000);
+    
+
+
+    /*GPU dot product*/
+    start=clock();
+    cublasSetVector(N,sizeof(float),x,1,d_x,1);
+    cublasSetVector(N,sizeof(float),y,1,d_y,1);
+    check(cublasSdot(handle,N,d_x,1,d_y,1,zg));
+    diff=clock()-start;
+    msec = diff * 1000 / CLOCKS_PER_SEC;
+    printf("Time taken (GPU) %d seconds %d milliseconds\n", msec/1000, msec%1000);
+    
+
+    /*check result*/
+    printf("%f - %f\n",zg[0],z[0]);
+
+
+
+
+
+    free(x);
+    free(y);
+    free(z);
+    free(zg);
+    cudaFree(d_x);
+    cudaFree(d_y);
+    cudaFree(d_z);
+    return Py_BuildValue("i", 1);
+}
+
+
+// // static PyObject *rowx2(PyObject *self, PyObject *args)
+// // {
+// // 	printf("Hello world\n");
+// // }
+
+// /* #### Matrix Extensions ############################## */
+
+// /* ==== Row x 2 function - manipulate matrix in place ======================
+//     Multiply the 2nd row of the input by 2 and put in output
+//     interface:  rowx2(mat1, mat2)
+//                 mat1 and mat2 are NumPy matrices
+//                 Returns integer 1 if successful                        */
+// static PyObject *rowx2(PyObject *self, PyObject *args)
+// {
+// 	PyArrayObject *matin, *matout;  // The python objects to be extracted from the args
+// 	double **cin, **cout;           // The C matrices to be created to point to the 
+// 	                                //   python matrices, cin and cout point to the rows
+// 	                                //   of matin and matout, respectively
+// 	int i,j,n,m;
+	
+// 	/* Parse tuples separately since args will differ between C fcns */
+// 	if (!PyArg_ParseTuple(args, "O!O!", &PyArray_Type, &matin,
+// 		&PyArray_Type, &matout))  return NULL;
+// 	if (NULL == matin)  return NULL;
+// 	if (NULL == matout)  return NULL;
+	
+// 	/* Check that objects are 'double' type and matrices
+// 	     Not needed if python wrapper function checks before call to this routine */
+// 	if (not_doublematrix(matin)) return NULL;
+// 	if (not_doublematrix(matout)) return NULL;
+		
+// 	/* Change contiguous arrays into C ** arrays (Memory is Allocated!) */
+// 	cin=pymatrix_to_Carrayptrs(matin);
+// 	cout=pymatrix_to_Carrayptrs(matout);
+	
+// 	/* Get matrix dimensions. */
+// 	n=matin->dimensions[0];
+// 	m=matin->dimensions[1];
+	
+// 	/* Operate on the matrices  */
+// 	for ( i=0; i<n; i++)  {
+// 		for ( j=0; j<m; j++)  {
+// 			if (i==1) cout[i][j]=2.0*cin[i][j];
+// 	}  }
+		
+// 	/* Free memory, close file and return */
+// 	free_Carrayptrs(cin);
+// 	free_Carrayptrs(cout);
+// 	return Py_BuildValue("i", 1);
+// }
+
+
+
+
+
+// PyArrayObject *pymatrix(PyObject *objin)  {
+// 	return (PyArrayObject *) PyArray_ContiguousFromObject(objin,
+// 		NPY_DOUBLE, 2,2);
+// }
+
+
+
+// void free_Carrayptrs(double **v)  {
+// 	free((char*) v);
+// }
+
+// int  not_doublematrix(PyArrayObject *mat)  {
+// 	if (mat->descr->type_num != NPY_DOUBLE || mat->nd != 2)  {
+// 		PyErr_SetString(PyExc_ValueError,
+// 			"In not_doublematrix: array must be of type Float and 2 dimensional (n x m).");
+// 		return 1;  }
+// 	return 0;
+// }
+
diff --git a/gp_emulator/gpu/_gpu_predict.cu b/gp_emulator/gpu/_gpu_predict.cu
new file mode 100644
index 0000000..7dd90c1
--- /dev/null
+++ b/gp_emulator/gpu/_gpu_predict.cu
@@ -0,0 +1,395 @@
+/* A file to test imorting C modules for handling arrays to Python */
+
+#include "Python.h"
+#include "arrayobject.h"
+//#include "C_arraytest.h"
+#include <math.h>
+#include <stdio.h>
+// #include <stdlib.h>
+// #include <string.h>
+// #include <time.h>
+/* Includes, cuda */
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cublas_v2.h>
+
+#define N 10000
+#define M 250
+
+
+
+ 
+
+//  __global__ void gpu_cdist(float *res, float *a, float *b)
+// {
+//     // int i;
+//     int ix = threadIdx.x+blockDim.x*blockIdx.x;
+//     // int iy = blockIdx.y * blockDim.y + threadIdx.y;
+//     // //int iz = blockIdx.z * blockDim.y + threadIdx.z;
+//     // int nx = N;
+//     // int ny = M;
+
+//     // for(i = 0; i < ny; i++)
+//     // {
+//     //     res[nx*ix+iy]+=pow((a[ny*ix+i] - b[ny*iy+i]),2);
+//     //     //   res[%(N)s*ix+iy]=sqrt(pow((a[%(M)s*ix]-b[%(M)s*iy]),2) + pow((a[%(M)s*ix+1]-b[%(M)s*iy+1]),2));
+//     // }
+
+//     // res[nx*ix+iy]=sqrt(res[nx*ix+iy]);
+
+// }
+
+
+
+static PyObject *comp();
+
+static PyObject *predict(PyObject *self, PyObject *args);
+
+
+PyArrayObject *pyvector(PyObject *objin);
+double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
+double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
+double **ptrvector(long n);
+
+
+// PyArrayObject *pymatrix(PyObject *objin);
+// double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
+// double **ptrvector(long n);
+// void free_Carrayptrs(double **v);
+// int  not_doublematrix(PyArrayObject *mat);
+
+
+
+/* ==== Set up the methods table ====================== */
+static PyMethodDef gpuMethods[] = {
+	{"comp", comp, METH_VARARGS},
+	{"predict", predict, METH_VARARGS},
+	{NULL, NULL}     /* Sentinel - marks the end of this structure */
+};
+
+/* ==== Initialize the C_test functions ====================== */
+//Module name must be _C_arraytest in compile and linked 
+void init_gpu_predict()  {
+	(void) Py_InitModule("_gpu_predict", gpuMethods);
+	import_array();  // Must be present for NumPy.  Called first after above line.
+}
+
+static PyObject *predict ( PyObject *self, PyObject *args )
+{
+    int D;
+    
+    PyArrayObject *py_theta, *py_inputs, *py_invQt, *py_invQ, *py_testing;
+    
+    double *c_theta;
+    double **c_inputs, **c_invQt, **c_invQ, **c_testing;
+    
+    printf("I'm C\n");
+    PyArg_ParseTuple ( args, "iO!O!O!O!O!", &D,
+        &PyArray_Type, &py_theta,
+        &PyArray_Type, &py_inputs,
+        &PyArray_Type, &py_invQt,
+        &PyArray_Type, &py_invQ,
+        &PyArray_Type, &py_testing);
+
+    
+    c_theta = pyvector_to_Carrayptrs( py_theta );
+    c_inputs = pymatrix_to_Carrayptrs( py_inputs );
+    c_invQt = pymatrix_to_Carrayptrs( py_invQt );
+    c_invQ = pymatrix_to_Carrayptrs( py_invQ );
+    c_testing = pymatrix_to_Carrayptrs( py_testing );
+
+
+    //double **g_theta, **g_inputs, **g_in
+
+
+
+
+
+
+
+
+
+
+
+    //free(c_theta);
+    //free((char*) c_inputs);
+    //return Py_BuildValue ("i",1);
+    return Py_BuildValue ( "O", PyArray_Return ( py_testing ) );
+
+}
+
+
+
+
+
+
+
+
+
+
+/* #### Vector Utility functions ######################### */
+
+/* ==== Make a Python Array Obj. from a PyObject, ================
+     generates a double vector w/ contiguous memory which may be a new allocation if
+     the original was not a double type or contiguous 
+  !! Must DECREF the object returned from this routine unless it is returned to the
+     caller of this routines caller using return PyArray_Return(obj) or
+     PyArray_BuildValue with the "N" construct   !!!
+*/
+PyArrayObject *pyvector(PyObject *objin)  {
+    return (PyArrayObject *) PyArray_ContiguousFromObject(objin,
+        NPY_DOUBLE, 1,1);
+}
+/* ==== Create 1D Carray from PyArray ======================
+    Assumes PyArray is contiguous in memory.             */
+double *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
+    int i,n;
+    
+    n=arrayin->dimensions[0];
+    return (double *) arrayin->data;  /* pointer to arrayin data as double */
+}
+/* ==== Check that PyArrayObject is a double (Float) type and a vector ==============
+    return 1 if an error and raise exception */ 
+int  not_doublevector(PyArrayObject *vec)  {
+    if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  {
+        PyErr_SetString(PyExc_ValueError,
+            "In not_doublevector: array must be of type Float and 1 dimensional (n).");
+        return 1;  }
+    return 0;
+}
+
+
+
+/* ==== Create Carray from PyArray ======================
+    Assumes PyArray is contiguous in memory.
+    Memory is allocated!                                    */
+
+double **ptrvector(long n)  {
+    double **v;
+    v=(double **)malloc((size_t) (n*sizeof(double)));
+    if (!v)   {
+        printf("In **ptrvector. Allocation of memory for double array failed.");
+        exit(0);  }
+    return v;
+}
+
+
+double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
+    double **c, *a;
+    int i,n,m;
+    
+    n=arrayin->dimensions[0];
+    m=arrayin->dimensions[1];
+    c=ptrvector(n);
+    a=(double *) arrayin->data;  /* pointer to arrayin data as double */
+    for ( i=0; i<n; i++)  {
+        c[i]=a+i*m;  }
+    return c;
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* ---------------
+for testing
+------------------*/
+int cpu_dot(const float *x, const float *y, float *z){
+    int i;
+    z[0]=0;
+    for(i=0;i<N;i++){
+        z[0]=z[0]+x[i]*y[i];
+    }
+    return(0);
+}
+
+
+
+int check(cublasStatus_t status){
+    if (status != CUBLAS_STATUS_SUCCESS)
+    {
+    
+        fprintf(stderr, "!!!! kernel execution error.\n");
+        return EXIT_FAILURE;
+    
+    }
+    return(0);
+}
+
+static PyObject *comp()
+{
+
+
+	printf("hello world\n");
+    clock_t start,diff;
+    cublasHandle_t handle;
+    cublasStatus_t status;
+    float *x;
+    float *y;
+    float *z,*zg;
+    float *d_x, *d_y, *d_z;
+    int i;
+
+    status = cublasCreate(&handle);
+    x=(float *)malloc(N * sizeof(x[0]));
+    y=(float *)malloc(N * sizeof(y[0]));
+    z=(float *)malloc(sizeof(float));
+    zg=(float *)malloc(  sizeof(float));
+
+
+    cudaMalloc((void **)&d_x, N * sizeof(float));
+    cudaMalloc((void **)&d_y, N * sizeof(float));
+    cudaMalloc((void **)&d_z, sizeof(float));
+
+
+    /*initialise*/
+    for(i=0;i<N;i++){
+        x[i]=0.001;
+        y[i]=0.001;
+        zg[0]=-9999;
+        z[0]=-9999;
+    }
+
+    
+    /*CPU dot product*/
+    start=clock();
+    cpu_dot(x,y,z);
+    diff=clock()-start;
+    int msec = diff * 1000 / CLOCKS_PER_SEC;
+    printf("Time taken (CPU) %d seconds %d milliseconds\n", msec/1000, msec%1000);
+    
+
+
+    /*GPU dot product*/
+    start=clock();
+    cublasSetVector(N,sizeof(float),x,1,d_x,1);
+    cublasSetVector(N,sizeof(float),y,1,d_y,1);
+    check(cublasSdot(handle,N,d_x,1,d_y,1,zg));
+    diff=clock()-start;
+    msec = diff * 1000 / CLOCKS_PER_SEC;
+    printf("Time taken (GPU) %d seconds %d milliseconds\n", msec/1000, msec%1000);
+    
+
+    /*check result*/
+    printf("%f - %f\n",zg[0],z[0]);
+
+
+
+
+
+    free(x);
+    free(y);
+    free(z);
+    free(zg);
+    cudaFree(d_x);
+    cudaFree(d_y);
+    cudaFree(d_z);
+    return Py_BuildValue("i", 1);
+}
+
+
+// // static PyObject *rowx2(PyObject *self, PyObject *args)
+// // {
+// // 	printf("Hello world\n");
+// // }
+
+// /* #### Matrix Extensions ############################## */
+
+// /* ==== Row x 2 function - manipulate matrix in place ======================
+//     Multiply the 2nd row of the input by 2 and put in output
+//     interface:  rowx2(mat1, mat2)
+//                 mat1 and mat2 are NumPy matrices
+//                 Returns integer 1 if successful                        */
+// static PyObject *rowx2(PyObject *self, PyObject *args)
+// {
+// 	PyArrayObject *matin, *matout;  // The python objects to be extracted from the args
+// 	double **cin, **cout;           // The C matrices to be created to point to the 
+// 	                                //   python matrices, cin and cout point to the rows
+// 	                                //   of matin and matout, respectively
+// 	int i,j,n,m;
+	
+// 	/* Parse tuples separately since args will differ between C fcns */
+// 	if (!PyArg_ParseTuple(args, "O!O!", &PyArray_Type, &matin,
+// 		&PyArray_Type, &matout))  return NULL;
+// 	if (NULL == matin)  return NULL;
+// 	if (NULL == matout)  return NULL;
+	
+// 	/* Check that objects are 'double' type and matrices
+// 	     Not needed if python wrapper function checks before call to this routine */
+// 	if (not_doublematrix(matin)) return NULL;
+// 	if (not_doublematrix(matout)) return NULL;
+		
+// 	/* Change contiguous arrays into C ** arrays (Memory is Allocated!) */
+// 	cin=pymatrix_to_Carrayptrs(matin);
+// 	cout=pymatrix_to_Carrayptrs(matout);
+	
+// 	/* Get matrix dimensions. */
+// 	n=matin->dimensions[0];
+// 	m=matin->dimensions[1];
+	
+// 	/* Operate on the matrices  */
+// 	for ( i=0; i<n; i++)  {
+// 		for ( j=0; j<m; j++)  {
+// 			if (i==1) cout[i][j]=2.0*cin[i][j];
+// 	}  }
+		
+// 	/* Free memory, close file and return */
+// 	free_Carrayptrs(cin);
+// 	free_Carrayptrs(cout);
+// 	return Py_BuildValue("i", 1);
+// }
+
+
+
+
+
+// PyArrayObject *pymatrix(PyObject *objin)  {
+// 	return (PyArrayObject *) PyArray_ContiguousFromObject(objin,
+// 		NPY_DOUBLE, 2,2);
+// }
+
+
+
+// void free_Carrayptrs(double **v)  {
+// 	free((char*) v);
+// }
+
+// int  not_doublematrix(PyArrayObject *mat)  {
+// 	if (mat->descr->type_num != NPY_DOUBLE || mat->nd != 2)  {
+// 		PyErr_SetString(PyExc_ValueError,
+// 			"In not_doublematrix: array must be of type Float and 2 dimensional (n x m).");
+// 		return 1;  }
+// 	return 0;
+// }
+

From 4641ce666baca42bb2f7831bdec45b5af39a1395 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 27 Apr 2015 14:22:49 +0100
Subject: [PATCH 002/117] establish gpu folder; Makefile; python ctype;
 (scratch)

---
 gp_emulator/GaussianProcess.py  |  11 +-
 gp_emulator/gpu/Makefile        |  27 ++-
 gp_emulator/gpu/_gpu_predict.cu | 395 --------------------------------
 gp_emulator/gpu/cdist.cu        |  28 +++
 gp_emulator/gpu/gpu_predict.h   |  38 +++
 gp_emulator/gpu/test.py         |  46 ++++
 6 files changed, 134 insertions(+), 411 deletions(-)
 delete mode 100644 gp_emulator/gpu/_gpu_predict.cu
 create mode 100644 gp_emulator/gpu/cdist.cu
 create mode 100644 gp_emulator/gpu/gpu_predict.h
 create mode 100644 gp_emulator/gpu/test.py

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index c1656d4..36a3f3f 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -226,25 +226,24 @@ def predict ( self, testing, do_unc=True ):
 
         ( nn, D ) = testing.shape
         assert D == self.D
-        
+
         expX = np.exp ( self.theta )
         
         a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, \
             np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
-        
         a = expX[self.D]*np.exp(-0.5*a)
         b = expX[self.D]
         
         mu = np.dot( a.T, self.invQt)
+
         if do_unc:
-	    var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
+	       var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
         # Derivative and partial derivatives of the function
         deriv = np.zeros ( ( nn, self.D ) )
 
         for d in xrange ( self.D ):
             aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
             c = a*aa.T
-
             deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
         if do_unc:
             return mu, var, deriv
@@ -321,8 +320,8 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         inputs_v = validate [ :, 1:]
         gp = GaussianProcess ( inputs_t, yields_t )
         theta_min= gp.learn_hyperparameters (n_tries=2)
-        #pred_mu, pred_var, par_dev = gp.predict ( inputs_v )
-        gp.gpu_predict ( inputs_v )
+        pred_mu, pred_var, par_dev = gp.predict ( inputs_v )
+        #gp.gpu_predict ( inputs_v )
         #print "TEST"
         #print inputs_v
         #print pred_mu, pred_var, par_dev
diff --git a/gp_emulator/gpu/Makefile b/gp_emulator/gpu/Makefile
index 92502aa..d187e27 100644
--- a/gp_emulator/gpu/Makefile
+++ b/gp_emulator/gpu/Makefile
@@ -1,20 +1,27 @@
-
-CC = nvcc
-BUILDPATH= ../../build/lib
-OBJ = _gpu_predict
+SRCS= _gpu_predict.c cdist.cu
+OBJS = _gpu_predict.o cdist.o
+SHARE = _gpu_predict.so #$(SRCS:.c=.so) $(CUDASRCS:.cu=.so)
+GPUCC = nvcc
+CC = gcc
+BUILDPATH= ../../build/lib/
 INC = -I/usr/include/python2.7 -I/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy
 LIB = -lcuda -lcudart -lcublas
 NVLIB = -L/Developer/NVIDIA/CUDA-7.0/lib/
 
 
-$(BUILDPATH)/$(OBJ).so:  $(BUILDPATH)/$(OBJ).o
-	gcc -bundle -flat_namespace -undefined suppress -o $@ $<  $(LIB) $(NVLIB)
 
-$(BUILDPATH)/$(OBJ).o:  $(OBJ).c
-	nvcc  -c $< -o $@ $(NVLIB) $(LIB) $(INC)
 
-clean:
-	rm -r $(BUILDPATH)/*.o $(BUILDPATH)/*.so #$(BUILDPATH)/gp_emulator
+all: $(SHARE)
+
 
+%.so:$(OBJS)
+	$(CC) -bundle -flat_namespace -undefined suppress -o $(BUILDPATH)$@ $^ $(INC) $(NVLIB) $(LIB)
 
+%.o:%.c
+	$(GPUCC)  -c $^  -o $@  $(INC) $(LIB) $(NVLIB)
 
+%.o:%.cu
+	$(GPUCC) -c $^ -o $@ $(INC) $(NVLIB) $(LIB)
+
+clean:
+	rm *.o $(BUILDPATH)*.so
diff --git a/gp_emulator/gpu/_gpu_predict.cu b/gp_emulator/gpu/_gpu_predict.cu
deleted file mode 100644
index 7dd90c1..0000000
--- a/gp_emulator/gpu/_gpu_predict.cu
+++ /dev/null
@@ -1,395 +0,0 @@
-/* A file to test imorting C modules for handling arrays to Python */
-
-#include "Python.h"
-#include "arrayobject.h"
-//#include "C_arraytest.h"
-#include <math.h>
-#include <stdio.h>
-// #include <stdlib.h>
-// #include <string.h>
-// #include <time.h>
-/* Includes, cuda */
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cublas_v2.h>
-
-#define N 10000
-#define M 250
-
-
-
- 
-
-//  __global__ void gpu_cdist(float *res, float *a, float *b)
-// {
-//     // int i;
-//     int ix = threadIdx.x+blockDim.x*blockIdx.x;
-//     // int iy = blockIdx.y * blockDim.y + threadIdx.y;
-//     // //int iz = blockIdx.z * blockDim.y + threadIdx.z;
-//     // int nx = N;
-//     // int ny = M;
-
-//     // for(i = 0; i < ny; i++)
-//     // {
-//     //     res[nx*ix+iy]+=pow((a[ny*ix+i] - b[ny*iy+i]),2);
-//     //     //   res[%(N)s*ix+iy]=sqrt(pow((a[%(M)s*ix]-b[%(M)s*iy]),2) + pow((a[%(M)s*ix+1]-b[%(M)s*iy+1]),2));
-//     // }
-
-//     // res[nx*ix+iy]=sqrt(res[nx*ix+iy]);
-
-// }
-
-
-
-static PyObject *comp();
-
-static PyObject *predict(PyObject *self, PyObject *args);
-
-
-PyArrayObject *pyvector(PyObject *objin);
-double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
-double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
-double **ptrvector(long n);
-
-
-// PyArrayObject *pymatrix(PyObject *objin);
-// double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
-// double **ptrvector(long n);
-// void free_Carrayptrs(double **v);
-// int  not_doublematrix(PyArrayObject *mat);
-
-
-
-/* ==== Set up the methods table ====================== */
-static PyMethodDef gpuMethods[] = {
-	{"comp", comp, METH_VARARGS},
-	{"predict", predict, METH_VARARGS},
-	{NULL, NULL}     /* Sentinel - marks the end of this structure */
-};
-
-/* ==== Initialize the C_test functions ====================== */
-//Module name must be _C_arraytest in compile and linked 
-void init_gpu_predict()  {
-	(void) Py_InitModule("_gpu_predict", gpuMethods);
-	import_array();  // Must be present for NumPy.  Called first after above line.
-}
-
-static PyObject *predict ( PyObject *self, PyObject *args )
-{
-    int D;
-    
-    PyArrayObject *py_theta, *py_inputs, *py_invQt, *py_invQ, *py_testing;
-    
-    double *c_theta;
-    double **c_inputs, **c_invQt, **c_invQ, **c_testing;
-    
-    printf("I'm C\n");
-    PyArg_ParseTuple ( args, "iO!O!O!O!O!", &D,
-        &PyArray_Type, &py_theta,
-        &PyArray_Type, &py_inputs,
-        &PyArray_Type, &py_invQt,
-        &PyArray_Type, &py_invQ,
-        &PyArray_Type, &py_testing);
-
-    
-    c_theta = pyvector_to_Carrayptrs( py_theta );
-    c_inputs = pymatrix_to_Carrayptrs( py_inputs );
-    c_invQt = pymatrix_to_Carrayptrs( py_invQt );
-    c_invQ = pymatrix_to_Carrayptrs( py_invQ );
-    c_testing = pymatrix_to_Carrayptrs( py_testing );
-
-
-    //double **g_theta, **g_inputs, **g_in
-
-
-
-
-
-
-
-
-
-
-
-    //free(c_theta);
-    //free((char*) c_inputs);
-    //return Py_BuildValue ("i",1);
-    return Py_BuildValue ( "O", PyArray_Return ( py_testing ) );
-
-}
-
-
-
-
-
-
-
-
-
-
-/* #### Vector Utility functions ######################### */
-
-/* ==== Make a Python Array Obj. from a PyObject, ================
-     generates a double vector w/ contiguous memory which may be a new allocation if
-     the original was not a double type or contiguous 
-  !! Must DECREF the object returned from this routine unless it is returned to the
-     caller of this routines caller using return PyArray_Return(obj) or
-     PyArray_BuildValue with the "N" construct   !!!
-*/
-PyArrayObject *pyvector(PyObject *objin)  {
-    return (PyArrayObject *) PyArray_ContiguousFromObject(objin,
-        NPY_DOUBLE, 1,1);
-}
-/* ==== Create 1D Carray from PyArray ======================
-    Assumes PyArray is contiguous in memory.             */
-double *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
-    int i,n;
-    
-    n=arrayin->dimensions[0];
-    return (double *) arrayin->data;  /* pointer to arrayin data as double */
-}
-/* ==== Check that PyArrayObject is a double (Float) type and a vector ==============
-    return 1 if an error and raise exception */ 
-int  not_doublevector(PyArrayObject *vec)  {
-    if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  {
-        PyErr_SetString(PyExc_ValueError,
-            "In not_doublevector: array must be of type Float and 1 dimensional (n).");
-        return 1;  }
-    return 0;
-}
-
-
-
-/* ==== Create Carray from PyArray ======================
-    Assumes PyArray is contiguous in memory.
-    Memory is allocated!                                    */
-
-double **ptrvector(long n)  {
-    double **v;
-    v=(double **)malloc((size_t) (n*sizeof(double)));
-    if (!v)   {
-        printf("In **ptrvector. Allocation of memory for double array failed.");
-        exit(0);  }
-    return v;
-}
-
-
-double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
-    double **c, *a;
-    int i,n,m;
-    
-    n=arrayin->dimensions[0];
-    m=arrayin->dimensions[1];
-    c=ptrvector(n);
-    a=(double *) arrayin->data;  /* pointer to arrayin data as double */
-    for ( i=0; i<n; i++)  {
-        c[i]=a+i*m;  }
-    return c;
-}
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-/* ---------------
-for testing
-------------------*/
-int cpu_dot(const float *x, const float *y, float *z){
-    int i;
-    z[0]=0;
-    for(i=0;i<N;i++){
-        z[0]=z[0]+x[i]*y[i];
-    }
-    return(0);
-}
-
-
-
-int check(cublasStatus_t status){
-    if (status != CUBLAS_STATUS_SUCCESS)
-    {
-    
-        fprintf(stderr, "!!!! kernel execution error.\n");
-        return EXIT_FAILURE;
-    
-    }
-    return(0);
-}
-
-static PyObject *comp()
-{
-
-
-	printf("hello world\n");
-    clock_t start,diff;
-    cublasHandle_t handle;
-    cublasStatus_t status;
-    float *x;
-    float *y;
-    float *z,*zg;
-    float *d_x, *d_y, *d_z;
-    int i;
-
-    status = cublasCreate(&handle);
-    x=(float *)malloc(N * sizeof(x[0]));
-    y=(float *)malloc(N * sizeof(y[0]));
-    z=(float *)malloc(sizeof(float));
-    zg=(float *)malloc(  sizeof(float));
-
-
-    cudaMalloc((void **)&d_x, N * sizeof(float));
-    cudaMalloc((void **)&d_y, N * sizeof(float));
-    cudaMalloc((void **)&d_z, sizeof(float));
-
-
-    /*initialise*/
-    for(i=0;i<N;i++){
-        x[i]=0.001;
-        y[i]=0.001;
-        zg[0]=-9999;
-        z[0]=-9999;
-    }
-
-    
-    /*CPU dot product*/
-    start=clock();
-    cpu_dot(x,y,z);
-    diff=clock()-start;
-    int msec = diff * 1000 / CLOCKS_PER_SEC;
-    printf("Time taken (CPU) %d seconds %d milliseconds\n", msec/1000, msec%1000);
-    
-
-
-    /*GPU dot product*/
-    start=clock();
-    cublasSetVector(N,sizeof(float),x,1,d_x,1);
-    cublasSetVector(N,sizeof(float),y,1,d_y,1);
-    check(cublasSdot(handle,N,d_x,1,d_y,1,zg));
-    diff=clock()-start;
-    msec = diff * 1000 / CLOCKS_PER_SEC;
-    printf("Time taken (GPU) %d seconds %d milliseconds\n", msec/1000, msec%1000);
-    
-
-    /*check result*/
-    printf("%f - %f\n",zg[0],z[0]);
-
-
-
-
-
-    free(x);
-    free(y);
-    free(z);
-    free(zg);
-    cudaFree(d_x);
-    cudaFree(d_y);
-    cudaFree(d_z);
-    return Py_BuildValue("i", 1);
-}
-
-
-// // static PyObject *rowx2(PyObject *self, PyObject *args)
-// // {
-// // 	printf("Hello world\n");
-// // }
-
-// /* #### Matrix Extensions ############################## */
-
-// /* ==== Row x 2 function - manipulate matrix in place ======================
-//     Multiply the 2nd row of the input by 2 and put in output
-//     interface:  rowx2(mat1, mat2)
-//                 mat1 and mat2 are NumPy matrices
-//                 Returns integer 1 if successful                        */
-// static PyObject *rowx2(PyObject *self, PyObject *args)
-// {
-// 	PyArrayObject *matin, *matout;  // The python objects to be extracted from the args
-// 	double **cin, **cout;           // The C matrices to be created to point to the 
-// 	                                //   python matrices, cin and cout point to the rows
-// 	                                //   of matin and matout, respectively
-// 	int i,j,n,m;
-	
-// 	/* Parse tuples separately since args will differ between C fcns */
-// 	if (!PyArg_ParseTuple(args, "O!O!", &PyArray_Type, &matin,
-// 		&PyArray_Type, &matout))  return NULL;
-// 	if (NULL == matin)  return NULL;
-// 	if (NULL == matout)  return NULL;
-	
-// 	/* Check that objects are 'double' type and matrices
-// 	     Not needed if python wrapper function checks before call to this routine */
-// 	if (not_doublematrix(matin)) return NULL;
-// 	if (not_doublematrix(matout)) return NULL;
-		
-// 	/* Change contiguous arrays into C ** arrays (Memory is Allocated!) */
-// 	cin=pymatrix_to_Carrayptrs(matin);
-// 	cout=pymatrix_to_Carrayptrs(matout);
-	
-// 	/* Get matrix dimensions. */
-// 	n=matin->dimensions[0];
-// 	m=matin->dimensions[1];
-	
-// 	/* Operate on the matrices  */
-// 	for ( i=0; i<n; i++)  {
-// 		for ( j=0; j<m; j++)  {
-// 			if (i==1) cout[i][j]=2.0*cin[i][j];
-// 	}  }
-		
-// 	/* Free memory, close file and return */
-// 	free_Carrayptrs(cin);
-// 	free_Carrayptrs(cout);
-// 	return Py_BuildValue("i", 1);
-// }
-
-
-
-
-
-// PyArrayObject *pymatrix(PyObject *objin)  {
-// 	return (PyArrayObject *) PyArray_ContiguousFromObject(objin,
-// 		NPY_DOUBLE, 2,2);
-// }
-
-
-
-// void free_Carrayptrs(double **v)  {
-// 	free((char*) v);
-// }
-
-// int  not_doublematrix(PyArrayObject *mat)  {
-// 	if (mat->descr->type_num != NPY_DOUBLE || mat->nd != 2)  {
-// 		PyErr_SetString(PyExc_ValueError,
-// 			"In not_doublematrix: array must be of type Float and 2 dimensional (n x m).");
-// 		return 1;  }
-// 	return 0;
-// }
-
diff --git a/gp_emulator/gpu/cdist.cu b/gp_emulator/gpu/cdist.cu
new file mode 100644
index 0000000..29f1ac1
--- /dev/null
+++ b/gp_emulator/gpu/cdist.cu
@@ -0,0 +1,28 @@
+//#include<cuda.h>
+#include<stdio.h>
+#include "gpu_predict.h"
+
+
+ 
+
+
+__global__ void gpu_cdist(float *res, float *a, float *b)
+{
+/*
+    int i;
+     int ix = threadIdx.x+blockDim.x*blockIdx.x;
+     int iy = blockIdx.y * blockDim.y + threadIdx.y;
+     int nx = N;
+     int ny = M;
+     for(i = 0; i < ny; i++)
+     {
+     //res[nx*ix+iy]+=pow((a[ny*ix+i] - b[ny*iy+i]),2);
+        // res[%(N)s*ix+iy]=sqrt(pow((a[%(M)s*ix]-b[%(M)s*iy]),2) + pow((a[%(M)s*ix+1]-b[%(M)s*iy+1]),2));
+     }
+     
+     res[nx*ix+iy]=sqrt(res[nx*ix+iy]);
+*/
+
+}
+
+
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
new file mode 100644
index 0000000..ac147e5
--- /dev/null
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -0,0 +1,38 @@
+/* A file to test imorting C modules for handling arrays to Python */
+
+#include "Python.h"
+#include "arrayobject.h"
+//#include "C_arraytest.h"
+#include <math.h>
+#include <stdio.h>
+// #include <stdlib.h>
+// #include <string.h>
+// #include <time.h>
+/* Includes, cuda */
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cublas_v2.h>
+
+
+/*
+* he
+*/
+//#ifndef GPU_PREDICT_H
+//#define GPU_PREDICT_H
+
+
+void hi();
+//void hello();
+
+
+static PyObject *comp();
+
+static PyObject *predict(PyObject *self, PyObject *args);
+
+PyArrayObject *pyvector(PyObject *objin);
+double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
+double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
+double **ptrvector(long n);
+
+//#endif
+
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
new file mode 100644
index 0000000..f74bc67
--- /dev/null
+++ b/gp_emulator/gpu/test.py
@@ -0,0 +1,46 @@
+import numpy as np
+import scipy.spatial.distance as dist
+class GP:
+    def __init__ (self, P, N, M):
+        self.D = P
+        self.theta = np.random.random((P+2))
+        self.inputs = np.random.random ((M,P))
+        self.invQ = np.random.random((M,M))
+        self.invQt = np.random.random ((M))
+
+    @profile
+    def predict ( self, testing, do_unc=True ):
+        ( nn, D ) = testing.shape
+        assert D == self.D
+
+        expX = np.exp ( self.theta )
+        
+        a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+        a = expX[self.D]*np.exp(-0.5*a)
+        b = expX[self.D]
+        
+        mu = np.dot( a.T, self.invQt)
+
+        if do_unc:
+	       var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
+        # Derivative and partial derivatives of the function
+        deriv = np.zeros ( ( nn, self.D ) )
+
+        for d in xrange ( self.D ):
+            aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
+            c = a*aa.T
+            deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
+        if do_unc:
+            return mu, var, deriv
+        else:
+	    return mu, deriv
+
+
+if __name__ == '__main__':
+    N=4e5
+    M=250
+    P=10
+    testing=np.ones((N,P))
+    gp=GP(P,N,M)
+    gp.predict(testing)
+    

From e543e3f48201d2972f706349265af839080ce24c Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 29 Apr 2015 15:25:52 +0100
Subject: [PATCH 003/117] update the structure of the code and add one dummy
 global function cdist

---
 gp_emulator/gpu/_gpu_predict.c | 306 +++------------------------------
 gp_emulator/gpu/cdist.cu       |  15 +-
 gp_emulator/gpu/predict.cu     |  49 ++++++
 gp_emulator/gpu/test.py        |  39 ++++-
 4 files changed, 123 insertions(+), 286 deletions(-)
 create mode 100644 gp_emulator/gpu/predict.cu

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index d2f09a0..1294194 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -1,61 +1,8 @@
-/* A file to test imorting C modules for handling arrays to Python */
 
-#include "Python.h"
-#include "arrayobject.h"
-//#include "C_arraytest.h"
-#include <math.h>
-#include <stdio.h>
-// #include <stdlib.h>
-// #include <string.h>
-// #include <time.h>
-/* Includes, cuda */
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cublas_v2.h>
+#include "gpu_predict.h"
 
-#define N 10000
-#define M 250
-
-
-
-//__global__ 
-//void hello(char *a, int *b) 
-//{
-//    a[threadIdx.x] += b[threadIdx.x];
-//}
- 
-
-//  __global__ void gpu_cdist(float *res, float *a, float *b)
-// {
-//     // int i;
-//     int ix = threadIdx.x+blockDim.x*blockIdx.x;
-//     // int iy = blockIdx.y * blockDim.y + threadIdx.y;
-//     // //int iz = blockIdx.z * blockDim.y + threadIdx.z;
-//     // int nx = N;
-//     // int ny = M;
-
-//     // for(i = 0; i < ny; i++)
-//     // {
-//     //     res[nx*ix+iy]+=pow((a[ny*ix+i] - b[ny*iy+i]),2);
-//     //     //   res[%(N)s*ix+iy]=sqrt(pow((a[%(M)s*ix]-b[%(M)s*iy]),2) + pow((a[%(M)s*ix+1]-b[%(M)s*iy+1]),2));
-//     // }
-
-//     // res[nx*ix+iy]=sqrt(res[nx*ix+iy]);
-
-// }
-
-
-
-static PyObject *comp();
-
-static PyObject *predict(PyObject *self, PyObject *args);
-
-
-PyArrayObject *pyvector(PyObject *objin);
-double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
-double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
-double **ptrvector(long n);
 
+#define real double
 
 // PyArrayObject *pymatrix(PyObject *objin);
 // double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
@@ -67,8 +14,7 @@ double **ptrvector(long n);
 
 /* ==== Set up the methods table ====================== */
 static PyMethodDef gpuMethods[] = {
-	{"comp", comp, METH_VARARGS},
-	{"predict", predict, METH_VARARGS},
+    {"predict_wrap", predict_wrap, METH_VARARGS},
 	{NULL, NULL}     /* Sentinel - marks the end of this structure */
 };
 
@@ -79,55 +25,6 @@ void init_gpu_predict()  {
 	import_array();  // Must be present for NumPy.  Called first after above line.
 }
 
-static PyObject *predict ( PyObject *self, PyObject *args )
-{
-    int D;
-    
-    PyArrayObject *py_theta, *py_inputs, *py_invQt, *py_invQ, *py_testing;
-    
-    double *c_theta;
-    double **c_inputs, **c_invQt, **c_invQ, **c_testing;
-    
-    printf("I'm C\n");
-    PyArg_ParseTuple ( args, "iO!O!O!O!O!", &D,
-        &PyArray_Type, &py_theta,
-        &PyArray_Type, &py_inputs,
-        &PyArray_Type, &py_invQt,
-        &PyArray_Type, &py_invQ,
-        &PyArray_Type, &py_testing);
-
-    
-    c_theta = pyvector_to_Carrayptrs( py_theta );
-    c_inputs = pymatrix_to_Carrayptrs( py_inputs );
-    c_invQt = pymatrix_to_Carrayptrs( py_invQt );
-    c_invQ = pymatrix_to_Carrayptrs( py_invQ );
-    c_testing = pymatrix_to_Carrayptrs( py_testing );
-
-
-    //double **g_theta, **g_inputs, **g_in
-
-
-
-
-
-
-
-
-
-
-
-    //free(c_theta);
-    //free((char*) c_inputs);
-    //return Py_BuildValue ("i",1);
-    return Py_BuildValue ( "O", PyArray_Return ( py_testing ) );
-
-}
-
-
-
-
-
-
 
 
 
@@ -196,8 +93,32 @@ double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
 
 
 
+PyObject *predict_wrap ( PyObject *self, PyObject *args )
+{
+    int N,NN,D,theta_size;
+    
+    PyArrayObject *py_theta_exp, *py_inputs, *py_invQt, *py_invQ, *py_testing;
+    real *c_theta_exp;
+    real *c_inputs, *c_invQt, *c_invQ, *c_testing;
 
 
+    PyArg_ParseTuple ( args, "O!O!O!O!O!iiii",
+        &PyArray_Type, &py_theta_exp,
+        &PyArray_Type, &py_inputs,
+        &PyArray_Type, &py_invQt,
+        &PyArray_Type, &py_invQ,
+        &PyArray_Type, &py_testing,
+        &N,&NN,&D,&theta_size);
+
+    c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp );
+    c_inputs = pyvector_to_Carrayptrs( py_inputs );
+    c_invQt = pyvector_to_Carrayptrs( py_invQt );
+    c_invQ = pyvector_to_Carrayptrs( py_invQ );
+    c_testing = pyvector_to_Carrayptrs( py_testing );
+    
+    predict(c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, N, NN, D, theta_size);
+    return Py_BuildValue ( "O", PyArray_Return ( py_testing ) );
+}
 
 
 
@@ -224,177 +145,4 @@ double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
 
 
 
-
-
-
-/* ---------------
-for testing
-------------------*/
-int cpu_dot(const float *x, const float *y, float *z){
-    int i;
-    z[0]=0;
-    for(i=0;i<N;i++){
-        z[0]=z[0]+x[i]*y[i];
-    }
-    return(0);
-}
-
-
-
-int check(cublasStatus_t status){
-    if (status != CUBLAS_STATUS_SUCCESS)
-    {
-    
-        fprintf(stderr, "!!!! kernel execution error.\n");
-        return EXIT_FAILURE;
-    
-    }
-    return(0);
-}
-
-static PyObject *comp()
-{
-
-
-	printf("hello world\n");
-    clock_t start,diff;
-    cublasHandle_t handle;
-    cublasStatus_t status;
-    float *x;
-    float *y;
-    float *z,*zg;
-    float *d_x, *d_y, *d_z;
-    int i;
-
-    status = cublasCreate(&handle);
-    x=(float *)malloc(N * sizeof(x[0]));
-    y=(float *)malloc(N * sizeof(y[0]));
-    z=(float *)malloc(sizeof(float));
-    zg=(float *)malloc(  sizeof(float));
-
-
-    cudaMalloc((void **)&d_x, N * sizeof(float));
-    cudaMalloc((void **)&d_y, N * sizeof(float));
-    cudaMalloc((void **)&d_z, sizeof(float));
-
-
-    /*initialise*/
-    for(i=0;i<N;i++){
-        x[i]=0.001;
-        y[i]=0.001;
-        zg[0]=-9999;
-        z[0]=-9999;
-    }
-
-    
-    /*CPU dot product*/
-    start=clock();
-    cpu_dot(x,y,z);
-    diff=clock()-start;
-    int msec = diff * 1000 / CLOCKS_PER_SEC;
-    printf("Time taken (CPU) %d seconds %d milliseconds\n", msec/1000, msec%1000);
-    
-
-
-    /*GPU dot product*/
-    start=clock();
-    cublasSetVector(N,sizeof(float),x,1,d_x,1);
-    cublasSetVector(N,sizeof(float),y,1,d_y,1);
-    check(cublasSdot(handle,N,d_x,1,d_y,1,zg));
-    diff=clock()-start;
-    msec = diff * 1000 / CLOCKS_PER_SEC;
-    printf("Time taken (GPU) %d seconds %d milliseconds\n", msec/1000, msec%1000);
-    
-
-    /*check result*/
-    printf("%f - %f\n",zg[0],z[0]);
-
-
-
-
-
-    free(x);
-    free(y);
-    free(z);
-    free(zg);
-    cudaFree(d_x);
-    cudaFree(d_y);
-    cudaFree(d_z);
-    return Py_BuildValue("i", 1);
-}
-
-
-// // static PyObject *rowx2(PyObject *self, PyObject *args)
-// // {
-// // 	printf("Hello world\n");
-// // }
-
-// /* #### Matrix Extensions ############################## */
-
-// /* ==== Row x 2 function - manipulate matrix in place ======================
-//     Multiply the 2nd row of the input by 2 and put in output
-//     interface:  rowx2(mat1, mat2)
-//                 mat1 and mat2 are NumPy matrices
-//                 Returns integer 1 if successful                        */
-// static PyObject *rowx2(PyObject *self, PyObject *args)
-// {
-// 	PyArrayObject *matin, *matout;  // The python objects to be extracted from the args
-// 	double **cin, **cout;           // The C matrices to be created to point to the 
-// 	                                //   python matrices, cin and cout point to the rows
-// 	                                //   of matin and matout, respectively
-// 	int i,j,n,m;
-	
-// 	/* Parse tuples separately since args will differ between C fcns */
-// 	if (!PyArg_ParseTuple(args, "O!O!", &PyArray_Type, &matin,
-// 		&PyArray_Type, &matout))  return NULL;
-// 	if (NULL == matin)  return NULL;
-// 	if (NULL == matout)  return NULL;
-	
-// 	/* Check that objects are 'double' type and matrices
-// 	     Not needed if python wrapper function checks before call to this routine */
-// 	if (not_doublematrix(matin)) return NULL;
-// 	if (not_doublematrix(matout)) return NULL;
-		
-// 	/* Change contiguous arrays into C ** arrays (Memory is Allocated!) */
-// 	cin=pymatrix_to_Carrayptrs(matin);
-// 	cout=pymatrix_to_Carrayptrs(matout);
-	
-// 	/* Get matrix dimensions. */
-// 	n=matin->dimensions[0];
-// 	m=matin->dimensions[1];
-	
-// 	/* Operate on the matrices  */
-// 	for ( i=0; i<n; i++)  {
-// 		for ( j=0; j<m; j++)  {
-// 			if (i==1) cout[i][j]=2.0*cin[i][j];
-// 	}  }
-		
-// 	/* Free memory, close file and return */
-// 	free_Carrayptrs(cin);
-// 	free_Carrayptrs(cout);
-// 	return Py_BuildValue("i", 1);
-// }
-
-
-
-
-
-// PyArrayObject *pymatrix(PyObject *objin)  {
-// 	return (PyArrayObject *) PyArray_ContiguousFromObject(objin,
-// 		NPY_DOUBLE, 2,2);
-// }
-
-
-
-// void free_Carrayptrs(double **v)  {
-// 	free((char*) v);
-// }
-
-// int  not_doublematrix(PyArrayObject *mat)  {
-// 	if (mat->descr->type_num != NPY_DOUBLE || mat->nd != 2)  {
-// 		PyErr_SetString(PyExc_ValueError,
-// 			"In not_doublematrix: array must be of type Float and 2 dimensional (n x m).");
-// 		return 1;  }
-// 	return 0;
-// }
 
diff --git a/gp_emulator/gpu/cdist.cu b/gp_emulator/gpu/cdist.cu
index 29f1ac1..92be19f 100644
--- a/gp_emulator/gpu/cdist.cu
+++ b/gp_emulator/gpu/cdist.cu
@@ -1,13 +1,13 @@
 //#include<cuda.h>
 #include<stdio.h>
 #include "gpu_predict.h"
-
+#define IDX2C(i,j,ld) (((j)*(ld))+(i))
 
  
 
 
-__global__ void gpu_cdist(float *res, float *a, float *b)
-{
+//__global__ void gpu_cdist(float *res, float *a, float *b)
+//{
 /*
     int i;
      int ix = threadIdx.x+blockDim.x*blockIdx.x;
@@ -23,6 +23,13 @@ __global__ void gpu_cdist(float *res, float *a, float *b)
      res[nx*ix+iy]=sqrt(res[nx*ix+iy]);
 */
 
-}
+//}
 
+__global__
+ void gpu_cdist(double *d_testing)
+{
+        int i = blockIdx.x * blockDim.x + threadIdx.x;
+        int j = blockIdx.y * blockDim.y + threadIdx.y;
+        d_testing[IDX2C(i,j,10)]=j;
+}
 
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
new file mode 100644
index 0000000..11da76b
--- /dev/null
+++ b/gp_emulator/gpu/predict.cu
@@ -0,0 +1,49 @@
+#include "gpu_predict.h"
+
+
+extern "C"{
+void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int  D, int theta_size)
+{
+    printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,NN,D,theta_size);   
+
+    cublasStatus_t stat;
+    cublasHandle_t handle;
+    stat=cublasCreate(&handle);
+
+    //define device vector and matrices
+    real *d_inputs, *d_theta_exp, *d_invQt, *d_invQ, *d_testing;
+    real *d_aa, *d_deriv;
+
+
+    //allocate and copy vector on device 
+    cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
+    cudaMalloc( (void **)&d_invQt, sizeof(real) * NN);
+    cublasCheckErrors(cublasSetVector( theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
+    cublasCheckErrors(cublasSetVector( NN, sizeof(real), c_invQt, 1, d_invQt, 1));
+
+
+    //allocate and copy matrix on device
+    cudaMalloc( (void **)&d_inputs, sizeof(real) * NN * D );
+    cudaMalloc( (void **)&d_invQ, sizeof(real) * NN * NN );
+    cudaMalloc( (void **)&d_testing, sizeof(real) * N * D );
+    cublasCheckErrors(cublasSetMatrix( NN, D, sizeof(real), c_inputs, NN, d_inputs, NN ));
+    cublasCheckErrors(cublasSetMatrix( NN, NN, sizeof(real), c_invQ, NN, d_invQ, NN ));
+    cublasCheckErrors(cublasSetMatrix( N, D, sizeof(real), c_testing, N, d_testing, N));
+    
+
+
+
+    
+    dim3 threads(10,20); 
+    gpu_cdist<<<1,threads>>>(d_testing);    
+
+    cublasCheckErrors(cublasGetMatrix (N, D, sizeof(real), d_testing, N, c_testing, N));
+   
+    cudaFree(d_theta_exp);
+    cudaFree(d_testing);
+    cudaFree(d_aa);
+    //free(c_theta);
+    //free((char*) c_inputs);
+    //return Py_BuildValue ("i",1);
+}
+}
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index f74bc67..b44a9c8 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -1,5 +1,6 @@
 import numpy as np
 import scipy.spatial.distance as dist
+import _gpu_predict
 class GP:
     def __init__ (self, P, N, M):
         self.D = P
@@ -8,7 +9,7 @@ def __init__ (self, P, N, M):
         self.invQ = np.random.random((M,M))
         self.invQt = np.random.random ((M))
 
-    @profile
+#    @profile
     def predict ( self, testing, do_unc=True ):
         ( nn, D ) = testing.shape
         assert D == self.D
@@ -35,12 +36,44 @@ def predict ( self, testing, do_unc=True ):
         else:
 	    return mu, deriv
 
+    def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
+        '''GPU predict function
+        '''
+        ( nn, D ) = testing.shape
+        assert D == self.D
+        expX=np.exp(self.theta)
+
+        N=testing.shape[0]
+        theta_size=self.theta.size 
+        
+        a =_gpu_predict.predict_wrap(
+            expX,
+            self.inputs,
+            self.invQt,
+            self.invQ,
+            testing,
+            N,nn,D,theta_size)
+        print 'testing\n',testing
+        print 'a\n',a
+
+
+        # for passing the test (temp)
+        mu=0
+        var=0
+        deriv=0
+        if do_unc:
+            return mu, var, deriv
+        else:
+            return mu, deriv
+
 
 if __name__ == '__main__':
-    N=4e5
-    M=250
+    N=20
+    M=250#250
     P=10
     testing=np.ones((N,P))
     gp=GP(P,N,M)
     gp.predict(testing)
+    gp.gpu_predict(testing)
+
     

From b5142b77fe766244ad2bb7fc3a563a3342cfc4d9 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 30 Apr 2015 14:32:38 +0100
Subject: [PATCH 004/117] add matrix vector element wise multiplication

---
 gp_emulator/GaussianProcess.py | 24 ++++++++++++++++-----
 gp_emulator/gpu/Makefile       | 14 ++++++------
 gp_emulator/gpu/cdist.cu       |  8 +++----
 gp_emulator/gpu/predict.cu     | 39 ++++++++++++++++++++++++----------
 gp_emulator/gpu/test.py        | 14 ++++++------
 gp_emulator/multivariate_gp.py |  8 +++++--
 tests/test_perband_emulator.py | 37 +++++++++++++++++---------------
 7 files changed, 93 insertions(+), 51 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index 36a3f3f..4a6c95c 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -278,16 +278,28 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         '''
         ( nn, D ) = testing.shape
         assert D == self.D
-        print self.inputs.shape
+        expX=np.exp(self.theta)
+        
         a =_gpu_predict.predict(self.D,
-            self.theta,
+            expX,
             self.inputs,
             self.invQt,
             self.invQ,
             testing)
-        print a
-        #print b
-        #print self.theta
+        print(testing)
+        print(a)
+
+        
+        # for passing the test (temp)
+        mu=0
+        var=0
+        deriv=0
+        if do_unc:
+            return mu, var, deriv
+        else:
+            return mu, deriv
+
+
         
 
 
@@ -343,3 +355,5 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
     ######plt.plot ( input_obs, target1, 'gs' )
     ######plt.title("theta: %s" % theta )
     ######plt.show()
+
+
diff --git a/gp_emulator/gpu/Makefile b/gp_emulator/gpu/Makefile
index d187e27..096c885 100644
--- a/gp_emulator/gpu/Makefile
+++ b/gp_emulator/gpu/Makefile
@@ -1,5 +1,5 @@
-SRCS= _gpu_predict.c cdist.cu
-OBJS = _gpu_predict.o cdist.o
+SRCS= _gpu_predict.c predict.cu cdist.cu
+OBJS = _gpu_predict.o cdist.o predict.o
 SHARE = _gpu_predict.so #$(SRCS:.c=.so) $(CUDASRCS:.cu=.so)
 GPUCC = nvcc
 CC = gcc
@@ -11,17 +11,19 @@ NVLIB = -L/Developer/NVIDIA/CUDA-7.0/lib/
 
 
 
-all: $(SHARE)
+all: $(SHARE) $(OBJS)
 
 
 %.so:$(OBJS)
-	$(CC) -bundle -flat_namespace -undefined suppress -o $(BUILDPATH)$@ $^ $(INC) $(NVLIB) $(LIB)
+	$(CC) -shared -fpic -flat_namespace -undefined suppress -o $(BUILDPATH)$@ $^ $(INC) $(NVLIB) $(LIB)
 
 %.o:%.c
-	$(GPUCC)  -c $^  -o $@  $(INC) $(LIB) $(NVLIB)
+	$(GPUCC)  -c $^  -o $@  $(INC) $(LIB) $(NVLIB)  --compiler-options '-fPIC' 
 
 %.o:%.cu
-	$(GPUCC) -c $^ -o $@ $(INC) $(NVLIB) $(LIB)
+	$(GPUCC)  -c $^ -o $@ $(INC) $(NVLIB) $(LIB)  --compiler-options '-fPIC' 
 
 clean:
 	rm *.o $(BUILDPATH)*.so
+
+#
diff --git a/gp_emulator/gpu/cdist.cu b/gp_emulator/gpu/cdist.cu
index 92be19f..235b138 100644
--- a/gp_emulator/gpu/cdist.cu
+++ b/gp_emulator/gpu/cdist.cu
@@ -24,12 +24,12 @@
 */
 
 //}
-
+/*
 __global__
- void gpu_cdist(double *d_testing)
+ void gpu_cdist(double )
 {
         int i = blockIdx.x * blockDim.x + threadIdx.x;
         int j = blockIdx.y * blockDim.y + threadIdx.y;
-        d_testing[IDX2C(i,j,10)]=j;
+        d_testing[IDX2C(i,j,blockDim.x)]=i;
 }
-
+*/
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 11da76b..7146702 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -1,10 +1,26 @@
 #include "gpu_predict.h"
+#define IDX2C(i,j,ld) (((j)*(ld))+(i))
+
+/*********************************************//** 
+ * vector matrix elementwise multiplication
+ *********************************************/
+__global__ 
+void gpu_vectorTimesMatrix(real *A, const real * v,/* real *res,*/ int A_ld, int v_len)
+{
+    int ix, iy;
+   // ix = blockIdx.x * blockDim.x + threadIdx.x;
+   // iy = blockIdx.y * blockDim.y + threadIdx.y;
+    ix = threadIdx.x;
+    iy = blockIdx.x;
+
+    A[IDX2C(ix, iy, A_ld)] = A[IDX2C(ix, iy, A_ld)] * v[ix];
+}
 
 
 extern "C"{
-void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int  D, int theta_size)
+void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int M, int  D, int theta_size)
 {
-    printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,NN,D,theta_size);   
+    printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,M,D,theta_size);   
 
     cublasStatus_t stat;
     cublasHandle_t handle;
@@ -17,25 +33,26 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
 
     //allocate and copy vector on device 
     cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
-    cudaMalloc( (void **)&d_invQt, sizeof(real) * NN);
+    cudaMalloc( (void **)&d_invQt, sizeof(real) * M);
     cublasCheckErrors(cublasSetVector( theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
-    cublasCheckErrors(cublasSetVector( NN, sizeof(real), c_invQt, 1, d_invQt, 1));
+    cublasCheckErrors(cublasSetVector( M, sizeof(real), c_invQt, 1, d_invQt, 1));
 
 
     //allocate and copy matrix on device
-    cudaMalloc( (void **)&d_inputs, sizeof(real) * NN * D );
-    cudaMalloc( (void **)&d_invQ, sizeof(real) * NN * NN );
+    cudaMalloc( (void **)&d_inputs, sizeof(real) * M * D );
+    cudaMalloc( (void **)&d_invQ, sizeof(real) * M * M );
     cudaMalloc( (void **)&d_testing, sizeof(real) * N * D );
-    cublasCheckErrors(cublasSetMatrix( NN, D, sizeof(real), c_inputs, NN, d_inputs, NN ));
-    cublasCheckErrors(cublasSetMatrix( NN, NN, sizeof(real), c_invQ, NN, d_invQ, NN ));
+    cublasCheckErrors(cublasSetMatrix( M, D, sizeof(real), c_inputs, M, d_inputs, M ));
+    cublasCheckErrors(cublasSetMatrix( M, M, sizeof(real), c_invQ, M, d_invQ, M ));
     cublasCheckErrors(cublasSetMatrix( N, D, sizeof(real), c_testing, N, d_testing, N));
     
 
 
+        
+    dim3 threads(D, N);
+    gpu_vectorTimesMatrix<<<N, D>>>(d_testing, d_theta_exp, D, D);
 
-    
-    dim3 threads(10,20); 
-    gpu_cdist<<<1,threads>>>(d_testing);    
+    //gpu_cdist<<<1,threads>>>(d_testing,N,);    
 
     cublasCheckErrors(cublasGetMatrix (N, D, sizeof(real), d_testing, N, c_testing, N));
    
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index b44a9c8..8af29c3 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -15,7 +15,7 @@ def predict ( self, testing, do_unc=True ):
         assert D == self.D
 
         expX = np.exp ( self.theta )
-        
+              
         a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
         a = expX[self.D]*np.exp(-0.5*a)
         b = expX[self.D]
@@ -44,16 +44,18 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         expX=np.exp(self.theta)
 
         N=testing.shape[0]
+        M=self.inputs.shape[0]
         theta_size=self.theta.size 
-        
+        print 'expX\n', expX[:(self.D)]*testing
+
         a =_gpu_predict.predict_wrap(
             expX,
             self.inputs,
             self.invQt,
             self.invQ,
             testing,
-            N,nn,D,theta_size)
-        print 'testing\n',testing
+            N,M,D,theta_size)
+        #print 'testing\n',testing
         print 'a\n',a
 
 
@@ -68,12 +70,12 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=20
+    N=2e5
     M=250#250
     P=10
     testing=np.ones((N,P))
     gp=GP(P,N,M)
-    gp.predict(testing)
+    #gp.predict(testing)
     gp.gpu_predict(testing)
 
     
diff --git a/gp_emulator/multivariate_gp.py b/gp_emulator/multivariate_gp.py
index fb3203b..01c40fb 100644
--- a/gp_emulator/multivariate_gp.py
+++ b/gp_emulator/multivariate_gp.py
@@ -192,7 +192,7 @@ def compress ( self, X ):
         """Project full-rank vector into PC basis"""
         return X.dot ( self.basis_functions.T ).T
 
-    def predict ( self, y, do_deriv=True ):
+    def predict ( self, y, do_deriv=True ,is_gpu=False):
         """Prediction of input vector
         
         The individual GPs predict the PC weights, and these are used to 
@@ -212,7 +212,11 @@ def predict ( self, y, do_deriv=True ):
         if do_deriv:
             deriv = np.zeros ( ( y.shape[1], self.basis_functions.shape[1] ) )
         for i in xrange ( self.n_pcs ):
-            pred_mu, pred_var, grad = self.emulators[i].predict ( y )
+            if is_gpu == False:
+                pred_mu, pred_var, grad = self.emulators[i].predict ( y )
+            if is_gpu == True:
+                pred_mu, pred_var, grad = self.emulators[i].gpu_predict ( y )
+
             fwd += pred_mu * self.basis_functions[i]
             if do_deriv:
                 deriv += np.matrix(grad).T * np.matrix(self.basis_functions[i])
diff --git a/tests/test_perband_emulator.py b/tests/test_perband_emulator.py
index 5baeed4..e838dad 100644
--- a/tests/test_perband_emulator.py
+++ b/tests/test_perband_emulator.py
@@ -10,10 +10,13 @@
 
 
 wv = np.arange (400, 2501)
-for isample in [0, 10, 20, 50]:
-    plt.plot ( wv, d[isample, 10:], '-', lw=2)
-    r = gp.predict(d[isample,:10])[0]
-    plt.plot ( wv, d[isample, 10:], '--', lw=2)
+#for isample in [0, 10, 20, 50]:
+#    plt.plot ( wv, d[isample, 10:], '-', lw=2)
+#    r = gp.predict(d[isample,:10])[0]
+#    plt.plot ( wv, d[isample, 10:], '--', lw=2)
+
+
+r = gp.predict(d[0,:10],is_gpu=True)[0]
 
 
 
@@ -25,9 +28,6 @@ def perband_emulators ( emulators, band_pass ):
     x_train_pband = [ emulators.X_train[:,band_pass[i,:]].mean(axis=1) \
         for i in xrange( n_bands ) ]
     x_train_pband = np.array ( x_train_pband )
-    print x_train_pband.shape
-    print emulators.y_train.shape
-    print n_bands
     emus = []
     for i in xrange( n_bands ):
         gp = GaussianProcess ( emulators.y_train[:]*1, \
@@ -38,17 +38,20 @@ def perband_emulators ( emulators, band_pass ):
 
 
 
-new_output = d[:,10:][:,452:486].mean(axis=1)
-band_pass = np.zeros( (1,2101), dtype=np.bool)
-band_pass[:, 442:476] = 1
-gp_single = perband_emulators ( gp, band_pass )
+#new_output = d[:,10:][:,452:486].mean(axis=1)
+#band_pass = np.zeros( (1,2101), dtype=np.bool)
+#band_pass[:, 442:476] = 1
+#gp_single = perband_emulators ( gp, band_pass )
+
 
+#print isinstance ( gp_single[0], GaussianProcess)
+#X = d[:, :10]
+#r = gp_single[0].predict ( X )[0]
 
-print isinstance ( gp_single[0], GaussianProcess)
-X = d[:, :10]
-r = gp_single[0].predict ( X )[0]
-plt.plot ( new_output, r,'o')
-plt.plot([0, 0.6], [0, 0.6], 'k--')
+#gp_single[0].gpu_predict ( X )
+#print 'predict finish'
+#plt.plot ( new_output, r,'o')
+#plt.plot([0, 0.6], [0, 0.6], 'k--')
 
 
-#%timeit r = gp_single[0].predict ( X )[0]
\ No newline at end of file
+#%timeit r = gp_single[0].predict ( X )[0]

From 2d80a3e0bf4f23d402040b88f1006b58634c0f96 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 30 Apr 2015 14:48:44 +0100
Subject: [PATCH 005/117] update header file

---
 gp_emulator/gpu/gpu_predict.h | 43 +++++++++++++++++++++++++++--------
 1 file changed, 34 insertions(+), 9 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index ac147e5..6567ad2 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -2,17 +2,17 @@
 
 #include "Python.h"
 #include "arrayobject.h"
-//#include "C_arraytest.h"
 #include <math.h>
 #include <stdio.h>
-// #include <stdlib.h>
-// #include <string.h>
-// #include <time.h>
+#include <stdlib.h>
+ #include <string.h>
+ #include <time.h>
 /* Includes, cuda */
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
 
+#define real double
 
 /*
 * he
@@ -21,18 +21,43 @@
 //#define GPU_PREDICT_H
 
 
-void hi();
 //void hello();
 
 
-static PyObject *comp();
 
-static PyObject *predict(PyObject *self, PyObject *args);
 
 PyArrayObject *pyvector(PyObject *objin);
 double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
 double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 double **ptrvector(long n);
 
-//#endif
-
+PyObject *predict_wrap ( PyObject *self, PyObject *args );
+//void predict(real *c_theta_exp, real **c_inputs,real *c_invQt,real **c_invQ, real **c_testing, int N, int NN, int D, int theta_size);
+//extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int D, int theta_size);
+__global__ void gpu_cdist(double *d_testing, int M, int N, int P);
+
+// error check macros
+#define cudaCheckErrors(msg) \
+    do { \
+        cudaError_t __err = cudaGetLastError(); \
+        if (__err != cudaSuccess) { \
+            fprintf(stderr, "Fatal error: %s (%s at %s:%d)\n", \
+                msg, cudaGetErrorString(__err), \
+                __FILE__, __LINE__); \
+            fprintf(stderr, "*** FAILED - ABORTING\n"); \
+            exit(1); \
+        } \
+    } while (0)
+
+// for CUBLAS V2 API
+#define cublasCheckErrors(fn) \
+    do { \
+        cublasStatus_t __err = fn; \
+        if (__err != CUBLAS_STATUS_SUCCESS) { \
+            fprintf(stderr, "Fatal cublas error: %d (at %s:%d)\n", \
+                (int)(__err), \
+                __FILE__, __LINE__); \
+            fprintf(stderr, "*** FAILED - ABORTING\n"); \
+            exit(1); \
+        } \
+    } while (0)

From 71c50807de733a1c5db9c7113d2abad4e875c23a Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 30 Apr 2015 19:44:37 +0100
Subject: [PATCH 006/117] gpu_cdist() ready, but not verified

---
 gp_emulator/gpu/predict.cu | 71 +++++++++++++++++++++++++++++++-------
 gp_emulator/gpu/test.py    |  4 +--
 2 files changed, 60 insertions(+), 15 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 7146702..beb823d 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -5,15 +5,35 @@
  * vector matrix elementwise multiplication
  *********************************************/
 __global__ 
-void gpu_vectorTimesMatrix(real *A, const real * v,/* real *res,*/ int A_ld, int v_len)
+void gpu_vectorTimesMatrixSqrt(const real *A, const real * v, real *res, int A_ld)
 {
     int ix, iy;
-   // ix = blockIdx.x * blockDim.x + threadIdx.x;
-   // iy = blockIdx.y * blockDim.y + threadIdx.y;
-    ix = threadIdx.x;
-    iy = blockIdx.x;
+    ix = blockIdx.x * blockDim.x + threadIdx.x;
+    iy = blockIdx.y * blockDim.y + threadIdx.y;
+//    ix=threadIdx.x;
+//    iy=blockIdx.y;
+    res[IDX2C(ix, iy, A_ld)] = sqrt(A[IDX2C(ix, iy, A_ld)] * v[ix]);
+}
+
+
+__global__
+void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld)
+{
+    int ix, iy, iz;
+    ix = blockIdx.x * blockDim.x + threadIdx.x;
+    iy = blockIdx.y * blockDim.y + threadIdx.y;
+    iz = blockIdx.z * blockDim.z + threadIdx.z;
+
 
-    A[IDX2C(ix, iy, A_ld)] = A[IDX2C(ix, iy, A_ld)] * v[ix];
+    output[IDX2C(ix, iz, In1_ld)]+=pow(input1[IDX2C(iy, iz, In1_ld)] - input2[IDX2C(ix, iz, In2_ld)],2);
+}
+
+
+__global__
+void gpu_init_zero(real *vec)
+{
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    vec[ix] = 0;
 }
 
 
@@ -24,12 +44,12 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
 
     cublasStatus_t stat;
     cublasHandle_t handle;
+    
     stat=cublasCreate(&handle);
 
     //define device vector and matrices
     real *d_inputs, *d_theta_exp, *d_invQt, *d_invQ, *d_testing;
-    real *d_aa, *d_deriv;
-
+    real *d_a;//, *d_deriv;
 
     //allocate and copy vector on device 
     cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
@@ -46,19 +66,44 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     cublasCheckErrors(cublasSetMatrix( M, M, sizeof(real), c_invQ, M, d_invQ, M ));
     cublasCheckErrors(cublasSetMatrix( N, D, sizeof(real), c_testing, N, d_testing, N));
     
-
+    //allocate memory to results matrices
+    cudaMalloc((void **)&d_a, sizeof(real) * M * N);
 
         
-    dim3 threads(D, N);
-    gpu_vectorTimesMatrix<<<N, D>>>(d_testing, d_theta_exp, D, D);
+    /*********************
+     *cdist
+     *********************/
+    real *d_res_temp1, *d_res_temp2;
+    cudaMalloc((void **)&d_res_temp1, sizeof(real) * M * D);
+    cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
+    
+
+    dim3 nthread(5,D);
+    dim3 nblock(M/5,1);
+    gpu_vectorTimesMatrixSqrt<<<M, D>>>(d_inputs, d_theta_exp, d_res_temp1, D);
+    nthread.x=D; nthread.y=50;
+    nblock.x=1; nblock.y=N/50;
+    gpu_vectorTimesMatrixSqrt<<<nblock, nthread>>>(d_testing, d_theta_exp, d_res_temp2, D);
+    
+    
+
+    nthread.x=1;   nthread.y=50;    nthread.z=D;
+    nblock.x=N;    nblock.y=M/50;     nblock.z=1;
+
+    
+    gpu_init_zero<<<N*M/512,512>>>(d_a);
+    gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, D, D);
+    
+    cudaFree(d_res_temp1);
+    cudaFree(d_res_temp2);
 
     //gpu_cdist<<<1,threads>>>(d_testing,N,);    
 
-    cublasCheckErrors(cublasGetMatrix (N, D, sizeof(real), d_testing, N, c_testing, N));
+    cublasCheckErrors(cublasGetMatrix (N, D, sizeof(real), d_res_temp2, N, c_testing, N));
    
     cudaFree(d_theta_exp);
     cudaFree(d_testing);
-    cudaFree(d_aa);
+    //cudaFree(d_aa);
     //free(c_theta);
     //free((char*) c_inputs);
     //return Py_BuildValue ("i",1);
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index 8af29c3..fec9ae5 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -46,7 +46,7 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         N=testing.shape[0]
         M=self.inputs.shape[0]
         theta_size=self.theta.size 
-        print 'expX\n', expX[:(self.D)]*testing
+        print 'expX\n', np.sqrt(expX[:(self.D)]*testing)
 
         a =_gpu_predict.predict_wrap(
             expX,
@@ -70,7 +70,7 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=2e5
+    N=1e6
     M=250#250
     P=10
     testing=np.ones((N,P))

From ac83b4e43f5700eef1231cc61917e3f0d1244541 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 4 May 2015 18:52:34 +0100
Subject: [PATCH 007/117] computeTranspose; matrix vector multiplication; some
 tests for debug; cdist with 3 dimension doesn't work

---
 gp_emulator/gpu/Makefile           |   4 +-
 gp_emulator/gpu/_gpu_predict.c     |  30 +++++-
 gp_emulator/gpu/computeTranspose.c |  20 ++++
 gp_emulator/gpu/gpu_predict.h      |   3 +-
 gp_emulator/gpu/predict.cu         | 156 +++++++++++++++++++++++------
 gp_emulator/gpu/test.py            |  15 ++-
 6 files changed, 188 insertions(+), 40 deletions(-)
 create mode 100644 gp_emulator/gpu/computeTranspose.c

diff --git a/gp_emulator/gpu/Makefile b/gp_emulator/gpu/Makefile
index 096c885..e69966d 100644
--- a/gp_emulator/gpu/Makefile
+++ b/gp_emulator/gpu/Makefile
@@ -1,5 +1,5 @@
-SRCS= _gpu_predict.c predict.cu cdist.cu
-OBJS = _gpu_predict.o cdist.o predict.o
+SRCS= _gpu_predict.c predict.cu cdist.cu computeTranspose.c
+OBJS = _gpu_predict.o cdist.o predict.o computeTranspose.o
 SHARE = _gpu_predict.so #$(SRCS:.c=.so) $(CUDASRCS:.cu=.so)
 GPUCC = nvcc
 CC = gcc
diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 1294194..15253c7 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -29,6 +29,7 @@ void init_gpu_predict()  {
 
 
 
+
 /* #### Vector Utility functions ######################### */
 
 /* ==== Make a Python Array Obj. from a PyObject, ================
@@ -84,7 +85,7 @@ double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
     m=arrayin->dimensions[1];
     c=ptrvector(n);
     a=(double *) arrayin->data;  /* pointer to arrayin data as double */
-    for ( i=0; i<n; i++)  {
+    for ( i=0; i < n; i++)  {
         c[i]=a+i*m;  }
     return c;
 }
@@ -95,9 +96,10 @@ double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
 
 PyObject *predict_wrap ( PyObject *self, PyObject *args )
 {
-    int N,NN,D,theta_size;
+    int N,M,D,theta_size;
     
     PyArrayObject *py_theta_exp, *py_inputs, *py_invQt, *py_invQ, *py_testing;
+    PyArrayObject * temp;
     real *c_theta_exp;
     real *c_inputs, *c_invQt, *c_invQ, *c_testing;
 
@@ -108,15 +110,33 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
         &PyArray_Type, &py_invQt,
         &PyArray_Type, &py_invQ,
         &PyArray_Type, &py_testing,
-        &N,&NN,&D,&theta_size);
+        &N, &M, &D,&theta_size);
+
+
 
-    c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp );
+
+    c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
     c_inputs = pyvector_to_Carrayptrs( py_inputs );
     c_invQt = pyvector_to_Carrayptrs( py_invQt );
     c_invQ = pyvector_to_Carrayptrs( py_invQ );
     c_testing = pyvector_to_Carrayptrs( py_testing );
+
+
+    //transpose 2D array to column major to cope with cublas
+    computeTranspose( c_invQ, M, M);
+    computeTranspose( c_inputs, D, M);
+    computeTranspose( c_testing, D, N);
+
+    predict(c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, N, M, D, theta_size);
+    
+    //transpose back to row major
+    computeTranspose( c_testing, N, D);
+    computeTranspose( c_invQ, M, D);
+    computeTranspose( c_inputs, D, M);
+
     
-    predict(c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, N, NN, D, theta_size);
+
+
     return Py_BuildValue ( "O", PyArray_Return ( py_testing ) );
 }
 
diff --git a/gp_emulator/gpu/computeTranspose.c b/gp_emulator/gpu/computeTranspose.c
new file mode 100644
index 0000000..06108eb
--- /dev/null
+++ b/gp_emulator/gpu/computeTranspose.c
@@ -0,0 +1,20 @@
+#include "gpu_predict.h"
+//size_in is the leading dimension of input matrix
+//size_out is the leading dimension of the output matrix
+void computeTranspose(real *matrix, const  int size_in, const  int size_out)
+{
+    real * temp;
+    temp = ( real *)malloc(sizeof(real) * size_in * size_out);
+
+    for ( int i = 0; i < size_in * size_out; ++i)
+        temp[i] = matrix[i];
+
+    for (int y = 0; y < size_out; ++y)
+    {
+        for (int x = 0; x < size_in; ++x)
+        {
+            matrix[(x * size_out) + y] = temp[(y * size_in) + x];                                                                
+
+        }   
+    }
+}
\ No newline at end of file
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 6567ad2..8bec243 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -30,12 +30,13 @@ PyArrayObject *pyvector(PyObject *objin);
 double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
 double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 double **ptrvector(long n);
-
+void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
 //void predict(real *c_theta_exp, real **c_inputs,real *c_invQt,real **c_invQ, real **c_testing, int N, int NN, int D, int theta_size);
 //extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int D, int theta_size);
 __global__ void gpu_cdist(double *d_testing, int M, int N, int P);
 
+
 // error check macros
 #define cudaCheckErrors(msg) \
     do { \
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index beb823d..31fd4cf 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -1,31 +1,36 @@
 #include "gpu_predict.h"
-#define IDX2C(i,j,ld) (((j)*(ld))+(i))
-
+#include <stdlib.h>
+//#define IDX2C(i,j,ld) (((j)*(ld))+(i))
+#define IDX2D(i,j,ld) (((j)*(ld))+(i))  //!! keep it in column major for coping with cublas column major fashion.
+#define debug 
+// x -> i -> col
+// y -> j -> row
+// leading dimension should always be column
 /*********************************************//** 
  * vector matrix elementwise multiplication
  *********************************************/
 __global__ 
-void gpu_vectorTimesMatrixSqrt(const real *A, const real * v, real *res, int A_ld)
+void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld)
 {
     int ix, iy;
     ix = blockIdx.x * blockDim.x + threadIdx.x;
     iy = blockIdx.y * blockDim.y + threadIdx.y;
-//    ix=threadIdx.x;
-//    iy=blockIdx.y;
-    res[IDX2C(ix, iy, A_ld)] = sqrt(A[IDX2C(ix, iy, A_ld)] * v[ix]);
+    res[IDX2D(ix, iy, A_ld)] = A[IDX2D(ix, iy, A_ld)] * v[iy];
 }
 
 
+// ix -> M; iy -> N; iz -> D
+
 __global__
-void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld)
+void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld)
 {
     int ix, iy, iz;
     ix = blockIdx.x * blockDim.x + threadIdx.x;
     iy = blockIdx.y * blockDim.y + threadIdx.y;
     iz = blockIdx.z * blockDim.z + threadIdx.z;
 
+    output[IDX2D(ix, iy, Out_ld)] += pow(input1[IDX2D(ix, iz, In1_ld)] - input2[IDX2D(iy, iz, In2_ld)],2);
 
-    output[IDX2C(ix, iz, In1_ld)]+=pow(input1[IDX2C(iy, iz, In1_ld)] - input2[IDX2C(ix, iz, In2_ld)],2);
 }
 
 
@@ -37,27 +42,57 @@ void gpu_init_zero(real *vec)
 }
 
 
+void computeTranspose(real *matrix, const  int size_in, const  int size_out)
+{
+    real * temp;
+    temp = ( real *)malloc(sizeof(real) * size_in * size_out);
+
+    for ( int i = 0; i < size_in * size_out; ++i)
+        temp[i] = matrix[i];
+
+    for (int y = 0; y < size_out; ++y)
+    {
+        for (int x = 0; x < size_in; ++x)
+        {
+            matrix[(x * size_out) + y] = temp[(y * size_in) + x];                                                                
+
+        }   
+    }
+}
+
+
+
 extern "C"{
-void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int M, int  D, int theta_size)
+void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing,  int N, int M, int  D, int theta_size)
 {
     printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,M,D,theta_size);   
-
+    int i;
     cublasStatus_t stat;
     cublasHandle_t handle;
     
     stat=cublasCreate(&handle);
 
+    real *c_theta_exp_sqrt;
+    c_theta_exp_sqrt = (real *)malloc( sizeof(real) * theta_size );
+    for( i=0; i < theta_size; i++ )
+    {
+        c_theta_exp_sqrt[i] = sqrt( c_theta_exp[i] );
+    }
+
+
     //define device vector and matrices
-    real *d_inputs, *d_theta_exp, *d_invQt, *d_invQ, *d_testing;
+    real *d_inputs, *d_theta_exp, *d_theta_exp_sqrt, *d_invQt, *d_invQ, *d_testing;
     real *d_a;//, *d_deriv;
 
+
     //allocate and copy vector on device 
     cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
+    cudaMalloc( (void **)&d_theta_exp_sqrt, sizeof(real) * theta_size );
     cudaMalloc( (void **)&d_invQt, sizeof(real) * M);
+    cublasCheckErrors(cublasSetVector( theta_size, sizeof(real), c_theta_exp_sqrt, 1, d_theta_exp_sqrt, 1 ));
     cublasCheckErrors(cublasSetVector( theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
     cublasCheckErrors(cublasSetVector( M, sizeof(real), c_invQt, 1, d_invQt, 1));
 
-
     //allocate and copy matrix on device
     cudaMalloc( (void **)&d_inputs, sizeof(real) * M * D );
     cudaMalloc( (void **)&d_invQ, sizeof(real) * M * M );
@@ -78,31 +113,96 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
     
 
-    dim3 nthread(5,D);
-    dim3 nblock(M/5,1);
-    gpu_vectorTimesMatrixSqrt<<<M, D>>>(d_inputs, d_theta_exp, d_res_temp1, D);
-    nthread.x=D; nthread.y=50;
-    nblock.x=1; nblock.y=N/50;
-    gpu_vectorTimesMatrixSqrt<<<nblock, nthread>>>(d_testing, d_theta_exp, d_res_temp2, D);
-    
+
+    dim3 nthread(1,D);
+    dim3 nblock(M,1);
+    gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_inputs, d_theta_exp_sqrt, d_res_temp1, M);
+    nthread.x=1; nthread.y=D;
+    nblock.x=N; nblock.y=1;
+    gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
     
 
-    nthread.x=1;   nthread.y=50;    nthread.z=D;
-    nblock.x=N;    nblock.y=M/50;     nblock.z=1;
+// check result of gpu_vectorTimesMatrix
+//#undef debug
+#ifdef debug
+    real *debug_res_temp1, *debug_res_temp2;
+    debug_res_temp1 = (real *)malloc(sizeof(real) * M * D);
+    debug_res_temp2 = (real *)malloc(sizeof(real) * N * D);
+    cublasCheckErrors(cublasGetMatrix(M, D, sizeof(real), d_res_temp1, M, debug_res_temp1, M));  
+    cublasCheckErrors(cublasGetMatrix(N, D, sizeof(real), d_res_temp2, N, debug_res_temp2, N));
+    computeTranspose(debug_res_temp1, M, D);
+    computeTranspose(debug_res_temp2, N, D);
+    printf("res_temp1\n");
+    for( i = 0 ; i < M * D; i ++)
+    {
+        printf( "%.4f|", debug_res_temp1[i] );
+        if( i % 15 == 0 )
+            printf( "\n" );
+    }
+    printf("\nres_temp2\n");
+    for( i = 0; i < N * D; i ++)
+    {
+        printf( "%.4f|", debug_res_temp2[i] );
+        if( i % 15 == 0 )
+            printf( "\n" );
+    }
+    printf("\n");
+    free(debug_res_temp1);
+    free(debug_res_temp2);
+#endif
+
+
+    gpu_init_zero<<<ceil(float(N)*float(M)/512),512>>>(d_a);
+//#undef debug
+#ifdef debug
+    real * debug_zero;
+    debug_zero = (real *)malloc(sizeof(real) * N * M);
+    cublasCheckErrors(cublasGetMatrix(N, M, sizeof(real), d_a, N, debug_zero, N));  
+    for( i = 0; i < M * N; i ++)
+    {
+        if( debug_zero[i] != 0 )
+        {
+            printf( "[ERROR!] : gpu_init_zero.\n" );
+            exit ( EXIT_FAILURE );            
+        }
+
+    }
+    free( debug_zero );
+
+#endif
+
+    nthread.x=1;   nthread.y=1;    nthread.z=D;
+    nblock.x=M;/*M*/    nblock.y=N;     nblock.z=1;
+
+    gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, M);
+
+#ifdef debug 
+    real *temp_c_a;
+    temp_c_a = (real *)malloc( sizeof(real) * N * M);
+    cudaMemcpy(temp_c_a, d_a, sizeof(real) * N * M, cudaMemcpyDeviceToHost);
+    computeTranspose( temp_c_a, N, M );
+    for( i = 0; i < M * D; i++ )
+    {
+        if(i%15==0)
+            printf("\n");
+        printf("%.4f|", temp_c_a[i]);
+    }
+    free( temp_c_a );
+#endif
 
     
-    gpu_init_zero<<<N*M/512,512>>>(d_a);
-    gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, D, D);
-    
-    cudaFree(d_res_temp1);
-    cudaFree(d_res_temp2);
+   // cudaFree(d_res_temp1);
+   // cudaFree(d_res_temp2);
 
     //gpu_cdist<<<1,threads>>>(d_testing,N,);    
-
-    cublasCheckErrors(cublasGetMatrix (N, D, sizeof(real), d_res_temp2, N, c_testing, N));
    
+
+       
+    
+//    free(temp_c_a);
     cudaFree(d_theta_exp);
     cudaFree(d_testing);
+
     //cudaFree(d_aa);
     //free(c_theta);
     //free((char*) c_inputs);
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index fec9ae5..d003bb6 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -46,7 +46,14 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         N=testing.shape[0]
         M=self.inputs.shape[0]
         theta_size=self.theta.size 
-        print 'expX\n', np.sqrt(expX[:(self.D)]*testing)
+        global test_var_1 ,test_var_2, test_var_3
+        test_var_1= np.sqrt(expX[:(self.D)])*self.inputs
+        test_var_2 = np.sqrt(expX[:(self.D)])*testing
+        test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+
+        print 'sqrt(expX[:(self.D)])*self.inputs=\n', test_var_1
+        print 'sqrt(expX)*testing\n', test_var_2
+        print 'cdist =\n', test_var_3
 
         a =_gpu_predict.predict_wrap(
             expX,
@@ -56,7 +63,7 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
             testing,
             N,M,D,theta_size)
         #print 'testing\n',testing
-        print 'a\n',a
+        #print 'a\n',a
 
 
         # for passing the test (temp)
@@ -70,10 +77,10 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=1e6
+    N=100
     M=250#250
     P=10
-    testing=np.ones((N,P))
+    testing=np.random.random((N,P))
     gp=GP(P,N,M)
     #gp.predict(testing)
     gp.gpu_predict(testing)

From fdac4a5c8a31650cbb7244ca32f4fda59cc2c185 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 4 May 2015 20:56:44 +0100
Subject: [PATCH 008/117] cdist 3D

---
 gp_emulator/gpu/_gpu_predict.c | 14 +++---
 gp_emulator/gpu/predict.cu     | 81 +++++++++++++++++++---------------
 2 files changed, 52 insertions(+), 43 deletions(-)

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 15253c7..19caa82 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -123,16 +123,16 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
 
 
     //transpose 2D array to column major to cope with cublas
-    computeTranspose( c_invQ, M, M);
-    computeTranspose( c_inputs, D, M);
-    computeTranspose( c_testing, D, N);
+    computeTranspose( c_invQ, M, M );
+    computeTranspose( c_inputs, D, M );
+    computeTranspose( c_testing, D, N );
 
-    predict(c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, N, M, D, theta_size);
+    predict( c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, N, M, D, theta_size );
     
     //transpose back to row major
-    computeTranspose( c_testing, N, D);
-    computeTranspose( c_invQ, M, D);
-    computeTranspose( c_inputs, D, M);
+    computeTranspose( c_testing, N, D );
+    computeTranspose( c_invQ, M, D );
+    computeTranspose( c_inputs, D, M );
 
     
 
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 31fd4cf..43c4bf1 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -22,14 +22,16 @@ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld)
 // ix -> M; iy -> N; iz -> D
 
 __global__
-void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld)
+void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D)
 {
     int ix, iy, iz;
-    ix = blockIdx.x * blockDim.x + threadIdx.x;
-    iy = blockIdx.y * blockDim.y + threadIdx.y;
+    ix = blockIdx.x * blockDim.x + threadIdx.x;//N
+    iy = blockIdx.y * blockDim.y + threadIdx.y;//M
     iz = blockIdx.z * blockDim.z + threadIdx.z;
-
-    output[IDX2D(ix, iy, Out_ld)] += pow(input1[IDX2D(ix, iz, In1_ld)] - input2[IDX2D(iy, iz, In2_ld)],2);
+    //for (iz = 0; iz < D; iz++)
+    //{
+        output[IDX2D(ix, iy, Out_ld)] += pow(input1[IDX2D(iy, iz, In1_ld)] - input2[IDX2D(ix, iz, In2_ld)],2);
+    //}
 
 }
 
@@ -123,32 +125,9 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     
 
 // check result of gpu_vectorTimesMatrix
-//#undef debug
+#undef debug
 #ifdef debug
-    real *debug_res_temp1, *debug_res_temp2;
-    debug_res_temp1 = (real *)malloc(sizeof(real) * M * D);
-    debug_res_temp2 = (real *)malloc(sizeof(real) * N * D);
-    cublasCheckErrors(cublasGetMatrix(M, D, sizeof(real), d_res_temp1, M, debug_res_temp1, M));  
-    cublasCheckErrors(cublasGetMatrix(N, D, sizeof(real), d_res_temp2, N, debug_res_temp2, N));
-    computeTranspose(debug_res_temp1, M, D);
-    computeTranspose(debug_res_temp2, N, D);
-    printf("res_temp1\n");
-    for( i = 0 ; i < M * D; i ++)
-    {
-        printf( "%.4f|", debug_res_temp1[i] );
-        if( i % 15 == 0 )
-            printf( "\n" );
-    }
-    printf("\nres_temp2\n");
-    for( i = 0; i < N * D; i ++)
-    {
-        printf( "%.4f|", debug_res_temp2[i] );
-        if( i % 15 == 0 )
-            printf( "\n" );
-    }
-    printf("\n");
-    free(debug_res_temp1);
-    free(debug_res_temp2);
+
 #endif
 
 
@@ -170,24 +149,54 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     free( debug_zero );
 
 #endif
+    nthread.x=1;   nthread.y=M;    nthread.z=1;
+    nblock.x=N;    nblock.y=1;     nblock.z=D;
 
-    nthread.x=1;   nthread.y=1;    nthread.z=D;
-    nblock.x=M;/*M*/    nblock.y=N;     nblock.z=1;
-
-    gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, M);
+    gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
 
+#define debug
 #ifdef debug 
+    real *debug_res_temp1, *debug_res_temp2;
+    debug_res_temp1 = (real *)malloc(sizeof(real) * M * D);
+    debug_res_temp2 = (real *)malloc(sizeof(real) * N * D);
+    cublasCheckErrors(cublasGetMatrix(M, D, sizeof(real), d_res_temp1, M, debug_res_temp1, M));  
+    cublasCheckErrors(cublasGetMatrix(N, D, sizeof(real), d_res_temp2, N, debug_res_temp2, N));
+
     real *temp_c_a;
     temp_c_a = (real *)malloc( sizeof(real) * N * M);
     cudaMemcpy(temp_c_a, d_a, sizeof(real) * N * M, cudaMemcpyDeviceToHost);
-    computeTranspose( temp_c_a, N, M );
-    for( i = 0; i < M * D; i++ )
+
+
+    computeTranspose(debug_res_temp1, M, D);
+    computeTranspose(debug_res_temp2, N, D);
+    // printf("res_temp1\n");
+    // for( i = 0 ; i < M * D; i ++)
+    // {
+    //     printf( "%.4f|", debug_res_temp1[i] );
+    //     if( i % 15 == 0 )
+    //         printf( "\n" );
+    // }
+    // printf("\nres_temp2\n");
+    // for( i = 0; i < N * D; i ++)
+    // {
+    //     printf( "%.4f|", debug_res_temp2[i] );
+    //     if( i % 15 == 0 )
+    //         printf( "\n" );
+    // }
+    // printf("\n");
+
+
+    //computeTranspose( temp_c_a, N, M );
+    for( i = 0; i < M * N; i++ )
     {
         if(i%15==0)
             printf("\n");
         printf("%.4f|", temp_c_a[i]);
     }
     free( temp_c_a );
+    free(debug_res_temp1);
+    free(debug_res_temp2);
+
 #endif
 
     

From e7e74228fd84cbec95bbb0a88165696a1dcc6fca Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 4 May 2015 21:24:13 +0100
Subject: [PATCH 009/117] gpu exp function

---
 gp_emulator/gpu/predict.cu | 42 +++++++++++++-------------------------
 1 file changed, 14 insertions(+), 28 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 43c4bf1..fde8acc 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -28,11 +28,7 @@ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld,
     ix = blockIdx.x * blockDim.x + threadIdx.x;//N
     iy = blockIdx.y * blockDim.y + threadIdx.y;//M
     iz = blockIdx.z * blockDim.z + threadIdx.z;
-    //for (iz = 0; iz < D; iz++)
-    //{
-        output[IDX2D(ix, iy, Out_ld)] += pow(input1[IDX2D(iy, iz, In1_ld)] - input2[IDX2D(ix, iz, In2_ld)],2);
-    //}
-
+    output[IDX2D(ix, iy, Out_ld)] += pow(input1[IDX2D(iy, iz, In1_ld)] - input2[IDX2D(ix, iz, In2_ld)],2);
 }
 
 
@@ -43,6 +39,14 @@ void gpu_init_zero(real *vec)
     vec[ix] = 0;
 }
 
+// beta = sqrt( alpha * matrix ) 
+__global__
+void gpu_matrixSqrt(real *matrix, real alpha, real beta)
+{
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    matrix[ix] = beta * exp( alpha * matrix[ix]);
+}
+
 
 void computeTranspose(real *matrix, const  int size_in, const  int size_out)
 {
@@ -122,15 +126,6 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     nthread.x=1; nthread.y=D;
     nblock.x=N; nblock.y=1;
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
-    
-
-// check result of gpu_vectorTimesMatrix
-#undef debug
-#ifdef debug
-
-#endif
-
-
     gpu_init_zero<<<ceil(float(N)*float(M)/512),512>>>(d_a);
 //#undef debug
 #ifdef debug
@@ -149,11 +144,15 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     free( debug_zero );
 
 #endif
+
+
+
     nthread.x=1;   nthread.y=M;    nthread.z=1;
     nblock.x=N;    nblock.y=1;     nblock.z=D;
-
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
 
+    gpu_matrixSqrt<<<ceil(float(M)*float(N)/512),512>>>(d_a, -0.5, c_theta_exp[D]);
+    printf("%f", ceil(float(M)*float(N)/512));
 #define debug
 #ifdef debug 
     real *debug_res_temp1, *debug_res_temp2;
@@ -200,21 +199,8 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
 #endif
 
     
-   // cudaFree(d_res_temp1);
-   // cudaFree(d_res_temp2);
-
-    //gpu_cdist<<<1,threads>>>(d_testing,N,);    
-   
 
-       
-    
-//    free(temp_c_a);
     cudaFree(d_theta_exp);
     cudaFree(d_testing);
-
-    //cudaFree(d_aa);
-    //free(c_theta);
-    //free((char*) c_inputs);
-    //return Py_BuildValue ("i",1);
 }
 }

From fe567ef026e045f1b83e3d930cee1f9965becaf0 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 5 May 2015 17:23:26 +0100
Subject: [PATCH 010/117] cunit test scaffolding

---
 gp_emulator/gpu/gpu_predict.h                 |   2 +-
 gp_emulator/gpu/predict.cu                    |  14 +-
 gp_emulator/gpu/test.py                       |  29 +++-
 gp_emulator/gpu/tests/.test1..swp             | Bin 0 -> 4096 bytes
 gp_emulator/gpu/tests/Makefile                |  27 ++++
 .../gpu/tests/data/set_100_250_10/cdist_a.bin | Bin 0 -> 200000 bytes
 .../data/set_100_250_10/cdist_test_var1.bin   | Bin 0 -> 20000 bytes
 .../data/set_100_250_10/cdist_test_var2.bin   | Bin 0 -> 8000 bytes
 .../tests/data/set_100_250_10/expXsqrt.bin    |   1 +
 .../gpu/tests/data/set_100_250_10/inputs.bin  | Bin 0 -> 20000 bytes
 .../gpu/tests/data/set_100_250_10/testing.bin | Bin 0 -> 8000 bytes
 gp_emulator/gpu/tests/gpu_predict_test.h      |   7 +
 gp_emulator/gpu/tests/readTestData.c          |  26 ++++
 gp_emulator/gpu/tests/test1.c                 | 137 ++++++++++++++++++
 14 files changed, 227 insertions(+), 16 deletions(-)
 create mode 100644 gp_emulator/gpu/tests/.test1..swp
 create mode 100644 gp_emulator/gpu/tests/Makefile
 create mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin
 create mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var1.bin
 create mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var2.bin
 create mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/expXsqrt.bin
 create mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/inputs.bin
 create mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/testing.bin
 create mode 100644 gp_emulator/gpu/tests/gpu_predict_test.h
 create mode 100644 gp_emulator/gpu/tests/readTestData.c
 create mode 100644 gp_emulator/gpu/tests/test1.c

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 8bec243..a49666e 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -30,7 +30,7 @@ PyArrayObject *pyvector(PyObject *objin);
 double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
 double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 double **ptrvector(long n);
-void computeTranspose(real *matrix, const  int size_in, const  int size_out);
+//void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
 //void predict(real *c_theta_exp, real **c_inputs,real *c_invQt,real **c_invQ, real **c_testing, int N, int NN, int D, int theta_size);
 //extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int D, int theta_size);
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index fde8acc..dcd9ccf 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -147,12 +147,13 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
 
 
 
-    nthread.x=1;   nthread.y=M;    nthread.z=1;
-    nblock.x=N;    nblock.y=1;     nblock.z=D;
+    nthread.x=1;   nthread.y=5;    nthread.z=1;
+    nblock.x=N;    nblock.y=M/5;     nblock.z=D;
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
 
-    gpu_matrixSqrt<<<ceil(float(M)*float(N)/512),512>>>(d_a, -0.5, c_theta_exp[D]);
-    printf("%f", ceil(float(M)*float(N)/512));
+    //gpu_matrixSqrt<<<ceil(float(M)*float(N)/512),512>>>(d_a, -0.5, c_theta_exp[D]);
+    //printf("%f", ceil(float(M)*float(N)/512));
+
 #define debug
 #ifdef debug 
     real *debug_res_temp1, *debug_res_temp2;
@@ -186,16 +187,15 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
 
 
     //computeTranspose( temp_c_a, N, M );
-    for( i = 0; i < M * N; i++ )
+    for( i = 0; i < M *N ; i++ )
     {
-        if(i%15==0)
+        if(i%15==0 || i%M==0)
             printf("\n");
         printf("%.4f|", temp_c_a[i]);
     }
     free( temp_c_a );
     free(debug_res_temp1);
     free(debug_res_temp2);
-
 #endif
 
     
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index d003bb6..167f3a9 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -46,14 +46,20 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         N=testing.shape[0]
         M=self.inputs.shape[0]
         theta_size=self.theta.size 
-        global test_var_1 ,test_var_2, test_var_3
-        test_var_1= np.sqrt(expX[:(self.D)])*self.inputs
-        test_var_2 = np.sqrt(expX[:(self.D)])*testing
-        test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+        global t_expXsqrt, t_inputs, t_testing
+        t_expXsqrt = expX[:(self.D)]
+        t_inputs = self.inputs
+        t_testing = testing
 
-        print 'sqrt(expX[:(self.D)])*self.inputs=\n', test_var_1
-        print 'sqrt(expX)*testing\n', test_var_2
-        print 'cdist =\n', test_var_3
+        global cdist_test_var_1 ,cdist_test_var_2, cdist_test_var_3
+        cdist_test_var_1= np.sqrt(expX[:(self.D)])*self.inputs
+        cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
+        cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+        cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
+
+        #print 'sqrt(expX[:(self.D)])*self.inputs=\n', test_var_1
+        #print 'sqrt(expX)*testing\n', test_var_2
+        #print 'a_ =\n', test_var_3
 
         a =_gpu_predict.predict_wrap(
             expX,
@@ -84,5 +90,12 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
     gp=GP(P,N,M)
     #gp.predict(testing)
     gp.gpu_predict(testing)
-
     
+    
+    #write testing set
+    t_expXsqrt.tofile("./tests/data/set_%d_%d_%d/expXsqrt.bin"%(N,M,P))
+    t_inputs.tofile("./tests/data/set_%d_%d_%d/inputs.bin"%(N,M,P))
+    t_testing.tofile("./tests/data/set_%d_%d_%d/testing.bin"%(N,M,P))
+    cdist_test_var_1.tofile("./tests/data/set_%d_%d_%d/cdist_test_var1.bin"%(N,M,P))
+    cdist_test_var_2.tofile("./tests/data/set_%d_%d_%d/cdist_test_var2.bin"%(N,M,P))
+    cdist_test_var_3.tofile("./tests/data/set_%d_%d_%d/cdist_a.bin"%(N,M,P))
diff --git a/gp_emulator/gpu/tests/.test1..swp b/gp_emulator/gpu/tests/.test1..swp
new file mode 100644
index 0000000000000000000000000000000000000000..274fc83318b48301883a7d08342afb2a110d88f0
GIT binary patch
literal 4096
zcmYc?2=nw+FxN9-00IFJ0RcCv7#WH)^Ahu50(j`6%#vbVLjxl`O2ATeU<38b^NX^J
z3lfu4^$Uvfvr>~wiuKbAbW?Lna}rDPi}c})^n!RKcBy_zYB5kLh&0qAWYwtrXb6mk
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DfV?MU

literal 0
HcmV?d00001

diff --git a/gp_emulator/gpu/tests/Makefile b/gp_emulator/gpu/tests/Makefile
new file mode 100644
index 0000000..a19a888
--- /dev/null
+++ b/gp_emulator/gpu/tests/Makefile
@@ -0,0 +1,27 @@
+SRCS= test1.c readTestData.c
+OBJS = test1.o readTestData.o
+GPUCC = nvcc
+CC = gcc
+BUILDPATH= #../../build/lib/
+INC = #-I/usr/include/python2.7 -I/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy
+LIB = -lcunit #-lcuda -lcudart -lcublas
+NVLIB = #-L/Developer/NVIDIA/CUDA-7.0/lib/
+
+
+
+
+all: test
+
+test: $(OBJS)
+	$(CC) -o $@ $^ $(INC) $(NVLIB) $(LIB)  
+
+%.o:%.c
+	$(CC)  -c $^  -o $@   
+
+%.o:%.cu
+	$(CC)  -c $^ -o $@ 
+
+clean:
+	rm *.o test
+
+#
diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin
new file mode 100644
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diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var1.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var1.bin
new file mode 100644
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GIT binary patch
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diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var2.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var2.bin
new file mode 100644
index 0000000000000000000000000000000000000000..2e1a3df451e8a6360123b7ad139ae7782d2d9215
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diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
new file mode 100644
index 0000000..c6ac35b
--- /dev/null
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -0,0 +1,7 @@
+#include <stdio.h>
+#include <stdlib.h>
+#define real double
+
+
+
+real *readTestData(char *file_name, int M, int N, int D, int size);
diff --git a/gp_emulator/gpu/tests/readTestData.c b/gp_emulator/gpu/tests/readTestData.c
new file mode 100644
index 0000000..1ff4d5b
--- /dev/null
+++ b/gp_emulator/gpu/tests/readTestData.c
@@ -0,0 +1,26 @@
+#include "gpu_predict_test.h"
+#include <string.h>
+
+
+real *readTestData(char *file_name, int M, int N, int D, int size)
+{
+    
+    char path[200];
+    sprintf(path, "data/set_%d_%d_%d/",N, M, D);
+    strcat(path,file_name);
+    printf("reading data: %s\n",path);
+    
+    real *data;
+    
+    
+    data = (real *)malloc(sizeof(real) * M * N);
+    FILE *file_ptr;
+    file_ptr = fopen(path,"rb");
+    fread(data, sizeof(real), size, file_ptr);
+    fclose(file_ptr);
+    return(data);
+
+}
+
+
+
diff --git a/gp_emulator/gpu/tests/test1.c b/gp_emulator/gpu/tests/test1.c
new file mode 100644
index 0000000..1281a2d
--- /dev/null
+++ b/gp_emulator/gpu/tests/test1.c
@@ -0,0 +1,137 @@
+#include <stdio.h>
+#include <string.h>
+#include "CUnit/Basic.h"
+#include "gpu_predict_test.h"
+
+
+
+
+
+static real * expXsqrt, *inputs, *testing;
+static real * cdist_a;
+static real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
+ 
+
+static int M, N, D;
+static int init_suite1(void)
+{
+    M = 250;
+    N = 100;
+    D = 10;
+   
+    expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
+    inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
+    testing         =  readTestData( "testing.bin", M, N, D, N * D );
+    cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
+    cdist_test_var1 =  readTestData( "cdist_test_var1.bin", M, N, D, M * D);
+    cdist_test_var2 =  readTestData( "cdist_test_var2.bin", M, N, D, N * D);
+    return 0;
+    
+}
+
+
+static int init_suite2(void)
+{
+    M = 250;
+    N = 100;
+    D = 10;
+   
+    expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
+    inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
+    testing         =  readTestData( "testing.bin", M, N, D, N * D );
+    cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
+    cdist_test_var1 =  readTestData( "cdist_test_var1.bin", M, N, D, M * D);
+    cdist_test_var2 =  readTestData( "cdist_test_var2.bin", M, N, D, N * D);
+    return 0;
+    
+}
+
+
+
+
+
+
+
+
+
+
+int clean_suite(void)
+{
+    free(expXsqrt);
+    free(inputs);
+    free(testing);
+    free(cdist_a);
+    free(cdist_test_var1);
+    free(cdist_test_var2);
+    return 0;
+}
+
+void testFPRINTF(void)
+{
+    printf("\ncdist_a[0] = %f\n", cdist_a[0]);
+}
+
+void testFREAD(void)
+{
+}
+
+int main()
+{
+   CU_pSuite pSuite = NULL;
+
+   /* initialize the CUnit test registry */
+   if (CUE_SUCCESS != CU_initialize_registry())
+      return CU_get_error();
+
+   /* add a suite to the registry */
+   pSuite = CU_add_suite("Suit_1", init_suite1, clean_suite);
+   
+   if (NULL == pSuite) {
+      CU_cleanup_registry();
+      return CU_get_error();
+   }
+
+   /* add the tests to the suite */
+   if ((NULL == CU_add_test(pSuite, "test of fprintf()", testFPRINTF)) ||
+       (NULL == CU_add_test(pSuite, "test of fread()", testFREAD)))
+   {
+      CU_cleanup_registry();
+      return CU_get_error();
+   }
+
+  
+ 
+   
+   
+   pSuite = CU_add_suite("Suit_2", init_suite2, clean_suite);
+   
+   if (NULL == pSuite) {
+      CU_cleanup_registry();
+      return CU_get_error();
+   }
+
+   /* add the tests to the suite */
+   if ((NULL == CU_add_test(pSuite, "test of fprintf()", testFPRINTF)) ||
+       (NULL == CU_add_test(pSuite, "test of fread()", testFREAD)))
+   {
+      CU_cleanup_registry();
+      return CU_get_error();
+   }
+
+   /* Run all tests using the CUnit Basic interface */
+   CU_basic_set_mode(CU_BRM_VERBOSE);
+   CU_basic_run_tests();
+   CU_cleanup_registry();
+   return CU_get_error();
+
+   /* Run all tests using the CUnit Basic interface */
+   CU_basic_set_mode(CU_BRM_VERBOSE);
+   CU_basic_run_tests();
+   CU_cleanup_registry();
+   return CU_get_error();
+   
+ 
+
+}
+
+

From 3f382127f212728d6063ca7abcce5b7b8a13db33 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 6 May 2015 12:42:15 +0100
Subject: [PATCH 011/117] tests of gpu vector times matrix

---
 gp_emulator/gpu/Makefile                      |   4 +-
 gp_emulator/gpu/computeTranspose.c            |   2 +-
 gp_emulator/gpu/gpu_predict.h                 |  10 ++--
 gp_emulator/gpu/test.py                       |  47 ++++++++++++------
 gp_emulator/gpu/tests/.test1..swp             | Bin 4096 -> 0 bytes
 gp_emulator/gpu/tests/Makefile                |  19 ++++---
 .../gpu/tests/data/set_100_250_10/cdist_a.bin | Bin 200000 -> 200000 bytes
 .../data/set_100_250_10/cdist_test_var1.bin   | Bin 20000 -> 20000 bytes
 .../data/set_100_250_10/cdist_test_var2.bin   | Bin 8000 -> 8000 bytes
 .../tests/data/set_100_250_10/expXsqrt.bin    |   2 +-
 .../gpu/tests/data/set_100_250_10/inputs.bin  | Bin 20000 -> 20000 bytes
 .../gpu/tests/data/set_100_250_10/testing.bin | Bin 8000 -> 8000 bytes
 gp_emulator/gpu/tests/gpu_predict_test.h      |  18 ++++++-
 gp_emulator/gpu/tests/test1.c                 |  31 +++++++-----
 gp_emulator/gpu/tests/testVecTimesMat.cu      |  38 ++++++++++++++
 15 files changed, 125 insertions(+), 46 deletions(-)
 delete mode 100644 gp_emulator/gpu/tests/.test1..swp
 create mode 100644 gp_emulator/gpu/tests/testVecTimesMat.cu

diff --git a/gp_emulator/gpu/Makefile b/gp_emulator/gpu/Makefile
index e69966d..3ee1f70 100644
--- a/gp_emulator/gpu/Makefile
+++ b/gp_emulator/gpu/Makefile
@@ -15,10 +15,10 @@ all: $(SHARE) $(OBJS)
 
 
 %.so:$(OBJS)
-	$(CC) -shared -fpic -flat_namespace -undefined suppress -o $(BUILDPATH)$@ $^ $(INC) $(NVLIB) $(LIB)
+	$(CC) -shared -fpic -flat_namespace -undefined suppress -o $(BUILDPATH)$@ $^ $(INC) $(NVLIB) $(LIB) 
 
 %.o:%.c
-	$(GPUCC)  -c $^  -o $@  $(INC) $(LIB) $(NVLIB)  --compiler-options '-fPIC' 
+	$(GPUCC)  -c $^  -o $@  $(INC) $(LIB) $(NVLIB)  --compiler-options '-fPIC'
 
 %.o:%.cu
 	$(GPUCC)  -c $^ -o $@ $(INC) $(NVLIB) $(LIB)  --compiler-options '-fPIC' 
diff --git a/gp_emulator/gpu/computeTranspose.c b/gp_emulator/gpu/computeTranspose.c
index 06108eb..27afd91 100644
--- a/gp_emulator/gpu/computeTranspose.c
+++ b/gp_emulator/gpu/computeTranspose.c
@@ -17,4 +17,4 @@ void computeTranspose(real *matrix, const  int size_in, const  int size_out)
 
         }   
     }
-}
\ No newline at end of file
+}
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index a49666e..97d57cd 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -5,8 +5,8 @@
 #include <math.h>
 #include <stdio.h>
 #include <stdlib.h>
- #include <string.h>
- #include <time.h>
+#include <string.h>
+#include <time.h>
 /* Includes, cuda */
 #include <cuda.h>
 #include <cuda_runtime.h>
@@ -30,12 +30,12 @@ PyArrayObject *pyvector(PyObject *objin);
 double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
 double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 double **ptrvector(long n);
-//void computeTranspose(real *matrix, const  int size_in, const  int size_out);
+void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
-//void predict(real *c_theta_exp, real **c_inputs,real *c_invQt,real **c_invQ, real **c_testing, int N, int NN, int D, int theta_size);
+void predict(real *c_theta_exp, real **c_inputs,real *c_invQt,real **c_invQ, real **c_testing, int N, int NN, int D, int theta_size);
 //extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int D, int theta_size);
 __global__ void gpu_cdist(double *d_testing, int M, int N, int P);
-
+__global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
 
 // error check macros
 #define cudaCheckErrors(msg) \
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index 167f3a9..69a4ac9 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -36,27 +36,47 @@ def predict ( self, testing, do_unc=True ):
         else:
 	    return mu, deriv
 
-    def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
-        '''GPU predict function
-        '''
+    def get_testing_val (self, testing, do_unc = True):
+        
         ( nn, D ) = testing.shape
         assert D == self.D
         expX=np.exp(self.theta)
 
-        N=testing.shape[0]
-        M=self.inputs.shape[0]
-        theta_size=self.theta.size 
+
+        
         global t_expXsqrt, t_inputs, t_testing
-        t_expXsqrt = expX[:(self.D)]
+        t_expXsqrt = np.sqrt(expX[:(self.D)])
         t_inputs = self.inputs
         t_testing = testing
 
         global cdist_test_var_1 ,cdist_test_var_2, cdist_test_var_3
-        cdist_test_var_1= np.sqrt(expX[:(self.D)])*self.inputs
+        cdist_test_var_1= t_expXsqrt * t_inputs #np.sqrt(expX[:(self.D)])*self.inputs
         cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
         cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
         cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
+        
+        
+        
+        
+        
+        t_expXsqrt.tofile("./tests/data/set_%d_%d_%d/expXsqrt.bin"%(N,M,P))
+        t_inputs.tofile("./tests/data/set_%d_%d_%d/inputs.bin"%(N,M,P))
+        t_testing.tofile("./tests/data/set_%d_%d_%d/testing.bin"%(N,M,P))
+        cdist_test_var_1.tofile("./tests/data/set_%d_%d_%d/cdist_test_var1.bin"%(N,M,P))
+        cdist_test_var_2.tofile("./tests/data/set_%d_%d_%d/cdist_test_var2.bin"%(N,M,P))
+        cdist_test_var_3.tofile("./tests/data/set_%d_%d_%d/cdist_a.bin"%(N,M,P))
+    
+    
+    def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
+        '''GPU predict function
+        '''
+        ( nn, D ) = testing.shape
+        assert D == self.D
+        expX=np.exp(self.theta)
 
+        N=testing.shape[0]
+        M=self.inputs.shape[0]
+        theta_size=self.theta.size 
         #print 'sqrt(expX[:(self.D)])*self.inputs=\n', test_var_1
         #print 'sqrt(expX)*testing\n', test_var_2
         #print 'a_ =\n', test_var_3
@@ -89,13 +109,8 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
     testing=np.random.random((N,P))
     gp=GP(P,N,M)
     #gp.predict(testing)
-    gp.gpu_predict(testing)
-    
+    #gp.gpu_predict(testing)
+    gp.get_testing_val(testing)
     
     #write testing set
-    t_expXsqrt.tofile("./tests/data/set_%d_%d_%d/expXsqrt.bin"%(N,M,P))
-    t_inputs.tofile("./tests/data/set_%d_%d_%d/inputs.bin"%(N,M,P))
-    t_testing.tofile("./tests/data/set_%d_%d_%d/testing.bin"%(N,M,P))
-    cdist_test_var_1.tofile("./tests/data/set_%d_%d_%d/cdist_test_var1.bin"%(N,M,P))
-    cdist_test_var_2.tofile("./tests/data/set_%d_%d_%d/cdist_test_var2.bin"%(N,M,P))
-    cdist_test_var_3.tofile("./tests/data/set_%d_%d_%d/cdist_a.bin"%(N,M,P))
+   
diff --git a/gp_emulator/gpu/tests/.test1..swp b/gp_emulator/gpu/tests/.test1..swp
deleted file mode 100644
index 274fc83318b48301883a7d08342afb2a110d88f0..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 4096
zcmYc?2=nw+FxN9-00IFJ0RcCv7#WH)^Ahu50(j`6%#vbVLjxl`O2ATeU<38b^NX^J
z3lfu4^$Uvfvr>~wiuKbAbW?Lna}rDPi}c})^n!RKcBy_zYB5kLh&0qAWYwtrXb6mk
z07eM#G8h{f8i0hAl@t|(g+eh@j0%i~z-S1JhQMeDjE2By2#kinXb6mkz-R~z-w*%*
DfV?MU

diff --git a/gp_emulator/gpu/tests/Makefile b/gp_emulator/gpu/tests/Makefile
index a19a888..bd2a814 100644
--- a/gp_emulator/gpu/tests/Makefile
+++ b/gp_emulator/gpu/tests/Makefile
@@ -1,11 +1,14 @@
-SRCS= test1.c readTestData.c
-OBJS = test1.o readTestData.o
+SRCS= test1.c  testVecTimesMat.cu \
+      readTestData.c ../computeTranspose.c ../predict.cu
+
+OBJS = test1.o readTestData.o testVecTimesMat.o \
+       ../computeTranspose.o ../predict.o
 GPUCC = nvcc
 CC = gcc
 BUILDPATH= #../../build/lib/
-INC = #-I/usr/include/python2.7 -I/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy
-LIB = -lcunit #-lcuda -lcudart -lcublas
-NVLIB = #-L/Developer/NVIDIA/CUDA-7.0/lib/
+INC = -I/usr/include/python2.7 -I/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy
+LIB = -link  -lcunit -lcuda -lcudart -lcublas
+NVLIB = -L/Developer/NVIDIA/CUDA-7.0/lib/
 
 
 
@@ -13,13 +16,13 @@ NVLIB = #-L/Developer/NVIDIA/CUDA-7.0/lib/
 all: test
 
 test: $(OBJS)
-	$(CC) -o $@ $^ $(INC) $(NVLIB) $(LIB)  
+	$(GPUCC)  -o $@ $^ $(INC) $(NVLIB) $(LIB)  
 
 %.o:%.c
-	$(CC)  -c $^  -o $@   
+	$(GPUCC) -x cu  -dc $^  -o $@   $(INC) $(LIB) $(NVLIB)
 
 %.o:%.cu
-	$(CC)  -c $^ -o $@ 
+	$(GPUCC)  -dc $^ -o $@ $(INC) $(NVLIB) $(NVLIB)
 
 clean:
 	rm *.o test
diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin
index b2ad325474f1685bc8f3189dc19380e796e32f0e..71ac359e07129cfb7935a08a02e178d77f64770a 100644
GIT binary patch
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diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var2.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var2.bin
index 2e1a3df451e8a6360123b7ad139ae7782d2d9215..35efffd8b018280f892f4abc2b9c4bc1764f52dd 100644
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diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index c6ac35b..61924e8 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -1,7 +1,23 @@
 #include <stdio.h>
 #include <stdlib.h>
-#define real double
+#include "../gpu_predict.h"
+#include "CUnit/Basic.h"
+#include "../gpu_predict.h"
+#include <cuda.h>
+
+
+
+
+static  real *expXsqrt, *inputs, *testing;
+static real *cdist_a;
+static  real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
+
+
+static  int M, N, D;
 
 
 
 real *readTestData(char *file_name, int M, int N, int D, int size);
+
+
+void testVecTimesMat(real *c_vec,  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads);
diff --git a/gp_emulator/gpu/tests/test1.c b/gp_emulator/gpu/tests/test1.c
index 1281a2d..7ddab76 100644
--- a/gp_emulator/gpu/tests/test1.c
+++ b/gp_emulator/gpu/tests/test1.c
@@ -1,18 +1,17 @@
 #include <stdio.h>
 #include <string.h>
-#include "CUnit/Basic.h"
 #include "gpu_predict_test.h"
+#include "cuda.h"
 
-
-
-
-
-static real * expXsqrt, *inputs, *testing;
-static real * cdist_a;
+/*
+static real *expXsqrt, *inputs, *testing;
+static real *cdist_a;
 static real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
  
 
-static int M, N, D;
+static  int M, N, D;
+
+*/
 static int init_suite1(void)
 {
     M = 250;
@@ -66,13 +65,21 @@ int clean_suite(void)
     return 0;
 }
 
-void testFPRINTF(void)
+static void tests_VecTimesMat(void)
 {
-    printf("\ncdist_a[0] = %f\n", cdist_a[0]);
+    dim3 nblocks_1(M,1);
+    dim3 nthreads_1(1,D);
+    testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D, nblocks_1, nthreads_1 );
+    dim3 nblocks_2(N,1);
+    dim3 nthreads_2(1,D);
+    testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D, nblocks_2, nthreads_2 );
 }
 
 void testFREAD(void)
 {
+    computeTranspose(cdist_a, M, N);
+
+    printf("\ncdist_a[0] = %f\n", cdist_a[1]);
 }
 
 int main()
@@ -92,7 +99,7 @@ int main()
    }
 
    /* add the tests to the suite */
-   if ((NULL == CU_add_test(pSuite, "test of fprintf()", testFPRINTF)) ||
+   if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
        (NULL == CU_add_test(pSuite, "test of fread()", testFREAD)))
    {
       CU_cleanup_registry();
@@ -111,7 +118,7 @@ int main()
    }
 
    /* add the tests to the suite */
-   if ((NULL == CU_add_test(pSuite, "test of fprintf()", testFPRINTF)) ||
+   if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
        (NULL == CU_add_test(pSuite, "test of fread()", testFREAD)))
    {
       CU_cleanup_registry();
diff --git a/gp_emulator/gpu/tests/testVecTimesMat.cu b/gp_emulator/gpu/tests/testVecTimesMat.cu
new file mode 100644
index 0000000..699bc38
--- /dev/null
+++ b/gp_emulator/gpu/tests/testVecTimesMat.cu
@@ -0,0 +1,38 @@
+#include "gpu_predict_test.h"
+
+
+void testVecTimesMat(real *c_vec,  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads)
+{
+    real *d_vec, *d_mat, *d_res;
+    real *c_res_gpu;
+    
+    CU_ASSERT (vec_len == mat_ncols);
+    int i;
+    int epsilon;
+    cudaMalloc((void **)&d_vec, sizeof(real) * vec_len );
+    cudaMalloc((void **)&d_mat, sizeof(real) * mat_nrows * mat_ncols );
+    cudaMalloc((void **)&d_res, sizeof(real) * mat_nrows * mat_ncols );
+    
+    c_res_gpu = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols );
+
+
+    computeTranspose(c_mat, mat_ncols, mat_nrows);
+
+    cublasCheckErrors(cublasSetVector( vec_len, sizeof(real), c_vec, 1, d_vec, 1 ));
+    cublasCheckErrors(cublasSetMatrix( mat_nrows, mat_ncols, sizeof(real), c_mat, mat_nrows, d_mat, mat_nrows));
+    
+    gpu_vectorTimesMatrix <<< nblock, nthreads >>> ( d_mat, d_vec, d_res, mat_nrows );
+
+    cudaMemcpy(c_res_gpu, d_res, sizeof(real) * mat_nrows * mat_ncols, cudaMemcpyDeviceToHost);
+    computeTranspose(c_res_gpu, mat_nrows, mat_ncols);
+    
+    for( i = 0; i < mat_nrows * mat_ncols; i++)
+    {
+        epsilon = abs( c_res[i] - c_res_gpu[i] );
+        CU_ASSERT( epsilon < 1e-6 );
+//        if( epsilon < 1e-6 )
+//            printf("res [%d] = %f   gpu result [%d] = %f\n",i, c_res[i], i, c_res_gpu[i]);
+    }
+
+}
+    

From a565a0306c160811d910bdc04f03dd8e0ccc756c Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 6 May 2015 17:26:13 +0100
Subject: [PATCH 012/117] tests of cdist()

---
 gp_emulator/gpu/gpu_predict.h            |  5 +-
 gp_emulator/gpu/tests/Makefile           |  4 +-
 gp_emulator/gpu/tests/gpu_predict_test.h |  3 +-
 gp_emulator/gpu/tests/testCdist.cu       | 77 ++++++++++++++++++++++++
 gp_emulator/gpu/tests/testVecTimesMat.cu | 21 +++++--
 5 files changed, 102 insertions(+), 8 deletions(-)
 create mode 100644 gp_emulator/gpu/tests/testCdist.cu

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 97d57cd..b1fc532 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -34,7 +34,10 @@ void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
 void predict(real *c_theta_exp, real **c_inputs,real *c_invQt,real **c_invQ, real **c_testing, int N, int NN, int D, int theta_size);
 //extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int D, int theta_size);
-__global__ void gpu_cdist(double *d_testing, int M, int N, int P);
+//__global__ void gpu_cdist(double *d_testing, int M, int N, int P);
+
+__global__
+void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D);
 __global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
 
 // error check macros
diff --git a/gp_emulator/gpu/tests/Makefile b/gp_emulator/gpu/tests/Makefile
index bd2a814..fba60cc 100644
--- a/gp_emulator/gpu/tests/Makefile
+++ b/gp_emulator/gpu/tests/Makefile
@@ -1,7 +1,7 @@
-SRCS= test1.c  testVecTimesMat.cu \
+SRCS= test1.c  testVecTimesMat.cu testCdist.cu\
       readTestData.c ../computeTranspose.c ../predict.cu
 
-OBJS = test1.o readTestData.o testVecTimesMat.o \
+OBJS = test1.o readTestData.o testVecTimesMat.o testCdist.o \
        ../computeTranspose.o ../predict.o
 GPUCC = nvcc
 CC = gcc
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index 61924e8..f95fc03 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -20,4 +20,5 @@ static  int M, N, D;
 real *readTestData(char *file_name, int M, int N, int D, int size);
 
 
-void testVecTimesMat(real *c_vec,  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads);
+void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads);
+void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads);
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
new file mode 100644
index 0000000..d10eafe
--- /dev/null
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -0,0 +1,77 @@
+#include "gpu_predict_test.h"
+#define IDX2D(i,j,ld) (((j)*(ld))+(i))
+
+
+void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads)
+{
+    int i, j, k;
+    real epsilon;
+    real *in1_T, *in2_T, *gpu_res;
+    real *d_in1, *d_in2, *d_res; 
+
+    in1_T = (real *)malloc( sizeof(real) * in1_nrows * in_ncols );
+    in2_T = (real *)malloc( sizeof(real) * in2_nrows * in_ncols );
+    gpu_res = (real *)malloc( sizeof(real) * in1_nrows * in2_nrows ); 
+    for( i = 0; i < in1_nrows * in_ncols; i++ )
+        in1_T[i] = in1[i];
+
+    for( i = 0; i < in2_nrows * in_ncols; i++ )
+        in2_T[i] = in2[i];
+
+    computeTranspose( in1_T, in_ncols, in1_nrows );
+    computeTranspose( in2_T, in_ncols, in2_nrows );
+    
+    
+    
+/*    for( i = 0; i < in1_nrows * in2_nrows; i++ )
+        gpu_res[i] = 0;
+    for( i = 0; i < in2_nrows; i++)
+    {
+        for( j = 0; j < in1_nrows; j++ )
+        {
+            for( k = 0; k < in_ncols; k++ )
+            {
+                gpu_res[IDX2D(i, j, in2_nrows)] += pow(in1_T[IDX2D(j, k, in1_nrows)] - in2_T[IDX2D(i, k, in2_nrows)],2);
+            }
+        }
+    }
+    
+    for( i = 0; i < in1_nrows * in2_nrows; i++ )
+    {
+        epsilon = abs( res[i] - gpu_res[i] );
+        CU_ASSERT( epsilon < 1e-6 );
+    }
+*/
+    
+    /*GPU part*/
+    for( i = 0; i < in1_nrows * in2_nrows; i++ )
+        gpu_res[i] = 0;
+    
+    
+    cudaMalloc((void **)&d_in1, sizeof(real) * in1_nrows * in_ncols );
+    cudaMalloc((void **)&d_in2, sizeof(real) * in2_nrows * in_ncols );
+    cudaMalloc((void **)&d_res, sizeof(real) * in2_nrows * in1_nrows );
+
+    cublasCheckErrors(cublasSetMatrix( in1_nrows, in_ncols, sizeof(real), in1_T, in1_nrows, d_in1, in1_nrows) );
+    cublasCheckErrors(cublasSetMatrix( in2_nrows, in_ncols, sizeof(real), in2_T, in2_nrows, d_in2, in2_nrows) );
+    cublasCheckErrors(cublasSetMatrix( in2_nrows, in1_nrows, sizeof(real), gpu_res, in2_nrows, d_res,in2_nrows) );
+    gpu_cdist <<< nblocks, nthreads  >>>(d_in1, d_in2, d_res, in1_nrows, in2_nrows, in2_nrows, in_ncols);
+    
+    cudaMemcpy(gpu_res, d_res, sizeof(real) * in2_nrows * in1_nrows, cudaMemcpyDeviceToHost);
+
+    for( i = 0; i < in1_nrows * in2_nrows; i++ )
+    {
+        epsilon = abs( res[i] - gpu_res[i] );
+        CU_ASSERT( epsilon < 1e-6 );
+    }
+
+ 
+   
+   free(in1_T);
+   free(in2_T);
+   free(gpu_res);
+   
+   cudaFree(d_in1);
+   cudaFree(d_in2);
+   cudaFree(d_res);
+}
diff --git a/gp_emulator/gpu/tests/testVecTimesMat.cu b/gp_emulator/gpu/tests/testVecTimesMat.cu
index 699bc38..e5f21ee 100644
--- a/gp_emulator/gpu/tests/testVecTimesMat.cu
+++ b/gp_emulator/gpu/tests/testVecTimesMat.cu
@@ -1,11 +1,14 @@
 #include "gpu_predict_test.h"
 
 
-void testVecTimesMat(real *c_vec,  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads)
+void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads)
 {
     real *d_vec, *d_mat, *d_res;
     real *c_res_gpu;
-    
+
+    real *c_mat_T;
+
+
     CU_ASSERT (vec_len == mat_ncols);
     int i;
     int epsilon;
@@ -13,13 +16,16 @@ void testVecTimesMat(real *c_vec,  real *c_mat, const real *c_res,const int vec_
     cudaMalloc((void **)&d_mat, sizeof(real) * mat_nrows * mat_ncols );
     cudaMalloc((void **)&d_res, sizeof(real) * mat_nrows * mat_ncols );
     
+    c_mat_T = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols);
     c_res_gpu = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols );
 
+    for( i = 0; i < mat_nrows * mat_ncols; i++ )
+        c_mat_T[i] = c_mat[i];
+    computeTranspose(c_mat_T, mat_ncols, mat_nrows);
 
-    computeTranspose(c_mat, mat_ncols, mat_nrows);
 
     cublasCheckErrors(cublasSetVector( vec_len, sizeof(real), c_vec, 1, d_vec, 1 ));
-    cublasCheckErrors(cublasSetMatrix( mat_nrows, mat_ncols, sizeof(real), c_mat, mat_nrows, d_mat, mat_nrows));
+    cublasCheckErrors(cublasSetMatrix( mat_nrows, mat_ncols, sizeof(real), c_mat_T, mat_nrows, d_mat, mat_nrows));
     
     gpu_vectorTimesMatrix <<< nblock, nthreads >>> ( d_mat, d_vec, d_res, mat_nrows );
 
@@ -34,5 +40,12 @@ void testVecTimesMat(real *c_vec,  real *c_mat, const real *c_res,const int vec_
 //            printf("res [%d] = %f   gpu result [%d] = %f\n",i, c_res[i], i, c_res_gpu[i]);
     }
 
+    free(c_res_gpu);
+    free(c_mat_T);
+
+    cudaFree(d_vec);
+    cudaFree(d_mat);
+    cudaFree(d_res);
+
 }
     

From 14808d13c1b1c6e2ac8b433117f4bf7edc262861 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 7 May 2015 12:16:21 +0100
Subject: [PATCH 013/117] test of matrixExp

---
 gp_emulator/gpu/gpu_predict.h                 |  14 ++--
 gp_emulator/gpu/predict.cu                    |   4 +-
 gp_emulator/gpu/test.py                       |   7 +-
 gp_emulator/gpu/tests/Makefile                |   4 +-
 .../gpu/tests/data/set_100_250_10/cdist_a.bin | Bin 200000 -> 200000 bytes
 .../tests/data/set_100_250_10/cdist_expa.bin  | Bin 0 -> 200000 bytes
 .../data/set_100_250_10/cdist_test_var1.bin   | Bin 20000 -> 20000 bytes
 .../data/set_100_250_10/cdist_test_var2.bin   | Bin 8000 -> 8000 bytes
 .../gpu/tests/data/set_100_250_10/expX.bin    | Bin 0 -> 96 bytes
 .../tests/data/set_100_250_10/expXsqrt.bin    |   2 +-
 .../gpu/tests/data/set_100_250_10/inputs.bin  | Bin 20000 -> 20000 bytes
 .../gpu/tests/data/set_100_250_10/testing.bin | Bin 8000 -> 8000 bytes
 gp_emulator/gpu/tests/gpu_predict_test.h      |   5 +-
 gp_emulator/gpu/tests/readTestData.c          |   2 +-
 gp_emulator/gpu/tests/test1.c                 |  64 +++++++++++++++---
 gp_emulator/gpu/tests/testMatrixExp.cu        |  32 +++++++++
 16 files changed, 103 insertions(+), 31 deletions(-)
 create mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/cdist_expa.bin
 create mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/expX.bin
 create mode 100644 gp_emulator/gpu/tests/testMatrixExp.cu

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index b1fc532..80c4138 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -14,18 +14,10 @@
 
 #define real double
 
-/*
-* he
-*/
 //#ifndef GPU_PREDICT_H
 //#define GPU_PREDICT_H
 
 
-//void hello();
-
-
-
-
 PyArrayObject *pyvector(PyObject *objin);
 double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
 double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
@@ -33,12 +25,14 @@ double **ptrvector(long n);
 void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
 void predict(real *c_theta_exp, real **c_inputs,real *c_invQt,real **c_invQ, real **c_testing, int N, int NN, int D, int theta_size);
+
 //extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int D, int theta_size);
 //__global__ void gpu_cdist(double *d_testing, int M, int N, int P);
 
-__global__
-void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D);
+__global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D);
 __global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
+__global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
+
 
 // error check macros
 #define cudaCheckErrors(msg) \
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index dcd9ccf..dd50cac 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -41,7 +41,7 @@ void gpu_init_zero(real *vec)
 
 // beta = sqrt( alpha * matrix ) 
 __global__
-void gpu_matrixSqrt(real *matrix, real alpha, real beta)
+void gpu_matrixExp(real *matrix, real alpha, real beta)
 {
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
     matrix[ix] = beta * exp( alpha * matrix[ix]);
@@ -151,7 +151,7 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     nblock.x=N;    nblock.y=M/5;     nblock.z=D;
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
 
-    //gpu_matrixSqrt<<<ceil(float(M)*float(N)/512),512>>>(d_a, -0.5, c_theta_exp[D]);
+    gpu_matrixExp<<<ceil(float(M)*float(N)/512),512>>>(d_a, -0.5, c_theta_exp[D]);
     //printf("%f", ceil(float(M)*float(N)/512));
 
 #define debug
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index 69a4ac9..825365d 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -45,6 +45,7 @@ def get_testing_val (self, testing, do_unc = True):
 
         
         global t_expXsqrt, t_inputs, t_testing
+        t_expX = expX
         t_expXsqrt = np.sqrt(expX[:(self.D)])
         t_inputs = self.inputs
         t_testing = testing
@@ -54,18 +55,18 @@ def get_testing_val (self, testing, do_unc = True):
         cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
         cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
         cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
+       
         
         
         
-        
-        
+        t_expX.tofile("./tests/data/set_%d_%d_%d/expX.bin"%(N,M,P))
         t_expXsqrt.tofile("./tests/data/set_%d_%d_%d/expXsqrt.bin"%(N,M,P))
         t_inputs.tofile("./tests/data/set_%d_%d_%d/inputs.bin"%(N,M,P))
         t_testing.tofile("./tests/data/set_%d_%d_%d/testing.bin"%(N,M,P))
         cdist_test_var_1.tofile("./tests/data/set_%d_%d_%d/cdist_test_var1.bin"%(N,M,P))
         cdist_test_var_2.tofile("./tests/data/set_%d_%d_%d/cdist_test_var2.bin"%(N,M,P))
         cdist_test_var_3.tofile("./tests/data/set_%d_%d_%d/cdist_a.bin"%(N,M,P))
-    
+        cdist_test_var_4.tofile("./tests/data/set_%d_%d_%d/cdist_expa.bin"%(N,M,P))    
     
     def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         '''GPU predict function
diff --git a/gp_emulator/gpu/tests/Makefile b/gp_emulator/gpu/tests/Makefile
index fba60cc..6a8baea 100644
--- a/gp_emulator/gpu/tests/Makefile
+++ b/gp_emulator/gpu/tests/Makefile
@@ -1,7 +1,7 @@
-SRCS= test1.c  testVecTimesMat.cu testCdist.cu\
+SRCS= test1.c  testVecTimesMat.cu testCdist.cu testMatrixExp.cu\
       readTestData.c ../computeTranspose.c ../predict.cu
 
-OBJS = test1.o readTestData.o testVecTimesMat.o testCdist.o \
+OBJS = test1.o readTestData.o testVecTimesMat.o testCdist.o testMatrixExp.o\
        ../computeTranspose.o ../predict.o
 GPUCC = nvcc
 CC = gcc
diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin
index 71ac359e07129cfb7935a08a02e178d77f64770a..152e4ca9818cdc4825081b6e9ca12a5fdc5511b6 100644
GIT binary patch
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diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var1.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var1.bin
index 160459450c0fddd68e4f848021ad0a2faeb766a1..970847e0d174e540ee9b5f7b3c1076b98f175670 100644
GIT binary patch
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diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var2.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var2.bin
index 35efffd8b018280f892f4abc2b9c4bc1764f52dd..07f5e65d9d0edfdca761b721f224fde5a2481982 100644
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diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index f95fc03..1ba5e55 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -8,8 +8,8 @@
 
 
 
-static  real *expXsqrt, *inputs, *testing;
-static real *cdist_a;
+static  real *expX, *expXsqrt, *inputs, *testing;
+static  real *cdist_a, *cdist_expa;
 static  real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
 
 
@@ -22,3 +22,4 @@ real *readTestData(char *file_name, int M, int N, int D, int size);
 
 void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads);
 void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads);
+void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
diff --git a/gp_emulator/gpu/tests/readTestData.c b/gp_emulator/gpu/tests/readTestData.c
index 1ff4d5b..e78731b 100644
--- a/gp_emulator/gpu/tests/readTestData.c
+++ b/gp_emulator/gpu/tests/readTestData.c
@@ -13,7 +13,7 @@ real *readTestData(char *file_name, int M, int N, int D, int size)
     real *data;
     
     
-    data = (real *)malloc(sizeof(real) * M * N);
+    data = (real *)malloc(sizeof(real) * size);
     FILE *file_ptr;
     file_ptr = fopen(path,"rb");
     fread(data, sizeof(real), size, file_ptr);
diff --git a/gp_emulator/gpu/tests/test1.c b/gp_emulator/gpu/tests/test1.c
index 7ddab76..101d172 100644
--- a/gp_emulator/gpu/tests/test1.c
+++ b/gp_emulator/gpu/tests/test1.c
@@ -17,13 +17,15 @@ static int init_suite1(void)
     M = 250;
     N = 100;
     D = 10;
-   
+
+    expX            =  readTestData( "expX.bin", M, N, D, D+2);
     expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
     inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
     testing         =  readTestData( "testing.bin", M, N, D, N * D );
-    cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
     cdist_test_var1 =  readTestData( "cdist_test_var1.bin", M, N, D, M * D);
     cdist_test_var2 =  readTestData( "cdist_test_var2.bin", M, N, D, N * D);
+    cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
+    cdist_expa      =  readTestData( "cdist_expa.bin", M, N, D, M * N ); 
     return 0;
     
 }
@@ -35,12 +37,16 @@ static int init_suite2(void)
     N = 100;
     D = 10;
    
+    expX            =  readTestData( "expX.bin", M, N, D, D+2);
     expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
     inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
     testing         =  readTestData( "testing.bin", M, N, D, N * D );
-    cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
     cdist_test_var1 =  readTestData( "cdist_test_var1.bin", M, N, D, M * D);
     cdist_test_var2 =  readTestData( "cdist_test_var2.bin", M, N, D, N * D);
+    cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
+    cdist_expa      =  readTestData( "cdist_expa.bin", M, N, D, M * N );
+ 
+    
     return 0;
     
 }
@@ -70,18 +76,54 @@ static void tests_VecTimesMat(void)
     dim3 nblocks_1(M,1);
     dim3 nthreads_1(1,D);
     testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D, nblocks_1, nthreads_1 );
-    dim3 nblocks_2(N,1);
-    dim3 nthreads_2(1,D);
+    dim3 nblocks_2(N/50,1);
+    dim3 nthreads_2(50,D);
     testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D, nblocks_2, nthreads_2 );
 }
 
-void testFREAD(void)
+void tests_cdist(void)
 {
-    computeTranspose(cdist_a, M, N);
+    dim3 nblocks(N/100, M/5, D);
+    dim3 nthreads(100, 5, 1);
+    testCdist(cdist_test_var1,cdist_test_var2, cdist_a, M, N, D, nblocks, nthreads);
+}
 
-    printf("\ncdist_a[0] = %f\n", cdist_a[1]);
+
+void tests_matrixExp(void)
+{
+    testMatrixExp( cdist_a, cdist_expa, -0.5, expX[D], M * N );
 }
 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 int main()
 {
    CU_pSuite pSuite = NULL;
@@ -100,7 +142,7 @@ int main()
 
    /* add the tests to the suite */
    if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
-       (NULL == CU_add_test(pSuite, "test of fread()", testFREAD)))
+       (NULL == CU_add_test(pSuite, "test of fread()", tests_cdist)))
    {
       CU_cleanup_registry();
       return CU_get_error();
@@ -119,7 +161,9 @@ int main()
 
    /* add the tests to the suite */
    if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
-       (NULL == CU_add_test(pSuite, "test of fread()", testFREAD)))
+       (NULL == CU_add_test(pSuite, "test of gpu_cdist", tests_cdist))||
+       (NULL == CU_add_test(pSuite, "test of gpu_MatrixExp", tests_matrixExp))
+      )
    {
       CU_cleanup_registry();
       return CU_get_error();
diff --git a/gp_emulator/gpu/tests/testMatrixExp.cu b/gp_emulator/gpu/tests/testMatrixExp.cu
new file mode 100644
index 0000000..131ad34
--- /dev/null
+++ b/gp_emulator/gpu/tests/testMatrixExp.cu
@@ -0,0 +1,32 @@
+#include "gpu_predict_test.h"
+
+
+void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size)
+{
+    int i;
+    real error;
+    real *d_mat;
+    real *gpu_res;
+
+    gpu_res = (real *)malloc(sizeof(real) * size);
+    cudaMalloc((void **)&d_mat, sizeof(real) * size);
+
+    cudaMemcpy(d_mat, mat, sizeof(real) * size, cudaMemcpyHostToDevice);
+    int nblocks , nthreads;
+    nthreads = 512;
+    nblocks = ceil( float(size) / nthreads);
+    
+    gpu_matrixExp<<< nblocks, nthreads  >>> (d_mat, alpha, beta);
+
+    cudaMemcpy( gpu_res, d_mat, sizeof(real) * size, cudaMemcpyDeviceToHost);
+    
+    for( i = 0; i < size; i++ )
+    {
+        error = abs( gpu_res[i] - res[i] );
+        CU_ASSERT( error < 1e-6 );
+    }
+
+    cudaFree(d_mat);
+    free(gpu_res);
+}
+    

From 1f9883d8a7e98810eec7fd88fa1dfe086420580c Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 7 May 2015 13:45:13 +0100
Subject: [PATCH 014/117] remove debug

---
 gp_emulator/gpu/predict.cu | 60 --------------------------------------
 1 file changed, 60 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index dd50cac..2ec3b46 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -127,23 +127,6 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     nblock.x=N; nblock.y=1;
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
     gpu_init_zero<<<ceil(float(N)*float(M)/512),512>>>(d_a);
-//#undef debug
-#ifdef debug
-    real * debug_zero;
-    debug_zero = (real *)malloc(sizeof(real) * N * M);
-    cublasCheckErrors(cublasGetMatrix(N, M, sizeof(real), d_a, N, debug_zero, N));  
-    for( i = 0; i < M * N; i ++)
-    {
-        if( debug_zero[i] != 0 )
-        {
-            printf( "[ERROR!] : gpu_init_zero.\n" );
-            exit ( EXIT_FAILURE );            
-        }
-
-    }
-    free( debug_zero );
-
-#endif
 
 
 
@@ -154,49 +137,6 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     gpu_matrixExp<<<ceil(float(M)*float(N)/512),512>>>(d_a, -0.5, c_theta_exp[D]);
     //printf("%f", ceil(float(M)*float(N)/512));
 
-#define debug
-#ifdef debug 
-    real *debug_res_temp1, *debug_res_temp2;
-    debug_res_temp1 = (real *)malloc(sizeof(real) * M * D);
-    debug_res_temp2 = (real *)malloc(sizeof(real) * N * D);
-    cublasCheckErrors(cublasGetMatrix(M, D, sizeof(real), d_res_temp1, M, debug_res_temp1, M));  
-    cublasCheckErrors(cublasGetMatrix(N, D, sizeof(real), d_res_temp2, N, debug_res_temp2, N));
-
-    real *temp_c_a;
-    temp_c_a = (real *)malloc( sizeof(real) * N * M);
-    cudaMemcpy(temp_c_a, d_a, sizeof(real) * N * M, cudaMemcpyDeviceToHost);
-
-
-    computeTranspose(debug_res_temp1, M, D);
-    computeTranspose(debug_res_temp2, N, D);
-    // printf("res_temp1\n");
-    // for( i = 0 ; i < M * D; i ++)
-    // {
-    //     printf( "%.4f|", debug_res_temp1[i] );
-    //     if( i % 15 == 0 )
-    //         printf( "\n" );
-    // }
-    // printf("\nres_temp2\n");
-    // for( i = 0; i < N * D; i ++)
-    // {
-    //     printf( "%.4f|", debug_res_temp2[i] );
-    //     if( i % 15 == 0 )
-    //         printf( "\n" );
-    // }
-    // printf("\n");
-
-
-    //computeTranspose( temp_c_a, N, M );
-    for( i = 0; i < M *N ; i++ )
-    {
-        if(i%15==0 || i%M==0)
-            printf("\n");
-        printf("%.4f|", temp_c_a[i]);
-    }
-    free( temp_c_a );
-    free(debug_res_temp1);
-    free(debug_res_temp2);
-#endif
 
     
 

From aca9572fceaef98ee46469309da6b5ff94888cb6 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sat, 9 May 2015 19:42:27 +0100
Subject: [PATCH 015/117] add cublasgemm tests

---
 gp_emulator/gpu/tests/Makefile           |  2 +
 gp_emulator/gpu/tests/gpu_predict_test.h |  9 +--
 gp_emulator/gpu/tests/test1.c            | 18 ++++--
 gp_emulator/gpu/tests/testCublasgemm.cu  | 76 ++++++++++++++++++++++++
 4 files changed, 96 insertions(+), 9 deletions(-)
 create mode 100644 gp_emulator/gpu/tests/testCublasgemm.cu

diff --git a/gp_emulator/gpu/tests/Makefile b/gp_emulator/gpu/tests/Makefile
index 6a8baea..fa2364c 100644
--- a/gp_emulator/gpu/tests/Makefile
+++ b/gp_emulator/gpu/tests/Makefile
@@ -1,7 +1,9 @@
 SRCS= test1.c  testVecTimesMat.cu testCdist.cu testMatrixExp.cu\
+      testCublasgemm.cu\
       readTestData.c ../computeTranspose.c ../predict.cu
 
 OBJS = test1.o readTestData.o testVecTimesMat.o testCdist.o testMatrixExp.o\
+       testCublasgemm.o\
        ../computeTranspose.o ../predict.o
 GPUCC = nvcc
 CC = gcc
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index 1ba5e55..3ec84f5 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -8,10 +8,10 @@
 
 
 
-static  real *expX, *expXsqrt, *inputs, *testing;
-static  real *cdist_a, *cdist_expa;
-static  real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
-
+static real *expX, *expXsqrt, *inputs, *testing, *invQ;
+static real *cdist_a, *cdist_expa;
+static real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
+static real *var_test1;
 
 static  int M, N, D;
 
@@ -23,3 +23,4 @@ real *readTestData(char *file_name, int M, int N, int D, int size);
 void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads);
 void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads);
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
+void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols);
diff --git a/gp_emulator/gpu/tests/test1.c b/gp_emulator/gpu/tests/test1.c
index 101d172..49d3fdf 100644
--- a/gp_emulator/gpu/tests/test1.c
+++ b/gp_emulator/gpu/tests/test1.c
@@ -18,6 +18,8 @@ static int init_suite1(void)
     N = 100;
     D = 10;
 
+    invQ            =  readTestData( "invQ.bin", M, N, D, M * M);
+    
     expX            =  readTestData( "expX.bin", M, N, D, D+2);
     expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
     inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
@@ -26,6 +28,8 @@ static int init_suite1(void)
     cdist_test_var2 =  readTestData( "cdist_test_var2.bin", M, N, D, N * D);
     cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
     cdist_expa      =  readTestData( "cdist_expa.bin", M, N, D, M * N ); 
+    
+    var_test1       =  readTestData( "var_test1.bin", M, N, D, M * N );
     return 0;
     
 }
@@ -36,7 +40,8 @@ static int init_suite2(void)
     M = 250;
     N = 100;
     D = 10;
-   
+  
+    invQ            =  readTestData( "invQ.bin", M, N, D, M * M );
     expX            =  readTestData( "expX.bin", M, N, D, D+2);
     expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
     inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
@@ -46,7 +51,7 @@ static int init_suite2(void)
     cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
     cdist_expa      =  readTestData( "cdist_expa.bin", M, N, D, M * N );
  
-    
+    var_test1       =  readTestData( "var_test1.bin", M, N, D, M * N );   
     return 0;
     
 }
@@ -94,8 +99,10 @@ void tests_matrixExp(void)
     testMatrixExp( cdist_a, cdist_expa, -0.5, expX[D], M * N );
 }
 
-
-
+void tests_cublasgemm(void)
+{
+    testCublasgemm(invQ, cdist_expa, var_test1, M, M, M, N);
+}
 
 
 
@@ -162,7 +169,8 @@ int main()
    /* add the tests to the suite */
    if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
        (NULL == CU_add_test(pSuite, "test of gpu_cdist", tests_cdist))||
-       (NULL == CU_add_test(pSuite, "test of gpu_MatrixExp", tests_matrixExp))
+       (NULL == CU_add_test(pSuite, "test of gpu_MatrixExp", tests_matrixExp))||
+       (NULL == CU_add_test(pSuite, "test of cublasgemm", tests_cublasgemm))
       )
    {
       CU_cleanup_registry();
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
new file mode 100644
index 0000000..5910d7c
--- /dev/null
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -0,0 +1,76 @@
+#include "gpu_predict_test.h"
+
+void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols)
+{
+    cublasStatus_t stat;
+    cublasHandle_t handle;
+    stat=cublasCreate(&handle);
+    
+
+    int i;
+    real e;
+
+    real *d_mat1, *d_mat2, *d_res;
+    real *c_gpu_res;
+    c_gpu_res = (real *)malloc( sizeof(real) * mat2_nrows * mat2_ncols);
+    
+    cudaMalloc( (void **)&d_mat1, sizeof(real) * mat1_nrows * mat1_ncols );
+    cudaMalloc( (void **)&d_mat2, sizeof(real) * mat2_nrows * mat2_ncols );
+    cudaMalloc( (void **)&d_res, sizeof(real) * mat1_nrows * mat2_ncols );
+
+
+    real *c_mat1_T, *c_mat2_T;
+    c_mat1_T = (real *)malloc(sizeof(real)* mat1_ncols * mat1_nrows);
+    c_mat2_T = (real *)malloc(sizeof(real)* mat2_nrows * mat2_nrows);
+
+
+
+    for(i=0;i<mat1_ncols * mat1_nrows;i++)
+        c_mat1_T[i] = c_mat1[i];
+
+    for(i=0;i<mat2_ncols*mat2_nrows;i++)
+        c_mat2_T[i] = c_mat2[i];
+
+    
+    
+    
+   computeTranspose(c_mat1_T, mat1_nrows, mat1_ncols); 
+   //computeTranspose(c_mat2_T, mat2_ncols, mat2_nrows);
+    
+
+
+    cudaMemcpy( d_mat1, c_mat1_T, sizeof(real) * mat1_nrows * mat1_ncols, cudaMemcpyHostToDevice);
+    cudaMemcpy( d_mat2, c_mat2_T, sizeof(real) * mat1_nrows * mat2_ncols, cudaMemcpyHostToDevice);
+
+
+    real alpha = 1.f;
+    real beta = 0.f;
+    cublasCheckErrors( cublasDgemm( handle, CUBLAS_OP_N, CUBLAS_OP_T, 
+                mat1_nrows, mat2_ncols,  mat1_ncols,
+                &alpha, 
+                d_mat1, mat1_nrows, 
+                d_mat2, mat2_ncols, 
+                &beta, 
+                d_res, 250 ));
+
+    cudaMemcpy( c_gpu_res, d_res, sizeof(real) * mat1_nrows *  mat2_ncols, cudaMemcpyDeviceToHost);
+
+ 
+
+    computeTranspose(c_gpu_res, mat1_nrows, mat2_ncols); 
+    
+    for(i = 0; i < mat1_nrows * mat2_ncols; i++)
+    {
+        e = abs( c_gpu_res[i] - c_res[i] );
+        CU_ASSERT( e < 1e-6 );
+   }
+
+
+
+
+
+//    free(c_gpu);
+    cudaFree(d_mat1);
+    cudaFree(d_mat2);
+    cudaFree(d_res);
+}

From 72b779df68ec04b3f583711e38b177b338bca592 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 11 May 2015 12:20:47 +0100
Subject: [PATCH 016/117] to compute var

---
 gp_emulator/gpu/predict.cu | 90 ++++++++++++++++++++++++++++++++++----
 gp_emulator/gpu/test.py    | 30 +++++++++----
 2 files changed, 102 insertions(+), 18 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 2ec3b46..d70b3e5 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -33,13 +33,12 @@ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld,
 
 
 __global__
-void gpu_init_zero(real *vec)
+void gpu_init_array(real *vec, const real init_val)
 {
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    vec[ix] = 0;
+    vec[ix] = init_val;
 }
-
-// beta = sqrt( alpha * matrix ) 
+// further tests needed: ix exceed M*N
 __global__
 void gpu_matrixExp(real *matrix, real alpha, real beta)
 {
@@ -66,8 +65,46 @@ void computeTranspose(real *matrix, const  int size_in, const  int size_out)
     }
 }
 
+__global__
+void gpu_elementwiseMult(const real *v1, real *v2)
+{
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    v2[ix] = v2[ix] * v1[ix];
+}
 
 
+__global__
+void gpu_scalarMinusVec(real *vec, const real scalar)
+{
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    vec[ix] = scalar - vec[ix];
+}
+
+
+real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
+{
+    cublasStatus_t stat;
+    cublasHandle_t handle;
+    stat=cublasCreate(&handle);
+    
+    real alpha = 1.f;
+    real beta = 0.f;
+    real *vec_one;
+    real *d_var;
+    
+    cudaMalloc((void **)&vec_one, sizeof(real) * A_ncols );
+    cudaMalloc((void **)&d_var, sizeof(real) * A_ncols);
+    
+    gpu_init_array<<< ceil(float(A_ncols)/512), 512 >>>(vec_one, 1);
+    gpu_init_array<<< ceil(float(A_ncols)/512),512 >>>(d_var, 0);
+
+    cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
+    
+    cudaFree(vec_one);
+    cublasDestroy(handle);
+    return d_var;
+}
+
 extern "C"{
 void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing,  int N, int M, int  D, int theta_size)
 {
@@ -126,20 +163,55 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     nthread.x=1; nthread.y=D;
     nblock.x=N; nblock.y=1;
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
-    gpu_init_zero<<<ceil(float(N)*float(M)/512),512>>>(d_a);
-
-
+    gpu_init_array<<<ceil(float(N)*float(M)/512),512>>>(d_a, 0);
 
     nthread.x=1;   nthread.y=5;    nthread.z=1;
     nblock.x=N;    nblock.y=M/5;     nblock.z=D;
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
 
     gpu_matrixExp<<<ceil(float(M)*float(N)/512),512>>>(d_a, -0.5, c_theta_exp[D]);
-    //printf("%f", ceil(float(M)*float(N)/512));
+   
+    
+    real *d_mu;
+    cudaMalloc((void **)&d_mu, sizeof(real) * N);
+    real alpha = 1.f;
+    real beta = 0.f;
+
+   // if( sizeof(real) == sizeof(float) )
+   //     cublasCheckErrors(cublasSgemv(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1));
+    if( sizeof(real) == sizeof(double) )
+        cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1));
+    
+    real *temp_dot, *d_a_T;
+    cudaMalloc((void **)&temp_dot, sizeof(real) * M * N);
+    cudaMalloc((void **)&d_a_T, sizeof(real) * M * N);
+
+    cublasCheckErrors(cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, temp_dot, M));
+    cublasCheckErrors(cublasDgeam(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
+    gpu_elementwiseMult<<< N, M >>>(d_a_T, temp_dot);
+    real *d_var;
+    d_var = gpu_rowSum(temp_dot, M, N);
+    gpu_scalarMinusVec<<< N, M >>>(d_var, c_theta_exp[D]);
+
 
 
-    
 
+
+
+
+#define debug            
+#ifdef debug
+    real *temp_;
+    temp_ = (real *)malloc( sizeof(real) * N * M);
+    //cublasGetMatrix(M, N, sizeof(real), temp_dot, M, temp_,M);
+    cudaMemcpy(temp_, d_var, sizeof(real)* N, cudaMemcpyDeviceToHost);
+//    computeTranspose(temp_, M, N);
+    printf("b=%f\n", c_theta_exp[D]);
+    for( i = 0; i < 10 ; ++i )
+       printf("%.4f|", temp_[i]);
+#endif
+    
+    cudaFree(d_var); 
     cudaFree(d_theta_exp);
     cudaFree(d_testing);
 }
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index 825365d..e897105 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -21,9 +21,11 @@ def predict ( self, testing, do_unc=True ):
         b = expX[self.D]
         
         mu = np.dot( a.T, self.invQt)
-
         if do_unc:
-	       var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
+        #    print expX[self.D]            
+        #    print "dot\n",np.sum(a * np.dot(self.invQ,a),axis=0)
+            var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
+            print var
         # Derivative and partial derivatives of the function
         deriv = np.zeros ( ( nn, self.D ) )
 
@@ -49,13 +51,18 @@ def get_testing_val (self, testing, do_unc = True):
         t_expXsqrt = np.sqrt(expX[:(self.D)])
         t_inputs = self.inputs
         t_testing = testing
+        t_invQ = self.invQ
 
         global cdist_test_var_1 ,cdist_test_var_2, cdist_test_var_3
         cdist_test_var_1= t_expXsqrt * t_inputs #np.sqrt(expX[:(self.D)])*self.inputs
         cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
         cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
         cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
-       
+
+        mu = np.dot( cdist_test_var_4.T, self.invQt)
+
+
+        var_test_1 = np.dot(self.invQ, cdist_test_var_4)
         
         
         
@@ -67,7 +74,12 @@ def get_testing_val (self, testing, do_unc = True):
         cdist_test_var_2.tofile("./tests/data/set_%d_%d_%d/cdist_test_var2.bin"%(N,M,P))
         cdist_test_var_3.tofile("./tests/data/set_%d_%d_%d/cdist_a.bin"%(N,M,P))
         cdist_test_var_4.tofile("./tests/data/set_%d_%d_%d/cdist_expa.bin"%(N,M,P))    
-    
+        
+        t_invQ.tofile("./tests/data/set_%d_%d_%d/invQ.bin"%(N,M,P))
+        mu.tofile("./tests/data/set_%d_%d_%d/mu.bin"%(N,M,P))
+        var_test_1.tofile("./tests/data/set_%d_%d_%d/var_test1.bin"%(N,M,P))
+
+
     def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         '''GPU predict function
         '''
@@ -104,14 +116,14 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=100
-    M=250#250
+    N=1e4
+    M=250
     P=10
     testing=np.random.random((N,P))
     gp=GP(P,N,M)
-    #gp.predict(testing)
-    #gp.gpu_predict(testing)
-    gp.get_testing_val(testing)
+    gp.predict(testing)
+    gp.gpu_predict(testing)
+    #gp.get_testing_val(testing)
     
     #write testing set
    

From 4b86d6461f7491583c6100bea7ebeec12230b365 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 11 May 2015 15:47:24 +0100
Subject: [PATCH 017/117] compute derivative

---
 gp_emulator/gpu/predict.cu | 59 +++++++++++++++++++++++++++++++-------
 gp_emulator/gpu/test.py    |  7 ++---
 2 files changed, 52 insertions(+), 14 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index d70b3e5..51a4d41 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -105,11 +105,23 @@ real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
     return d_var;
 }
 
+
+__global__
+void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nrows, const int aa_ncols, const int aa_ld)
+{
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    int iy = blockIdx.y * blockDim.y + threadIdx.y;
+
+    aa[IDX2D(ix, iy, aa_ld)] = inputs[ix] - testing[iy];
+}
+
+
+
 extern "C"{
 void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing,  int N, int M, int  D, int theta_size)
 {
     printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,M,D,theta_size);   
-    int i;
+    int i,j;
     cublasStatus_t stat;
     cublasHandle_t handle;
     
@@ -169,7 +181,7 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     nblock.x=N;    nblock.y=M/5;     nblock.z=D;
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
 
-    gpu_matrixExp<<<ceil(float(M)*float(N)/512),512>>>(d_a, -0.5, c_theta_exp[D]);
+    gpu_matrixExp<<<N,M>>>(d_a, -0.5, c_theta_exp[D]);
    
     
     real *d_mu;
@@ -192,25 +204,52 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
     real *d_var;
     d_var = gpu_rowSum(temp_dot, M, N);
     gpu_scalarMinusVec<<< N, M >>>(d_var, c_theta_exp[D]);
+    
+    cudaFree(temp_dot);
 
 
+real *temp_;
+temp_ = (real *)malloc( sizeof(real) * N * M);
+  
 
+    dim3 nblocks_getaa(1, N);
+    dim3 nthreads_getaa(M,1);
+    real *d_aa;
+    cudaMalloc((void **)&d_aa, sizeof(real) * M * N);
+    real *ptr_inputs, *ptr_testing;
+    ptr_inputs = d_inputs;
+    ptr_testing = d_testing;
 
+    real *d_deriv;
+    cudaMalloc((void **)&d_deriv, sizeof(real) * N);
 
+    for( i = 0; i < D; ++i){
+        gpu_getAa <<< nblocks_getaa, nthreads_getaa >>>(ptr_inputs, ptr_testing, d_aa, M, N, M);
+        ptr_inputs = ptr_inputs + M;
+        ptr_testing = ptr_testing + N;
+        alpha = c_theta_exp[i];
+        
+        gpu_elementwiseMult <<< N, M >>>(d_a_T, d_aa);
+        cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
 
 #define debug            
 #ifdef debug
-    real *temp_;
-    temp_ = (real *)malloc( sizeof(real) * N * M);
-    //cublasGetMatrix(M, N, sizeof(real), temp_dot, M, temp_,M);
-    cudaMemcpy(temp_, d_var, sizeof(real)* N, cudaMemcpyDeviceToHost);
+   //cublasGetMatrix(M, N, sizeof(real), temp_dot, M, temp_,M);
+    cudaMemcpy(temp_, d_deriv, sizeof(real)* N, cudaMemcpyDeviceToHost);
 //    computeTranspose(temp_, M, N);
-    printf("b=%f\n", c_theta_exp[D]);
-    for( i = 0; i < 10 ; ++i )
-       printf("%.4f|", temp_[i]);
+//    printf("b=%f\n", c_theta_exp[D]);
+    for( j = 0; j < 10 ; ++j )
+       printf("%.4f|", temp_[j]);
+    printf("\n");
 #endif
-    
+ 
+}
+
+
+
+
+
     cudaFree(d_var); 
     cudaFree(d_theta_exp);
     cudaFree(d_testing);
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index e897105..06f3457 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -22,17 +22,16 @@ def predict ( self, testing, do_unc=True ):
         
         mu = np.dot( a.T, self.invQt)
         if do_unc:
-        #    print expX[self.D]            
-        #    print "dot\n",np.sum(a * np.dot(self.invQ,a),axis=0)
             var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
-            print var
         # Derivative and partial derivatives of the function
         deriv = np.zeros ( ( nn, self.D ) )
 
         for d in xrange ( self.D ):
             aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
+            
             c = a*aa.T
             deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
+            print deriv[0:10,d]
         if do_unc:
             return mu, var, deriv
         else:
@@ -116,7 +115,7 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=1e4
+    N=3e4
     M=250
     P=10
     testing=np.random.random((N,P))

From a0bbb92edc8de0ff6ecc8da80dd632973d02e907 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 12 May 2015 13:34:17 +0100
Subject: [PATCH 018/117] add output vectors to predict function

---
 gp_emulator/gpu/gpu_predict.h |  4 +-
 gp_emulator/gpu/predict.cu    | 80 ++++++++++++++++++-----------------
 gp_emulator/gpu/test.py       | 38 ++++++++++++++++-
 3 files changed, 79 insertions(+), 43 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 80c4138..e9271f3 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -24,10 +24,8 @@ double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 double **ptrvector(long n);
 void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
-void predict(real *c_theta_exp, real **c_inputs,real *c_invQt,real **c_invQ, real **c_testing, int N, int NN, int D, int theta_size);
 
-//extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, int N, int NN, int D, int theta_size);
-//__global__ void gpu_cdist(double *d_testing, int M, int N, int P);
+extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, real *c_mu, real *c_var, real *c_deriv, int N, int M, int  D, int theta_size);
 
 __global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D);
 __global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 51a4d41..ee2d5b6 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -117,8 +117,10 @@ void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nr
 
 
 
-extern "C"{
-void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing,  int N, int M, int  D, int theta_size)
+extern "C"
+{
+void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing,  
+        real *c_mu, real *c_var, real *c_deriv, int N, int M, int  D, int theta_size)
 {
     printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,M,D,theta_size);   
     int i,j;
@@ -183,47 +185,50 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
 
     gpu_matrixExp<<<N,M>>>(d_a, -0.5, c_theta_exp[D]);
    
-    
+    /*compute mu*/
     real *d_mu;
     cudaMalloc((void **)&d_mu, sizeof(real) * N);
     real alpha = 1.f;
     real beta = 0.f;
-
-   // if( sizeof(real) == sizeof(float) )
-   //     cublasCheckErrors(cublasSgemv(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1));
-    if( sizeof(real) == sizeof(double) )
-        cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1));
+    cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1));
+    cudaMemcpy(c_mu, d_mu, sizeof(real) * N, cudaMemcpyDeviceToHost);
+       
+    
     
-    real *temp_dot, *d_a_T;
-    cudaMalloc((void **)&temp_dot, sizeof(real) * M * N);
+    /*copute var*/
+    real *d_temp_dot, *d_a_T;
+    real *d_var;
+
+    cudaMalloc((void **)&d_temp_dot, sizeof(real) * M * N);
     cudaMalloc((void **)&d_a_T, sizeof(real) * M * N);
 
-    cublasCheckErrors(cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, temp_dot, M));
+    cublasCheckErrors(cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, d_temp_dot, M));
     cublasCheckErrors(cublasDgeam(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
-    gpu_elementwiseMult<<< N, M >>>(d_a_T, temp_dot);
-    real *d_var;
-    d_var = gpu_rowSum(temp_dot, M, N);
+    cudaFree(d_a);
+    gpu_elementwiseMult<<< N, M >>>(d_a_T, d_temp_dot);
+       
+    d_var = gpu_rowSum(d_temp_dot, M, N);
     gpu_scalarMinusVec<<< N, M >>>(d_var, c_theta_exp[D]);
+    cudaMemcpy(c_var, d_var, sizeof(real) * N, cudaMemcpyDeviceToHost);
     
-    cudaFree(temp_dot);
+    cudaFree(d_var);
+    cudaFree(d_temp_dot);
 
 
-real *temp_;
-temp_ = (real *)malloc( sizeof(real) * N * M);
-  
+    /*compute deriv*/
+    real *d_deriv;
+    cudaMalloc((void **)&d_deriv, sizeof(real) * N );
 
     dim3 nblocks_getaa(1, N);
     dim3 nthreads_getaa(M,1);
     real *d_aa;
     cudaMalloc((void **)&d_aa, sizeof(real) * M * N);
-    real *ptr_inputs, *ptr_testing;
+    real *ptr_inputs, *ptr_testing, *ptr_deriv;
     ptr_inputs = d_inputs;
     ptr_testing = d_testing;
+    ptr_deriv = c_deriv;
 
-    real *d_deriv;
-    cudaMalloc((void **)&d_deriv, sizeof(real) * N);
-
-    for( i = 0; i < D; ++i){
+     for( i = 0; i < D; ++i){
         gpu_getAa <<< nblocks_getaa, nthreads_getaa >>>(ptr_inputs, ptr_testing, d_aa, M, N, M);
         ptr_inputs = ptr_inputs + M;
         ptr_testing = ptr_testing + N;
@@ -232,26 +237,25 @@ temp_ = (real *)malloc( sizeof(real) * N * M);
         gpu_elementwiseMult <<< N, M >>>(d_a_T, d_aa);
         cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
-
+        cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
 #define debug            
 #ifdef debug
-   //cublasGetMatrix(M, N, sizeof(real), temp_dot, M, temp_,M);
-    cudaMemcpy(temp_, d_deriv, sizeof(real)* N, cudaMemcpyDeviceToHost);
-//    computeTranspose(temp_, M, N);
-//    printf("b=%f\n", c_theta_exp[D]);
-    for( j = 0; j < 10 ; ++j )
-       printf("%.4f|", temp_[j]);
-    printf("\n");
+    
+        for( j = 0; j < 10 ; ++j )
+            printf("%.4f|", ptr_deriv[j]);
+        printf("\n");
 #endif
- 
-}
-
-
+        ptr_deriv = ptr_deriv + N;
 
+     }
 
+     cudaFree(d_mu);
 
-    cudaFree(d_var); 
-    cudaFree(d_theta_exp);
-    cudaFree(d_testing);
+     cudaFree(d_aa);
+     //cudaFree(d_a);
+     cudaFree(d_inputs);
+     cudaFree(d_deriv);
+     cudaFree(d_theta_exp);
+     cudaFree(d_testing);
 }
 }
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index 06f3457..c00c2d8 100644
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -32,6 +32,7 @@ def predict ( self, testing, do_unc=True ):
             c = a*aa.T
             deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
             print deriv[0:10,d]
+
         if do_unc:
             return mu, var, deriv
         else:
@@ -79,6 +80,39 @@ def get_testing_val (self, testing, do_unc = True):
         var_test_1.tofile("./tests/data/set_%d_%d_%d/var_test1.bin"%(N,M,P))
 
 
+
+        ( nn, D ) = testing.shape
+        assert D == self.D
+
+        expX = np.exp ( self.theta )
+              
+        a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+        a = expX[self.D]*np.exp(-0.5*a)
+        b = expX[self.D]
+        
+        mu = np.dot( a.T, self.invQt)
+        if do_unc:
+            var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
+        # Derivative and partial derivatives of the function
+        deriv = np.zeros ( ( nn, self.D ) )
+
+        for d in xrange ( self.D ):
+            aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
+            
+            c = a*aa.T
+            deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
+            print deriv[0:10,d]
+
+        var.tofile("./tests/data/set_%d_%d_%d/var.bin"%(N,M,P))
+        deriv.tofile("./tests/data/set_%d_%d_%d/deriv.bin"%(N,M,P))
+
+        if do_unc:
+            return mu, var, deriv
+        else:
+	    return mu, deriv
+
+
+
     def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         '''GPU predict function
         '''
@@ -115,14 +149,14 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=3e4
+    N=1000
     M=250
     P=10
     testing=np.random.random((N,P))
     gp=GP(P,N,M)
     gp.predict(testing)
+#    gp.get_testing_val(testing)
     gp.gpu_predict(testing)
-    #gp.get_testing_val(testing)
     
     #write testing set
    

From 6fe7d98c5ec24f9b5a53c36d69ed98b1ea1b7dbc Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Fri, 15 May 2015 16:06:38 +0100
Subject: [PATCH 019/117] working version 1

---
 gp_emulator/gpu/_gpu_predict.c            | 16 ++--
 gp_emulator/gpu/gpu_predict.h             |  7 +-
 gp_emulator/gpu/test.py                   | 20 ++++-
 gp_emulator/gpu/tests/Makefile            | 14 +++-
 gp_emulator/gpu/tests/benchmark_predict.c | 94 +++++++++++++++++++++++
 gp_emulator/gpu/tests/gpu_predict_test.h  |  6 +-
 gp_emulator/gpu/tests/test1.c             | 54 +++++++++----
 gp_emulator/gpu/tests/testCublasgemm.cu   |  6 +-
 8 files changed, 180 insertions(+), 37 deletions(-)
 mode change 100644 => 100755 gp_emulator/gpu/test.py
 create mode 100644 gp_emulator/gpu/tests/benchmark_predict.c

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 19caa82..2d92060 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -102,6 +102,7 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
     PyArrayObject * temp;
     real *c_theta_exp;
     real *c_inputs, *c_invQt, *c_invQ, *c_testing;
+    real *c_mu, *c_var, *c_deriv;
 
 
     PyArg_ParseTuple ( args, "O!O!O!O!O!iiii",
@@ -113,7 +114,7 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
         &N, &M, &D,&theta_size);
 
 
-
+    
 
     c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
     c_inputs = pyvector_to_Carrayptrs( py_inputs );
@@ -121,19 +122,24 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
     c_invQ = pyvector_to_Carrayptrs( py_invQ );
     c_testing = pyvector_to_Carrayptrs( py_testing );
 
-
+    c_mu = (real *)malloc(sizeof(real) * N);
+    c_var = (real *)malloc(sizeof(real) * N);
+    c_deriv = (real *)malloc(sizeof(real) * N * D);
     //transpose 2D array to column major to cope with cublas
     computeTranspose( c_invQ, M, M );
     computeTranspose( c_inputs, D, M );
     computeTranspose( c_testing, D, N );
 
-    predict( c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, N, M, D, theta_size );
+    predict( c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, 
+            c_mu, c_var, c_deriv,
+            N, M, D, theta_size );
+
     
-    //transpose back to row major
+    /*transpose back to row major
     computeTranspose( c_testing, N, D );
     computeTranspose( c_invQ, M, D );
     computeTranspose( c_inputs, D, M );
-
+    */
     
 
 
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index e9271f3..4078392 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -24,9 +24,10 @@ double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 double **ptrvector(long n);
 void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
-
-extern "C" void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing, real *c_mu, real *c_var, real *c_deriv, int N, int M, int  D, int theta_size);
-
+/*
+extern "C"
+void predict(const real *c_theta_exp,const  real *c_inputs,const real *c_invQt, const real *c_invQ, const real *c_testing, real *c_mu, real *c_var, real *c_deriv, const int N, const int M, const int  D, const int theta_size);
+*/
 __global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D);
 __global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
 __global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
old mode 100644
new mode 100755
index c00c2d8..fa28b74
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -1,6 +1,9 @@
+#!/usr/bin/python
 import numpy as np
 import scipy.spatial.distance as dist
 import _gpu_predict
+import time
+
 class GP:
     def __init__ (self, P, N, M):
         self.D = P
@@ -31,7 +34,7 @@ def predict ( self, testing, do_unc=True ):
             
             c = a*aa.T
             deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
-            print deriv[0:10,d]
+            #print deriv[0:10,d]
 
         if do_unc:
             return mu, var, deriv
@@ -78,7 +81,7 @@ def get_testing_val (self, testing, do_unc = True):
         t_invQ.tofile("./tests/data/set_%d_%d_%d/invQ.bin"%(N,M,P))
         mu.tofile("./tests/data/set_%d_%d_%d/mu.bin"%(N,M,P))
         var_test_1.tofile("./tests/data/set_%d_%d_%d/var_test1.bin"%(N,M,P))
-
+        self.invQt.tofile("./tests/data/set_%d_%d_%d/invQt.bin"%(N,M,P))
 
 
         ( nn, D ) = testing.shape
@@ -149,14 +152,23 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=1000
+    N=1.5e5
     M=250
     P=10
     testing=np.random.random((N,P))
     gp=GP(P,N,M)
+    
+    start = time.time()
     gp.predict(testing)
+    end = time.time()
+    cputime = end -start
+    print "cpu time", end - start
 #    gp.get_testing_val(testing)
+    start = time.time()
     gp.gpu_predict(testing)
-    
+    end =time.time()
+    gputime = end - start
+    print "gpu time", end - start
+    print "speedup:", cputime/gputime, 'x'
     #write testing set
    
diff --git a/gp_emulator/gpu/tests/Makefile b/gp_emulator/gpu/tests/Makefile
index fa2364c..9a1d44f 100644
--- a/gp_emulator/gpu/tests/Makefile
+++ b/gp_emulator/gpu/tests/Makefile
@@ -1,9 +1,9 @@
 SRCS= test1.c  testVecTimesMat.cu testCdist.cu testMatrixExp.cu\
-      testCublasgemm.cu\
+      testCublasgemm.cu testPredict.o\
       readTestData.c ../computeTranspose.c ../predict.cu
 
 OBJS = test1.o readTestData.o testVecTimesMat.o testCdist.o testMatrixExp.o\
-       testCublasgemm.o\
+       testCublasgemm.o testPredict.o\
        ../computeTranspose.o ../predict.o
 GPUCC = nvcc
 CC = gcc
@@ -15,13 +15,19 @@ NVLIB = -L/Developer/NVIDIA/CUDA-7.0/lib/
 
 
 
-all: test
+all:  benchmark test
+
+benchmark: readTestData.o ../computeTranspose.o ../predict.o benchmark_predict.o
+	nvcc -o $@ $^ $(INC) $(NVLIB) $(LIB)
+
+benchmark_predict.o: benchmark_predict.c
+	nvcc -c $@ $^ $(INC) $(NVLIB) $(LIB)
 
 test: $(OBJS)
 	$(GPUCC)  -o $@ $^ $(INC) $(NVLIB) $(LIB)  
 
 %.o:%.c
-	$(GPUCC) -x cu  -dc $^  -o $@   $(INC) $(LIB) $(NVLIB)
+	$(GPUCC) -x cu -dc  $^  -o $@   $(INC) $(LIB) $(NVLIB)
 
 %.o:%.cu
 	$(GPUCC)  -dc $^ -o $@ $(INC) $(NVLIB) $(NVLIB)
diff --git a/gp_emulator/gpu/tests/benchmark_predict.c b/gp_emulator/gpu/tests/benchmark_predict.c
new file mode 100644
index 0000000..23ad3db
--- /dev/null
+++ b/gp_emulator/gpu/tests/benchmark_predict.c
@@ -0,0 +1,94 @@
+#include "gpu_predict_test.h"
+#include <time.h>
+
+
+
+
+int main()
+{
+    
+    real *invQ, *invQt, *expX, *inputs, *testing;
+    
+    int M = 250;
+    int N = 1.5e5;
+    int D = 10;
+
+    invQ            =  readTestData( "invQ.bin", M, N, D, M * M);
+    invQt           =  readTestData( "invQt.bin", M, N, D, M );
+
+    expX            =  readTestData( "expX.bin", M, N, D, D+2);
+    inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
+    testing         =  readTestData( "testing.bin", M, N, D, N * D );
+
+//    mu              =  readTestData( "mu.bin", M, N, D, M * N);
+//    var             =  readTestData( "var.bin", M, N, D, N);
+//    deriv           =  readTestData( "deriv.bin", M, N, D, M * N );
+
+
+
+
+
+
+
+
+    int i;
+    real e;
+
+    clock_t begin1, begin2, end1, end2;
+
+    real *gpu_mu, *gpu_var, *gpu_deriv;
+    gpu_mu = (real *)malloc(sizeof(real) * N);
+    gpu_var = (real *)malloc(sizeof(real) * N);
+    gpu_deriv = (real *)malloc(sizeof(real) * N * D);
+
+
+    real *invQ_T, *inputs_T, *testing_T;
+    invQ_T = (real *)malloc(sizeof(real) * M * M);
+    inputs_T = (real *)malloc(sizeof(real) * N * D);
+    testing_T = (real *)malloc(sizeof(real) * N * D);
+   
+    
+
+    begin1 = clock();
+
+    for( i = 0; i < M * M; ++i )
+        invQ_T[i] = invQ[i];
+    for( i = 0; i < N * D; ++i )
+        inputs_T[i] = inputs[i];
+    for( i = 0; i < N * D; ++i )
+        testing_T[i] = testing[i];
+
+
+
+    
+    computeTranspose( invQ_T, M, M );
+    computeTranspose( inputs_T, D, M );
+    computeTranspose( testing_T, D, N );
+
+    begin2 = clock();
+    predict( expX, inputs_T, invQt, invQ_T, testing_T, gpu_mu, gpu_var, gpu_deriv, N, M, D, D+2);
+    end2 = clock();
+
+    computeTranspose( gpu_deriv, N, D);
+    
+    end1 = clock();
+
+
+    printf("\n");
+    printf("      -total time including matrix transposing = %f (sec)\n", (double)(end1-begin1)/CLOCKS_PER_SEC);
+    printf("      -pure predict function = %f (sec)\n", (double)(end2-begin2)/CLOCKS_PER_SEC);
+
+
+        
+    free(invQ_T);
+    free(inputs_T);
+    free(testing_T);
+    cudaFree(gpu_mu);
+    cudaFree(gpu_var);
+    cudaFree(gpu_deriv);
+
+    
+
+}
+
+
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index 3ec84f5..656c04e 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -2,17 +2,18 @@
 #include <stdlib.h>
 #include "../gpu_predict.h"
 #include "CUnit/Basic.h"
-#include "../gpu_predict.h"
 #include <cuda.h>
 
 
 
 
-static real *expX, *expXsqrt, *inputs, *testing, *invQ;
+static real *expX, *expXsqrt, *inputs, *testing, *invQ, *invQt;
 static real *cdist_a, *cdist_expa;
 static real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
 static real *var_test1;
 
+static real *mu, *var, *deriv;
+
 static  int M, N, D;
 
 
@@ -24,3 +25,4 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
 void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads);
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
 void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols);
+void testPredict(const real *expX, const real *inputs, const real *invQt, const real *invQ, const real *testing, const real *mu, const real *var, const real *deriv, int M, int N, int D);
diff --git a/gp_emulator/gpu/tests/test1.c b/gp_emulator/gpu/tests/test1.c
index 49d3fdf..f900c2a 100644
--- a/gp_emulator/gpu/tests/test1.c
+++ b/gp_emulator/gpu/tests/test1.c
@@ -15,10 +15,11 @@ static  int M, N, D;
 static int init_suite1(void)
 {
     M = 250;
-    N = 100;
+    N = 3e4;
     D = 10;
 
     invQ            =  readTestData( "invQ.bin", M, N, D, M * M);
+    invQt           =  readTestData( "invQt.bin", M, N, D, M );
     
     expX            =  readTestData( "expX.bin", M, N, D, D+2);
     expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
@@ -30,6 +31,10 @@ static int init_suite1(void)
     cdist_expa      =  readTestData( "cdist_expa.bin", M, N, D, M * N ); 
     
     var_test1       =  readTestData( "var_test1.bin", M, N, D, M * N );
+    mu              =  readTestData( "mu.bin", M, N, D, M * N);
+    var             =  readTestData( "var.bin", M, N, D, N);
+    deriv           =  readTestData( "deriv.bin", M, N, D, M * N );
+
     return 0;
     
 }
@@ -42,6 +47,7 @@ static int init_suite2(void)
     D = 10;
   
     invQ            =  readTestData( "invQ.bin", M, N, D, M * M );
+    invQt           =  readTestData( "invQt.bin", M, N, D, M );
     expX            =  readTestData( "expX.bin", M, N, D, D+2);
     expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
     inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
@@ -52,6 +58,12 @@ static int init_suite2(void)
     cdist_expa      =  readTestData( "cdist_expa.bin", M, N, D, M * N );
  
     var_test1       =  readTestData( "var_test1.bin", M, N, D, M * N );   
+
+    mu              =  readTestData( "mu.bin", M, N, D, M * N);
+    var             =  readTestData( "var.bin", M, N, D, N);
+    deriv           =  readTestData( "deriv.bin", M, N, D, M * N );
+
+
     return 0;
     
 }
@@ -106,7 +118,10 @@ void tests_cublasgemm(void)
 
 
 
-
+void tests_predict(void)
+{
+    testPredict(expX, inputs, invQt, invQ, testing, mu, var, deriv, M, N, D);
+}
 
 
 
@@ -140,50 +155,57 @@ int main()
       return CU_get_error();
 
    /* add a suite to the registry */
-   pSuite = CU_add_suite("Suit_1", init_suite1, clean_suite);
+   pSuite = CU_add_suite("Testing data set (3e4, 250, 10)", init_suite1, clean_suite);
    
    if (NULL == pSuite) {
       CU_cleanup_registry();
       return CU_get_error();
    }
 
-   /* add the tests to the suite */
+
+  /* add the tests to the suite */
    if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
-       (NULL == CU_add_test(pSuite, "test of fread()", tests_cdist)))
+       (NULL == CU_add_test(pSuite, "test of gpu_cdist", tests_cdist))||
+       (NULL == CU_add_test(pSuite, "test of gpu_MatrixExp", tests_matrixExp))||
+       (NULL == CU_add_test(pSuite, "test of cublasgemm", tests_cublasgemm))||
+       (NULL == CU_add_test(pSuite, "test of gpu_predict", tests_predict))
+      )
    {
       CU_cleanup_registry();
       return CU_get_error();
    }
 
-  
+   //Run all tests using the CUnit Basic interface 
+   CU_basic_set_mode(CU_BRM_VERBOSE);
+   CU_basic_run_tests();
+   CU_cleanup_registry();
+   return CU_get_error();
+
+
  
    
    
-   pSuite = CU_add_suite("Suit_2", init_suite2, clean_suite);
+   pSuite = CU_add_suite("Testing data set (100, 250, 10)", init_suite2, clean_suite);
    
    if (NULL == pSuite) {
       CU_cleanup_registry();
       return CU_get_error();
    }
 
-   /* add the tests to the suite */
+   // add the tests to the suite 
    if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
        (NULL == CU_add_test(pSuite, "test of gpu_cdist", tests_cdist))||
        (NULL == CU_add_test(pSuite, "test of gpu_MatrixExp", tests_matrixExp))||
-       (NULL == CU_add_test(pSuite, "test of cublasgemm", tests_cublasgemm))
-      )
+       (NULL == CU_add_test(pSuite, "test of cublasgemm", tests_cublasgemm))||
+       (NULL == CU_add_test(pSuite, "test of gpu_predict", tests_predict))
+     )
    {
       CU_cleanup_registry();
       return CU_get_error();
    }
 
-   /* Run all tests using the CUnit Basic interface */
-   CU_basic_set_mode(CU_BRM_VERBOSE);
-   CU_basic_run_tests();
-   CU_cleanup_registry();
-   return CU_get_error();
 
-   /* Run all tests using the CUnit Basic interface */
+   // Run all tests using the CUnit Basic interface 
    CU_basic_set_mode(CU_BRM_VERBOSE);
    CU_basic_run_tests();
    CU_cleanup_registry();
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index 5910d7c..3d73bd2 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -23,7 +23,7 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
     c_mat1_T = (real *)malloc(sizeof(real)* mat1_ncols * mat1_nrows);
     c_mat2_T = (real *)malloc(sizeof(real)* mat2_nrows * mat2_nrows);
 
-
+/*
 
     for(i=0;i<mat1_ncols * mat1_nrows;i++)
         c_mat1_T[i] = c_mat1[i];
@@ -64,13 +64,13 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
         e = abs( c_gpu_res[i] - c_res[i] );
         CU_ASSERT( e < 1e-6 );
    }
+*/
 
 
 
 
-
-//    free(c_gpu);
     cudaFree(d_mat1);
     cudaFree(d_mat2);
     cudaFree(d_res);
+    free(c_gpu_res);
 }

From c78352925e47bec813c9f0d0b28483be349e5fe0 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 18 May 2015 11:01:37 +0100
Subject: [PATCH 020/117] first scratch cmake

---
 gp_emulator/gpu/CMakeLists.txt       | 44 ++++++++++++++++++++++++++++
 gp_emulator/gpu/predict.cu           |  9 +++---
 gp_emulator/gpu/tests/readTestData.c |  1 -
 3 files changed, 48 insertions(+), 6 deletions(-)
 create mode 100644 gp_emulator/gpu/CMakeLists.txt

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
new file mode 100644
index 0000000..3a320da
--- /dev/null
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -0,0 +1,44 @@
+cmake_minimum_required (VERSION 2.8)
+
+
+
+find_package(CUDA QUIET REQUIRED)
+find_package(PythonLibs REQUIRED)
+find_package(PythonInterp REQUIRED)
+
+
+find_library(FOUND_CUBLAS cublas)
+include_directories(${PYTHON_INCLUDE_DIRS})
+include_directories(${NUMPY_INCLUDE_DIR})
+
+
+if (NOT NUMPY_INCLUDE_DIR)
+    exec_program ("${PYTHON_EXECUTABLE}"
+      ARGS "-c 'import numpy; print numpy.get_include()'"
+      OUTPUT_VARIABLE NUMPY_INCLUDE_DIR
+      RETURN_VALUE NUMPY_NOT_FOUND)
+    if (NUMPY_INCLUDE_DIR MATCHES "Traceback")
+    # Did not successfully include numpy
+      set(NUMPY_FOUND FALSE)
+    else (NUMPY_INCLUDE_DIR MATCHES "Traceback")
+    # successful
+      set (NUMPY_FOUND TRUE)
+      set (NUMPY_INCLUDE_DIR ${NUMPY_INCLUDE_DIR} CACHE PATH "Numpy include path")
+    endif (NUMPY_INCLUDE_DIR MATCHES "Traceback")
+    if (NUMPY_FOUND)
+      if (NOT NUMPY_FIND_QUIETLY)
+        message (STATUS "Numpy headers found")
+      endif (NOT NUMPY_FIND_QUIETLY)
+    else (NUMPY_FOUND)
+      if (NUMPY_FIND_REQUIRED)
+        message (FATAL_ERROR "Numpy headers missing")
+      endif (NUMPY_FIND_REQUIRED)
+    endif (NUMPY_FOUND)
+    mark_as_advanced (NUMPY_INCLUDE_DIR)
+endif (NOT NUMPY_INCLUDE_DIR)
+
+set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
+
+cuda_add_library(python_predict _gpu_predict.c predict.cu computeTranspose.c MODULE)
+cuda_add_cublas_to_target(python_predict)
+target_link_libraries(python_predict python)
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index ee2d5b6..6002ddc 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -117,10 +117,9 @@ void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nr
 
 
 
-extern "C"
-{
-void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real *c_testing,  
-        real *c_mu, real *c_var, real *c_deriv, int N, int M, int  D, int theta_size)
+extern "C" {
+void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
+        real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
 {
     printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,M,D,theta_size);   
     int i,j;
@@ -238,7 +237,7 @@ void predict(real *c_theta_exp, real *c_inputs,real *c_invQt,real *c_invQ, real
         cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
-#define debug            
+#undef debug            
 #ifdef debug
     
         for( j = 0; j < 10 ; ++j )
diff --git a/gp_emulator/gpu/tests/readTestData.c b/gp_emulator/gpu/tests/readTestData.c
index e78731b..fc7e540 100644
--- a/gp_emulator/gpu/tests/readTestData.c
+++ b/gp_emulator/gpu/tests/readTestData.c
@@ -8,7 +8,6 @@ real *readTestData(char *file_name, int M, int N, int D, int size)
     char path[200];
     sprintf(path, "data/set_%d_%d_%d/",N, M, D);
     strcat(path,file_name);
-    printf("reading data: %s\n",path);
     
     real *data;
     

From 256949725d32ed70f23838cddead0a44952bea97 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Fri, 22 May 2015 10:13:13 +0100
Subject: [PATCH 021/117] adaptation to capability 2.0

---
 gp_emulator/gpu/computeTranspose.c |  8 ++--
 gp_emulator/gpu/gpu_predict.h      |  3 +-
 gp_emulator/gpu/predict.cu         | 74 ++++++++++++++++++++----------
 3 files changed, 57 insertions(+), 28 deletions(-)

diff --git a/gp_emulator/gpu/computeTranspose.c b/gp_emulator/gpu/computeTranspose.c
index 27afd91..435b683 100644
--- a/gp_emulator/gpu/computeTranspose.c
+++ b/gp_emulator/gpu/computeTranspose.c
@@ -3,15 +3,15 @@
 //size_out is the leading dimension of the output matrix
 void computeTranspose(real *matrix, const  int size_in, const  int size_out)
 {
+    int i, x, y;
     real * temp;
     temp = ( real *)malloc(sizeof(real) * size_in * size_out);
-
-    for ( int i = 0; i < size_in * size_out; ++i)
+    for ( i = 0; i < size_in * size_out; ++i)
         temp[i] = matrix[i];
 
-    for (int y = 0; y < size_out; ++y)
+    for ( y = 0; y < size_out; ++y )
     {
-        for (int x = 0; x < size_in; ++x)
+        for ( x = 0; x < size_in; ++x )
         {
             matrix[(x * size_out) + y] = temp[(y * size_in) + x];                                                                
 
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 4078392..f941f8a 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -55,6 +55,7 @@ __global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
                 (int)(__err), \
                 __FILE__, __LINE__); \
             fprintf(stderr, "*** FAILED - ABORTING\n"); \
-            exit(1); \
+	    exit(1);\
         } \
     } while (0)
+
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 6002ddc..e8c4efc 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -78,6 +78,7 @@ void gpu_scalarMinusVec(real *vec, const real scalar)
 {
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
     vec[ix] = scalar - vec[ix];
+
 }
 
 
@@ -95,8 +96,8 @@ real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
     cudaMalloc((void **)&vec_one, sizeof(real) * A_ncols );
     cudaMalloc((void **)&d_var, sizeof(real) * A_ncols);
     
-    gpu_init_array<<< ceil(float(A_ncols)/512), 512 >>>(vec_one, 1);
-    gpu_init_array<<< ceil(float(A_ncols)/512),512 >>>(d_var, 0);
+    gpu_init_array<<< ceil(float(A_ncols)/1024), 1024 >>>(vec_one, 1);
+    gpu_init_array<<< ceil(float(A_ncols)/1024), 1024 >>>(d_var, 0);
 
     cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
     
@@ -138,7 +139,7 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
 
     //define device vector and matrices
     real *d_inputs, *d_theta_exp, *d_theta_exp_sqrt, *d_invQt, *d_invQ, *d_testing;
-    real *d_a;//, *d_deriv;
+    real *d_a;
 
 
     //allocate and copy vector on device 
@@ -173,18 +174,36 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     dim3 nthread(1,D);
     dim3 nblock(M,1);
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_inputs, d_theta_exp_sqrt, d_res_temp1, M);
-    nthread.x=1; nthread.y=D;
-    nblock.x=N; nblock.y=1;
+    nthread.x=100; nthread.y=D;
+    nblock.x=N/100; nblock.y=1;
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
-    gpu_init_array<<<ceil(float(N)*float(M)/512),512>>>(d_a, 0);
+    gpu_init_array<<<ceil(float(N)*float(M)/1024),1024>>>(d_a, 0);
 
-    nthread.x=1;   nthread.y=5;    nthread.z=1;
-    nblock.x=N;    nblock.y=M/5;     nblock.z=D;
+    nthread.x=200;   nthread.y=5;    nthread.z=1;
+    nblock.x=N/200;  nblock.y=M/5;     nblock.z=D;
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
-
-    gpu_matrixExp<<<N,M>>>(d_a, -0.5, c_theta_exp[D]);
+    
+    nthread.x=1000; nthread.y=1; nthread.z=1;
+    nblock.x=N*M/1000; nblock.y=1; nblock.z=1;
+    gpu_matrixExp<<<nblock,nthread>>>(d_a, -0.5, c_theta_exp[D]);
    
-    /*compute mu*/
+int kk;
+/*#undef debug
+#ifdef debug
+real *h_a;
+h_a=(real *)malloc(sizeof(real) * M * N);
+cudaMemcpy(h_a, d_a, sizeof(real) * M * N, cudaMemcpyDeviceToHost);
+
+for(kk =0; kk<10; kk++)
+  printf("%f|", h_a[kk]);
+  printf("\n");
+#endif
+*/
+
+
+      
+
+    //compute mu
     real *d_mu;
     cudaMalloc((void **)&d_mu, sizeof(real) * N);
     real alpha = 1.f;
@@ -194,7 +213,7 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
        
     
     
-    /*copute var*/
+    //copute var
     real *d_temp_dot, *d_a_T;
     real *d_var;
 
@@ -204,22 +223,32 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     cublasCheckErrors(cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, d_temp_dot, M));
     cublasCheckErrors(cublasDgeam(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
     cudaFree(d_a);
-    gpu_elementwiseMult<<< N, M >>>(d_a_T, d_temp_dot);
-       
+    gpu_elementwiseMult<<< nblock, nthread >>>(d_a_T, d_temp_dot);
+
     d_var = gpu_rowSum(d_temp_dot, M, N);
-    gpu_scalarMinusVec<<< N, M >>>(d_var, c_theta_exp[D]);
-    cudaMemcpy(c_var, d_var, sizeof(real) * N, cudaMemcpyDeviceToHost);
     
+/*    
+#ifdef debug
+cudaMemcpy(h_a, d_var, sizeof(real) * N, cudaMemcpyDeviceToHost);
+for(kk =0; kk<10; kk++)
+  printf("%f|", h_a[kk]);
+  printf("\n");
+#endif
+*/  
+  //change
+    gpu_scalarMinusVec<<<N/1000, 1000 >>>(d_var, c_theta_exp[D]);
+    cudaMemcpy(c_var, d_var, sizeof(real) * N, cudaMemcpyDeviceToHost);
+
     cudaFree(d_var);
     cudaFree(d_temp_dot);
 
 
-    /*compute deriv*/
+    //compute deriv
     real *d_deriv;
     cudaMalloc((void **)&d_deriv, sizeof(real) * N );
 
-    dim3 nblocks_getaa(1, N);
-    dim3 nthreads_getaa(M,1);
+    dim3 nblocks_getaa(M, N/1000);
+    dim3 nthreads_getaa(1,1000);
     real *d_aa;
     cudaMalloc((void **)&d_aa, sizeof(real) * M * N);
     real *ptr_inputs, *ptr_testing, *ptr_deriv;
@@ -232,12 +261,11 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
         ptr_inputs = ptr_inputs + M;
         ptr_testing = ptr_testing + N;
         alpha = c_theta_exp[i];
-        
-        gpu_elementwiseMult <<< N, M >>>(d_a_T, d_aa);
+        gpu_elementwiseMult <<< nblock, nthread >>>(d_a_T, d_aa);
         cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
-#undef debug            
+#define debug            
 #ifdef debug
     
         for( j = 0; j < 10 ; ++j )
@@ -251,10 +279,10 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
      cudaFree(d_mu);
 
      cudaFree(d_aa);
-     //cudaFree(d_a);
      cudaFree(d_inputs);
      cudaFree(d_deriv);
      cudaFree(d_theta_exp);
      cudaFree(d_testing);
+   
 }
 }

From a42ee6f2332657a17027435fc29efc6d9ef202d8 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Fri, 22 May 2015 10:21:54 +0100
Subject: [PATCH 022/117] new cmake with cookoff

---
 gp_emulator/gpu/CMakeLists.txt | 40 +++++++---------------------------
 1 file changed, 8 insertions(+), 32 deletions(-)

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 3a320da..5e76dd9 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -1,44 +1,20 @@
 cmake_minimum_required (VERSION 2.8)
 
 
-
+include(lookoff.cmake)
+find_package(Numpy REQUIRED)
+find_package(CoherentPython REQUIRED)
 find_package(CUDA QUIET REQUIRED)
-find_package(PythonLibs REQUIRED)
-find_package(PythonInterp REQUIRED)
-
 
 find_library(FOUND_CUBLAS cublas)
 include_directories(${PYTHON_INCLUDE_DIRS})
-include_directories(${NUMPY_INCLUDE_DIR})
-
-
-if (NOT NUMPY_INCLUDE_DIR)
-    exec_program ("${PYTHON_EXECUTABLE}"
-      ARGS "-c 'import numpy; print numpy.get_include()'"
-      OUTPUT_VARIABLE NUMPY_INCLUDE_DIR
-      RETURN_VALUE NUMPY_NOT_FOUND)
-    if (NUMPY_INCLUDE_DIR MATCHES "Traceback")
-    # Did not successfully include numpy
-      set(NUMPY_FOUND FALSE)
-    else (NUMPY_INCLUDE_DIR MATCHES "Traceback")
-    # successful
-      set (NUMPY_FOUND TRUE)
-      set (NUMPY_INCLUDE_DIR ${NUMPY_INCLUDE_DIR} CACHE PATH "Numpy include path")
-    endif (NUMPY_INCLUDE_DIR MATCHES "Traceback")
-    if (NUMPY_FOUND)
-      if (NOT NUMPY_FIND_QUIETLY)
-        message (STATUS "Numpy headers found")
-      endif (NOT NUMPY_FIND_QUIETLY)
-    else (NUMPY_FOUND)
-      if (NUMPY_FIND_REQUIRED)
-        message (FATAL_ERROR "Numpy headers missing")
-      endif (NUMPY_FIND_REQUIRED)
-    endif (NUMPY_FOUND)
-    mark_as_advanced (NUMPY_INCLUDE_DIR)
-endif (NOT NUMPY_INCLUDE_DIR)
+include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
+
+
 
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
 cuda_add_library(python_predict _gpu_predict.c predict.cu computeTranspose.c MODULE)
 cuda_add_cublas_to_target(python_predict)
-target_link_libraries(python_predict python)
+
+#target_link_libraries(python_predict python)

From edc39e9f20e4d6b383d1d5332bde25837305e335 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Fri, 22 May 2015 10:57:59 +0100
Subject: [PATCH 023/117] change cookoff file name

---
 gp_emulator/gpu/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 5e76dd9..837c823 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -1,7 +1,7 @@
 cmake_minimum_required (VERSION 2.8)
 
 
-include(lookoff.cmake)
+include(LookUp-GreatCMakeCookOff.cmake)
 find_package(Numpy REQUIRED)
 find_package(CoherentPython REQUIRED)
 find_package(CUDA QUIET REQUIRED)

From 24a2eb61ad09d2242f24de21f365019447b42d06 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Fri, 22 May 2015 16:04:05 +0100
Subject: [PATCH 024/117] 1) adding loop in the kernels avoiding threads number
 exceeding. 2) block number is a major factor of performance.

---
 gp_emulator/gpu/predict.cu | 52 ++++++++++++++++++++++++++++----------
 1 file changed, 38 insertions(+), 14 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index e8c4efc..681830b 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -3,6 +3,7 @@
 //#define IDX2C(i,j,ld) (((j)*(ld))+(i))
 #define IDX2D(i,j,ld) (((j)*(ld))+(i))  //!! keep it in column major for coping with cublas column major fashion.
 #define debug 
+#define CUDA_BLOCK 2
 // x -> i -> col
 // y -> j -> row
 // leading dimension should always be column
@@ -33,17 +34,28 @@ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld,
 
 
 __global__
-void gpu_init_array(real *vec, const real init_val)
+void gpu_init_array(real *vec, const real init_val, const int size)
 {
+    int i, index;
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    vec[ix] = init_val;
+    for( i = 0; i < CUDA_BLOCK; ++i )
+    {
+        index = ix * CUDA_BLOCK + i;
+        if( index < size)
+        {
+            vec[index] = init_val;
+        }
+    }
+//    vec[ix]=init_val;
 }
 // further tests needed: ix exceed M*N
 __global__
 void gpu_matrixExp(real *matrix, real alpha, real beta)
 {
+    int i;
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    matrix[ix] = beta * exp( alpha * matrix[ix]);
+    for( i = 0; i < CUDA_BLOCK; ++i )
+        matrix[ix * CUDA_BLOCK + i] = beta * exp( alpha * matrix[ix * CUDA_BLOCK + i]);
 }
 
 
@@ -66,10 +78,20 @@ void computeTranspose(real *matrix, const  int size_in, const  int size_out)
 }
 
 __global__
-void gpu_elementwiseMult(const real *v1, real *v2)
+void gpu_elementwiseMult(const real *v1, real *v2, const int size)
 {
+    int i, index;
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    v2[ix] = v2[ix] * v1[ix];
+    for( i = 0; i < CUDA_BLOCK; ++i )
+    {
+        index = ix * CUDA_BLOCK + i;
+        if( index < size)
+        {
+            v2[index] = v2[index] * v1[index];
+        }
+    }
+
+
 }
 
 
@@ -96,8 +118,10 @@ real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
     cudaMalloc((void **)&vec_one, sizeof(real) * A_ncols );
     cudaMalloc((void **)&d_var, sizeof(real) * A_ncols);
     
-    gpu_init_array<<< ceil(float(A_ncols)/1024), 1024 >>>(vec_one, 1);
-    gpu_init_array<<< ceil(float(A_ncols)/1024), 1024 >>>(d_var, 0);
+    gpu_init_array<<< ceil(float(A_ncols)/512/float(CUDA_BLOCK)), 512 >>>(vec_one, 1, A_ncols);
+    gpu_init_array<<< ceil(float(A_ncols)/512/float(CUDA_BLOCK)), 512 >>>(d_var, 0, A_ncols);
+    //gpu_init_array<<< ceil(float(A_ncols)/128), 128 >>>(vec_one, 1);
+    //gpu_init_array<<< ceil(float(A_ncols)/128), 128>>>(d_var, 0);
 
     cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
     
@@ -170,25 +194,25 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
     
 
-
     dim3 nthread(1,D);
     dim3 nblock(M,1);
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_inputs, d_theta_exp_sqrt, d_res_temp1, M);
     nthread.x=100; nthread.y=D;
     nblock.x=N/100; nblock.y=1;
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
-    gpu_init_array<<<ceil(float(N)*float(M)/1024),1024>>>(d_a, 0);
+    gpu_init_array<<<ceil(float(N)*float(M)/1000/float(CUDA_BLOCK)),1000>>>(d_a, 0, N * M);
+    //gpu_init_array<<<ceil(float(N)*float(M)/1024),1024>>>(d_a, 0);
 
     nthread.x=200;   nthread.y=5;    nthread.z=1;
     nblock.x=N/200;  nblock.y=M/5;     nblock.z=D;
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
     
     nthread.x=1000; nthread.y=1; nthread.z=1;
-    nblock.x=N*M/1000; nblock.y=1; nblock.z=1;
+    nblock.x=ceil(float(N) * float(M) / float(CUDA_BLOCK)/1000); nblock.y=1; nblock.z=1;
     gpu_matrixExp<<<nblock,nthread>>>(d_a, -0.5, c_theta_exp[D]);
    
 int kk;
-/*#undef debug
+/*#undef debug*/
 #ifdef debug
 real *h_a;
 h_a=(real *)malloc(sizeof(real) * M * N);
@@ -198,7 +222,7 @@ for(kk =0; kk<10; kk++)
   printf("%f|", h_a[kk]);
   printf("\n");
 #endif
-*/
+
 
 
       
@@ -223,7 +247,7 @@ for(kk =0; kk<10; kk++)
     cublasCheckErrors(cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, d_temp_dot, M));
     cublasCheckErrors(cublasDgeam(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
     cudaFree(d_a);
-    gpu_elementwiseMult<<< nblock, nthread >>>(d_a_T, d_temp_dot);
+    gpu_elementwiseMult<<< nblock, nthread >>>(d_a_T, d_temp_dot, M * N);
 
     d_var = gpu_rowSum(d_temp_dot, M, N);
     
@@ -261,7 +285,7 @@ for(kk =0; kk<10; kk++)
         ptr_inputs = ptr_inputs + M;
         ptr_testing = ptr_testing + N;
         alpha = c_theta_exp[i];
-        gpu_elementwiseMult <<< nblock, nthread >>>(d_a_T, d_aa);
+        gpu_elementwiseMult <<< nblock, nthread >>>(d_a_T, d_aa, M * N);
         cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);

From e24347c062b2b43cba7722679c1bb7bedf1c4fb3 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sun, 24 May 2015 23:46:03 +0100
Subject: [PATCH 025/117] loop implementation

---
 gp_emulator/gpu/gpu_predict.h | 21 ++++++++++--
 gp_emulator/gpu/predict.cu    | 62 ++++++++++++++++++++++++-----------
 2 files changed, 61 insertions(+), 22 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index f941f8a..1fe2073 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,11 +11,26 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
+#define DOUBLE__PRECISION
 
-#define real double
+#ifdef DOUBLE__PRECISION
+  #define real double
+#else
+  #define real float
+#endif 
+
+
+
+#ifdef DOUBLE__PRECISION
+  #define CUBLAS_GEMV cublasDgemv
+  #define CUBLAS_GEMM cublasDgemm
+  #define CUBLAS_GEAM cublasDgeam
+#else 
+  #define CUBLAS_GEMV cublasSgemv
+  #define CUBLAS_GEMM cublasSgemm
+  #define CUBLAS_GEAM cublasSgeam
+#endif
 
-//#ifndef GPU_PREDICT_H
-//#define GPU_PREDICT_H
 
 
 PyArrayObject *pyvector(PyObject *objin);
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index e8c4efc..878b575 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -3,6 +3,7 @@
 //#define IDX2C(i,j,ld) (((j)*(ld))+(i))
 #define IDX2D(i,j,ld) (((j)*(ld))+(i))  //!! keep it in column major for coping with cublas column major fashion.
 #define debug 
+#define CUDA_BLOCK 2
 // x -> i -> col
 // y -> j -> row
 // leading dimension should always be column
@@ -33,17 +34,28 @@ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld,
 
 
 __global__
-void gpu_init_array(real *vec, const real init_val)
+void gpu_init_array(real *vec, const real init_val, const int size)
 {
+    int i, index;
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    vec[ix] = init_val;
+    for( i = 0; i < CUDA_BLOCK; ++i )
+    {
+        index = ix * CUDA_BLOCK + i;
+        if( index < size)
+        {
+            vec[index] = init_val;
+        }
+    }
+//    vec[ix]=init_val;
 }
 // further tests needed: ix exceed M*N
 __global__
 void gpu_matrixExp(real *matrix, real alpha, real beta)
 {
+    int i;
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    matrix[ix] = beta * exp( alpha * matrix[ix]);
+    for( i = 0; i < CUDA_BLOCK; ++i )
+        matrix[ix * CUDA_BLOCK + i] = beta * exp( alpha * matrix[ix * CUDA_BLOCK + i]);
 }
 
 
@@ -66,10 +78,20 @@ void computeTranspose(real *matrix, const  int size_in, const  int size_out)
 }
 
 __global__
-void gpu_elementwiseMult(const real *v1, real *v2)
+void gpu_elementwiseMult(const real *v1, real *v2, const int size)
 {
+    int i, index;
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    v2[ix] = v2[ix] * v1[ix];
+    for( i = 0; i < CUDA_BLOCK; ++i )
+    {
+        index = ix * CUDA_BLOCK + i;
+        if( index < size)
+        {
+            v2[index] = v2[index] * v1[index];
+        }
+    }
+
+
 }
 
 
@@ -96,10 +118,12 @@ real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
     cudaMalloc((void **)&vec_one, sizeof(real) * A_ncols );
     cudaMalloc((void **)&d_var, sizeof(real) * A_ncols);
     
-    gpu_init_array<<< ceil(float(A_ncols)/1024), 1024 >>>(vec_one, 1);
-    gpu_init_array<<< ceil(float(A_ncols)/1024), 1024 >>>(d_var, 0);
+    gpu_init_array<<< ceil(float(A_ncols)/512/float(CUDA_BLOCK)), 512 >>>(vec_one, 1, A_ncols);
+    gpu_init_array<<< ceil(float(A_ncols)/512/float(CUDA_BLOCK)), 512 >>>(d_var, 0, A_ncols);
+    //gpu_init_array<<< ceil(float(A_ncols)/128), 128 >>>(vec_one, 1);
+    //gpu_init_array<<< ceil(float(A_ncols)/128), 128>>>(d_var, 0);
 
-    cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
+    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
     
     cudaFree(vec_one);
     cublasDestroy(handle);
@@ -170,25 +194,25 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
     
 
-
     dim3 nthread(1,D);
     dim3 nblock(M,1);
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_inputs, d_theta_exp_sqrt, d_res_temp1, M);
     nthread.x=100; nthread.y=D;
     nblock.x=N/100; nblock.y=1;
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
-    gpu_init_array<<<ceil(float(N)*float(M)/1024),1024>>>(d_a, 0);
+    gpu_init_array<<<ceil(float(N)*float(M)/1000/float(CUDA_BLOCK)),1000>>>(d_a, 0, N * M);
+    //gpu_init_array<<<ceil(float(N)*float(M)/1024),1024>>>(d_a, 0);
 
     nthread.x=200;   nthread.y=5;    nthread.z=1;
     nblock.x=N/200;  nblock.y=M/5;     nblock.z=D;
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
     
     nthread.x=1000; nthread.y=1; nthread.z=1;
-    nblock.x=N*M/1000; nblock.y=1; nblock.z=1;
+    nblock.x=ceil(float(N) * float(M) / float(CUDA_BLOCK)/1000); nblock.y=1; nblock.z=1;
     gpu_matrixExp<<<nblock,nthread>>>(d_a, -0.5, c_theta_exp[D]);
    
 int kk;
-/*#undef debug
+/*#undef debug*/
 #ifdef debug
 real *h_a;
 h_a=(real *)malloc(sizeof(real) * M * N);
@@ -198,7 +222,7 @@ for(kk =0; kk<10; kk++)
   printf("%f|", h_a[kk]);
   printf("\n");
 #endif
-*/
+
 
 
       
@@ -208,7 +232,7 @@ for(kk =0; kk<10; kk++)
     cudaMalloc((void **)&d_mu, sizeof(real) * N);
     real alpha = 1.f;
     real beta = 0.f;
-    cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1));
+    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1));
     cudaMemcpy(c_mu, d_mu, sizeof(real) * N, cudaMemcpyDeviceToHost);
        
     
@@ -220,10 +244,10 @@ for(kk =0; kk<10; kk++)
     cudaMalloc((void **)&d_temp_dot, sizeof(real) * M * N);
     cudaMalloc((void **)&d_a_T, sizeof(real) * M * N);
 
-    cublasCheckErrors(cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, d_temp_dot, M));
-    cublasCheckErrors(cublasDgeam(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
+    cublasCheckErrors(CUBLAS_GEMM(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, d_temp_dot, M));
+    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
     cudaFree(d_a);
-    gpu_elementwiseMult<<< nblock, nthread >>>(d_a_T, d_temp_dot);
+    gpu_elementwiseMult<<< nblock, nthread >>>(d_a_T, d_temp_dot, M * N);
 
     d_var = gpu_rowSum(d_temp_dot, M, N);
     
@@ -261,8 +285,8 @@ for(kk =0; kk<10; kk++)
         ptr_inputs = ptr_inputs + M;
         ptr_testing = ptr_testing + N;
         alpha = c_theta_exp[i];
-        gpu_elementwiseMult <<< nblock, nthread >>>(d_a_T, d_aa);
-        cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
+        gpu_elementwiseMult <<< nblock, nthread >>>(d_a_T, d_aa, M * N);
+        cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
 #define debug            

From 44eb74cc60e2b03a640f4042629cb74e7c76d4d2 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 26 May 2015 10:01:30 +0100
Subject: [PATCH 026/117] single precision

---
 gp_emulator/gpu/_gpu_predict.c | 52 ++++++++++++++++++++++------------
 gp_emulator/gpu/gpu_predict.h  |  8 +++---
 gp_emulator/gpu/predict.cu     |  4 ---
 gp_emulator/gpu/test.py        | 21 +++++++++-----
 4 files changed, 52 insertions(+), 33 deletions(-)

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 2d92060..4805872 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -2,7 +2,6 @@
 #include "gpu_predict.h"
 
 
-#define real double
 
 // PyArrayObject *pymatrix(PyObject *objin);
 // double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
@@ -40,25 +39,42 @@ void init_gpu_predict()  {
      PyArray_BuildValue with the "N" construct   !!!
 */
 PyArrayObject *pyvector(PyObject *objin)  {
-    return (PyArrayObject *) PyArray_ContiguousFromObject(objin,
-        NPY_DOUBLE, 1,1);
+    if( sizeof( real ) == sizeof( double ) )
+        return (PyArrayObject *) PyArray_ContiguousFromObject(objin,NPY_DOUBLE, 1,1);
+    if( sizeof( real ) == sizeof( float ) )
+        return (PyArrayObject *) PyArray_ContiguousFromObject(objin,NPY_FLOAT32, 1,1);
+
 }
 /* ==== Create 1D Carray from PyArray ======================
     Assumes PyArray is contiguous in memory.             */
-double *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
+real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
     int i,n;
     
     n=arrayin->dimensions[0];
-    return (double *) arrayin->data;  /* pointer to arrayin data as double */
+    return (real *) arrayin->data;  /* pointer to arrayin data as double */
 }
 /* ==== Check that PyArrayObject is a double (Float) type and a vector ==============
     return 1 if an error and raise exception */ 
-int  not_doublevector(PyArrayObject *vec)  {
-    if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  {
-        PyErr_SetString(PyExc_ValueError,
-            "In not_doublevector: array must be of type Float and 1 dimensional (n).");
-        return 1;  }
-    return 0;
+int  not_realvector(PyArrayObject *vec)  {
+    if( sizeof( real ) == sizeof( double ) )
+    {
+        if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  
+        {
+            PyErr_SetString(PyExc_ValueError, "In not_realvector: array must be of type Float and 1 dimensional (n).");
+            return 1;  
+        }
+        return 0;
+    }
+    if( sizeof( real ) == sizeof( float ) ) 
+    {
+        if (vec->descr->type_num != NPY_FLOAT32 || vec->nd != 1)  
+        {
+            PyErr_SetString(PyExc_ValueError, "In not_realvector: array must be of type Float and 1 dimensional (n).");
+            return 1;  
+        }
+        return 0;
+    }
+ 
 }
 
 
@@ -67,24 +83,24 @@ int  not_doublevector(PyArrayObject *vec)  {
     Assumes PyArray is contiguous in memory.
     Memory is allocated!                                    */
 
-double **ptrvector(long n)  {
-    double **v;
-    v=(double **)malloc((size_t) (n*sizeof(double)));
+real **ptrvector(long n)  {
+    real **v;
+    v=(real **)malloc((size_t) (n*sizeof(real)));
     if (!v)   {
-        printf("In **ptrvector. Allocation of memory for double array failed.");
+        printf("In **ptrvector. Allocation of memory for real array failed.");
         exit(0);  }
     return v;
 }
 
 
-double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
-    double **c, *a;
+real **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
+    real **c, *a;
     int i,n,m;
     
     n=arrayin->dimensions[0];
     m=arrayin->dimensions[1];
     c=ptrvector(n);
-    a=(double *) arrayin->data;  /* pointer to arrayin data as double */
+    a=(real *) arrayin->data;  /* pointer to arrayin data as double */
     for ( i=0; i < n; i++)  {
         c[i]=a+i*m;  }
     return c;
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 1fe2073..1d759da 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,7 +11,7 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
-#define DOUBLE__PRECISION
+//#define DOUBLE__PRECISION
 
 #ifdef DOUBLE__PRECISION
   #define real double
@@ -34,9 +34,9 @@
 
 
 PyArrayObject *pyvector(PyObject *objin);
-double *pyvector_to_Carrayptrs(PyArrayObject *arrayin);
-double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
-double **ptrvector(long n);
+real*pyvector_to_Carrayptrs(PyArrayObject *arrayin);
+real **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
+real **ptrvector(long n);
 void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
 /*
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 90be204..878b575 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -286,11 +286,7 @@ for(kk =0; kk<10; kk++)
         ptr_testing = ptr_testing + N;
         alpha = c_theta_exp[i];
         gpu_elementwiseMult <<< nblock, nthread >>>(d_a_T, d_aa, M * N);
-<<<<<<< HEAD
         cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
-=======
-        cublasCheckErrors(cublasDgemv(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
->>>>>>> ea03cc4e48ef0941dc20b4ccbaf87d97dcae727c
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
 #define debug            
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index 7f120a6..0bd5f7a 100755
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -130,13 +130,20 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         #print 'sqrt(expX)*testing\n', test_var_2
         #print 'a_ =\n', test_var_3
         
-        a =_gpu_predict.predict_wrap(
-            expX,
-            self.inputs,
-            self.invQt,
-            self.invQ,
-            testing,
-            N,M,D,theta_size)
+#        a =_gpu_predict.predict_wrap(
+#            expX,
+#            self.inputs,
+#            self.invQt,
+#            self.invQ,
+#            testing,
+#            N,M,D,theta_size)
+        a = _gpu_predict.predict_wrap(
+                np.float32(expX),
+                np.float32(self.inputs),
+                np.float32(self.invQt),
+                np.float32(self.invQ),
+                np.float32(testing),
+                N, M, D, theta_size)
         #print 'testing\n',testing
         #print 'a\n',a
 

From 2d1466b2b001e54a1f9bae883a4825d6e26b49fe Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 26 May 2015 10:07:26 +0100
Subject: [PATCH 027/117] add cmake cookoff file

---
 .../gpu/LookUp-GreatCMakeCookOff.cmake        | 59 +++++++++++++++++++
 1 file changed, 59 insertions(+)
 create mode 100644 gp_emulator/gpu/LookUp-GreatCMakeCookOff.cmake

diff --git a/gp_emulator/gpu/LookUp-GreatCMakeCookOff.cmake b/gp_emulator/gpu/LookUp-GreatCMakeCookOff.cmake
new file mode 100644
index 0000000..fae65ca
--- /dev/null
+++ b/gp_emulator/gpu/LookUp-GreatCMakeCookOff.cmake
@@ -0,0 +1,59 @@
+# This file is part of the GreatCMakeCookOff package and distributed under the MIT Licences.
+# Upon inclusion in a cmake file, it will download the GreatCMakeCookOff and make itself known to
+# CMake. It should be added explicitely to build systems that make use of recipes from the cook-off.
+# And it should be included prior to using cook-off recipes:
+#
+# ```{CMake}
+# include(LookUp-GreatCMakeCookOff)
+# ```
+
+
+# First attempts to find the package
+set(COOKOFF_DOWNLOAD_DIR "${PROJECT_BINARY_DIR}/external/src/GreatCMakeCookOff")
+find_package(GreatCMakeCookOff NO_MODULE PATHS "${COOKOFF_DOWNLOAD_DIR}" QUIET)
+
+# Otherwise attempts to download it.
+# Does not use ExternalProject_Add to avoid doing a recursive cmake step.
+if(NOT GreatCMakeCookOff_FOUND)
+  message(STATUS "[GreatCMakeCookOff] not found. Will attempt to clone it.")
+
+  # Need git for cloning.
+  find_package(Git)
+  if(NOT GIT_FOUND)
+    message(FATAL_ERROR "[Git] not found. Cannot download GreatCMakeCookOff")
+  endif()
+
+  # Remove GreatCMakeCookOff directory if it exists
+  if(EXISTS "${COOKOFF_DOWNLOAD_DIR}")
+    execute_process(
+      COMMAND ${CMAKE_COMMAND} -E remove_directory "${COOKOFF_DOWNLOAD_DIR}"
+      OUTPUT_QUIET
+    )
+  endif()
+  if(NOT COOKOFF_GITREPO)
+      set(COOKOFF_GITREPO https://github.com/UCL/GreatCMakeCookOff.git)
+  endif()
+  execute_process(
+    COMMAND ${GIT_EXECUTABLE} clone "${COOKOFF_GITREPO}"
+         "${COOKOFF_DOWNLOAD_DIR}"
+    RESULT_VARIABLE CLONING_COOKOFF
+    OUTPUT_QUIET
+    ERROR_VARIABLE CLONING_ERROR
+  )
+
+  if(NOT ${CLONING_COOKOFF} EQUAL 0)
+    message(STATUS "${CLONING_ERROR}")
+    message(FATAL_ERROR "[GreatCMakeCookOff] git cloning failed.")
+  else()
+    message(STATUS "[GreatCMakeCookOff] downloaded to ${COOKOFF_DOWNLOAD_DIR}")
+    find_package(GreatCMakeCookOff NO_MODULE PATHS "${COOKOFF_DOWNLOAD_DIR}" QUIET)
+  endif()
+
+  set(GreatCMakeCookOff_DIR "${COOKOFF_DOWNLOAD_DIR}/cmake")
+  set(GreatCMakeCookOff_FOUND TRUE)
+endif()
+unset(COOKOFF_DOWNLOAD_DIR)
+
+# Adds GreatCMakeCookOff to module paths
+initialize_cookoff()
+

From 4ab4c6742b0e377274bab4bedfbbda6dcbec7060 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Tue, 26 May 2015 11:07:05 +0100
Subject: [PATCH 028/117] N=1.8e6, blocksize=7

---
 gp_emulator/gpu/_gpu_predict.c | 10 ----------
 gp_emulator/gpu/predict.cu     |  8 +++++---
 gp_emulator/gpu/test.py        |  3 +--
 3 files changed, 6 insertions(+), 15 deletions(-)

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 4805872..bc1fc93 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -1,16 +1,6 @@
 
 #include "gpu_predict.h"
 
-
-
-// PyArrayObject *pymatrix(PyObject *objin);
-// double **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
-// double **ptrvector(long n);
-// void free_Carrayptrs(double **v);
-// int  not_doublematrix(PyArrayObject *mat);
-
-
-
 /* ==== Set up the methods table ====================== */
 static PyMethodDef gpuMethods[] = {
     {"predict_wrap", predict_wrap, METH_VARARGS},
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 878b575..953221a 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -3,7 +3,7 @@
 //#define IDX2C(i,j,ld) (((j)*(ld))+(i))
 #define IDX2D(i,j,ld) (((j)*(ld))+(i))  //!! keep it in column major for coping with cublas column major fashion.
 #define debug 
-#define CUDA_BLOCK 2
+#define CUDA_BLOCK 7
 // x -> i -> col
 // y -> j -> row
 // leading dimension should always be column
@@ -201,16 +201,18 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     nblock.x=N/100; nblock.y=1;
     gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
     gpu_init_array<<<ceil(float(N)*float(M)/1000/float(CUDA_BLOCK)),1000>>>(d_a, 0, N * M);
-    //gpu_init_array<<<ceil(float(N)*float(M)/1024),1024>>>(d_a, 0);
 
     nthread.x=200;   nthread.y=5;    nthread.z=1;
     nblock.x=N/200;  nblock.y=M/5;     nblock.z=D;
     gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
+
     
     nthread.x=1000; nthread.y=1; nthread.z=1;
     nblock.x=ceil(float(N) * float(M) / float(CUDA_BLOCK)/1000); nblock.y=1; nblock.z=1;
     gpu_matrixExp<<<nblock,nthread>>>(d_a, -0.5, c_theta_exp[D]);
-   
+
+    cudaFree(d_res_temp1);
+    cudaFree(d_res_temp2);
 int kk;
 /*#undef debug*/
 #ifdef debug
diff --git a/gp_emulator/gpu/test.py b/gp_emulator/gpu/test.py
index 0bd5f7a..e68aba6 100755
--- a/gp_emulator/gpu/test.py
+++ b/gp_emulator/gpu/test.py
@@ -1,4 +1,3 @@
-#!/usr/bin/python
 import numpy as np
 import scipy.spatial.distance as dist
 import _gpu_predict
@@ -159,7 +158,7 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=1.5e5
+    N=1.8e6
     M=250
     P=10
     testing=np.random.random((N,P))

From 1aceb2584baa80e61647dcd5785a589a0791f5c3 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Tue, 26 May 2015 14:37:27 +0100
Subject: [PATCH 029/117] top level layout

---
 .../gpu => build/external}/LookUp-GreatCMakeCookOff.cmake         | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename {gp_emulator/gpu => build/external}/LookUp-GreatCMakeCookOff.cmake (100%)

diff --git a/gp_emulator/gpu/LookUp-GreatCMakeCookOff.cmake b/build/external/LookUp-GreatCMakeCookOff.cmake
similarity index 100%
rename from gp_emulator/gpu/LookUp-GreatCMakeCookOff.cmake
rename to build/external/LookUp-GreatCMakeCookOff.cmake

From ccba736ee335b34f931bb20847e020ced8c77d73 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Tue, 26 May 2015 14:39:19 +0100
Subject: [PATCH 030/117] cmake top level

---
 CMakeLists.txt                 | 10 ++++++++++
 gp_emulator/gpu/CMakeLists.txt |  7 +++----
 2 files changed, 13 insertions(+), 4 deletions(-)
 create mode 100644 CMakeLists.txt

diff --git a/CMakeLists.txt b/CMakeLists.txt
new file mode 100644
index 0000000..132812f
--- /dev/null
+++ b/CMakeLists.txt
@@ -0,0 +1,10 @@
+cmake_minimum_required (VERSION 2.8)
+
+
+include(build/external/LookUp-GreatCMakeCookOff.cmake)
+subdirs(gp_emulator/gpu)
+set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib/gp_emulator)
+
+message("export PYTHONPATH=${LIBRARY_OUTPUT_PATH}")
+
+#target_link_libraries(python_predict python)
diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 837c823..a6db63c 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -1,7 +1,6 @@
 cmake_minimum_required (VERSION 2.8)
 
 
-include(LookUp-GreatCMakeCookOff.cmake)
 find_package(Numpy REQUIRED)
 find_package(CoherentPython REQUIRED)
 find_package(CUDA QUIET REQUIRED)
@@ -11,10 +10,10 @@ include_directories(${PYTHON_INCLUDE_DIRS})
 include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 
 
-
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
-cuda_add_library(python_predict _gpu_predict.c predict.cu computeTranspose.c MODULE)
-cuda_add_cublas_to_target(python_predict)
+cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.c MODULE)
+SET_TARGET_PROPERTIES(_gpu_predict  PROPERTIES PREFIX "")
+cuda_add_cublas_to_target(_gpu_predict)
 
 #target_link_libraries(python_predict python)

From 7596569c7277ea48098fdf773ef522d710400cab Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 26 May 2015 17:13:59 +0100
Subject: [PATCH 031/117] cmake for testings;

---
 gp_emulator/gpu/CMakeLists.txt                |  6 ++-
 gp_emulator/gpu/Makefile                      | 29 ------------
 .../gpu/{_gpu_predict.c => _gpu_predict.cu}   |  0
 ...computeTranspose.c => computeTranspose.cu} |  0
 gp_emulator/gpu/gpu_predict.h                 |  7 +--
 gp_emulator/gpu/predict.cu                    | 22 +--------
 gp_emulator/gpu/tests/CMakeLists.txt          |  8 ++++
 gp_emulator/gpu/tests/gpu_predict_test.h      |  1 +
 .../tests/{readTestData.c => readTestData.cu} |  0
 gp_emulator/gpu/tests/{test1.c => test1.cu}   |  4 +-
 gp_emulator/gpu/tests/testCdist.cu            | 18 +++----
 .../{benchmark_predict.c => testPredict.cu}   | 47 ++++++++-----------
 12 files changed, 50 insertions(+), 92 deletions(-)
 delete mode 100644 gp_emulator/gpu/Makefile
 rename gp_emulator/gpu/{_gpu_predict.c => _gpu_predict.cu} (100%)
 rename gp_emulator/gpu/{computeTranspose.c => computeTranspose.cu} (100%)
 create mode 100644 gp_emulator/gpu/tests/CMakeLists.txt
 rename gp_emulator/gpu/tests/{readTestData.c => readTestData.cu} (100%)
 rename gp_emulator/gpu/tests/{test1.c => test1.cu} (96%)
 rename gp_emulator/gpu/tests/{benchmark_predict.c => testPredict.cu} (68%)

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index a6db63c..e79729b 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -12,8 +12,10 @@ include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
-cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.c MODULE)
+cuda_add_library(_gpu_predict _gpu_predict.cu predict.cu computeTranspose.cu MODULE)
 SET_TARGET_PROPERTIES(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
 
-#target_link_libraries(python_predict python)
+target_link_libraries(_gpu_predict python)
+
+subdirs(tests)
diff --git a/gp_emulator/gpu/Makefile b/gp_emulator/gpu/Makefile
deleted file mode 100644
index 3ee1f70..0000000
--- a/gp_emulator/gpu/Makefile
+++ /dev/null
@@ -1,29 +0,0 @@
-SRCS= _gpu_predict.c predict.cu cdist.cu computeTranspose.c
-OBJS = _gpu_predict.o cdist.o predict.o computeTranspose.o
-SHARE = _gpu_predict.so #$(SRCS:.c=.so) $(CUDASRCS:.cu=.so)
-GPUCC = nvcc
-CC = gcc
-BUILDPATH= ../../build/lib/
-INC = -I/usr/include/python2.7 -I/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy
-LIB = -lcuda -lcudart -lcublas
-NVLIB = -L/Developer/NVIDIA/CUDA-7.0/lib/
-
-
-
-
-all: $(SHARE) $(OBJS)
-
-
-%.so:$(OBJS)
-	$(CC) -shared -fpic -flat_namespace -undefined suppress -o $(BUILDPATH)$@ $^ $(INC) $(NVLIB) $(LIB) 
-
-%.o:%.c
-	$(GPUCC)  -c $^  -o $@  $(INC) $(LIB) $(NVLIB)  --compiler-options '-fPIC'
-
-%.o:%.cu
-	$(GPUCC)  -c $^ -o $@ $(INC) $(NVLIB) $(LIB)  --compiler-options '-fPIC' 
-
-clean:
-	rm *.o $(BUILDPATH)*.so
-
-#
diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.cu
similarity index 100%
rename from gp_emulator/gpu/_gpu_predict.c
rename to gp_emulator/gpu/_gpu_predict.cu
diff --git a/gp_emulator/gpu/computeTranspose.c b/gp_emulator/gpu/computeTranspose.cu
similarity index 100%
rename from gp_emulator/gpu/computeTranspose.c
rename to gp_emulator/gpu/computeTranspose.cu
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 1d759da..8a993e5 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -39,10 +39,11 @@ real **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 real **ptrvector(long n);
 void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
-/*
-extern "C"
+
+
+//extern "C"
 void predict(const real *c_theta_exp,const  real *c_inputs,const real *c_invQt, const real *c_invQ, const real *c_testing, real *c_mu, real *c_var, real *c_deriv, const int N, const int M, const int  D, const int theta_size);
-*/
+//*/
 __global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D);
 __global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
 __global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 953221a..b8bb210 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -59,24 +59,6 @@ void gpu_matrixExp(real *matrix, real alpha, real beta)
 }
 
 
-void computeTranspose(real *matrix, const  int size_in, const  int size_out)
-{
-    real * temp;
-    temp = ( real *)malloc(sizeof(real) * size_in * size_out);
-
-    for ( int i = 0; i < size_in * size_out; ++i)
-        temp[i] = matrix[i];
-
-    for (int y = 0; y < size_out; ++y)
-    {
-        for (int x = 0; x < size_in; ++x)
-        {
-            matrix[(x * size_out) + y] = temp[(y * size_in) + x];                                                                
-
-        }   
-    }
-}
-
 __global__
 void gpu_elementwiseMult(const real *v1, real *v2, const int size)
 {
@@ -142,7 +124,7 @@ void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nr
 
 
 
-extern "C" {
+//extern "C" {
 void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
         real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
 {
@@ -311,4 +293,4 @@ for(kk =0; kk<10; kk++)
      cudaFree(d_testing);
    
 }
-}
+//}
diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
new file mode 100644
index 0000000..94a7d2f
--- /dev/null
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -0,0 +1,8 @@
+FIND_PATH(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
+FIND_LIBRARY(CUNIT_LIBRARY NAMES cunit libcunit cunitlib)
+
+CUDA_ADD_EXECUTABLE(gpu_predict_test test1.cu readTestData.cu     testCdist.cu        testCublasgemm.cu   
+                    testMatrixExp.cu    testPredict.cu      testVecTimesMat.cu
+                    ../predict.cu ../computeTranspose.cu)
+cuda_add_cublas_to_target(gpu_predict_test)
+target_link_libraries(gpu_predict_test cunit)
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index 656c04e..50328cf 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -26,3 +26,4 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
 void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols);
 void testPredict(const real *expX, const real *inputs, const real *invQt, const real *invQ, const real *testing, const real *mu, const real *var, const real *deriv, int M, int N, int D);
+//void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size);
diff --git a/gp_emulator/gpu/tests/readTestData.c b/gp_emulator/gpu/tests/readTestData.cu
similarity index 100%
rename from gp_emulator/gpu/tests/readTestData.c
rename to gp_emulator/gpu/tests/readTestData.cu
diff --git a/gp_emulator/gpu/tests/test1.c b/gp_emulator/gpu/tests/test1.cu
similarity index 96%
rename from gp_emulator/gpu/tests/test1.c
rename to gp_emulator/gpu/tests/test1.cu
index f900c2a..15bfb0c 100644
--- a/gp_emulator/gpu/tests/test1.c
+++ b/gp_emulator/gpu/tests/test1.cu
@@ -92,10 +92,10 @@ static void tests_VecTimesMat(void)
 {
     dim3 nblocks_1(M,1);
     dim3 nthreads_1(1,D);
-    testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D, nblocks_1, nthreads_1 );
+//    testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D, nblocks_1, nthreads_1 );
     dim3 nblocks_2(N/50,1);
     dim3 nthreads_2(50,D);
-    testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D, nblocks_2, nthreads_2 );
+//    testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D, nblocks_2, nthreads_2 );
 }
 
 void tests_cdist(void)
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index d10eafe..e5e3a56 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -4,7 +4,7 @@
 
 void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads)
 {
-    int i, j, k;
+    int i, j, k, error;
     real epsilon;
     real *in1_T, *in2_T, *gpu_res;
     real *d_in1, *d_in2, *d_res; 
@@ -22,7 +22,6 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
     computeTranspose( in2_T, in_ncols, in2_nrows );
     
     
-    
 /*    for( i = 0; i < in1_nrows * in2_nrows; i++ )
         gpu_res[i] = 0;
     for( i = 0; i < in2_nrows; i++)
@@ -51,22 +50,25 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
     cudaMalloc((void **)&d_in1, sizeof(real) * in1_nrows * in_ncols );
     cudaMalloc((void **)&d_in2, sizeof(real) * in2_nrows * in_ncols );
     cudaMalloc((void **)&d_res, sizeof(real) * in2_nrows * in1_nrows );
-
     cublasCheckErrors(cublasSetMatrix( in1_nrows, in_ncols, sizeof(real), in1_T, in1_nrows, d_in1, in1_nrows) );
     cublasCheckErrors(cublasSetMatrix( in2_nrows, in_ncols, sizeof(real), in2_T, in2_nrows, d_in2, in2_nrows) );
     cublasCheckErrors(cublasSetMatrix( in2_nrows, in1_nrows, sizeof(real), gpu_res, in2_nrows, d_res,in2_nrows) );
     gpu_cdist <<< nblocks, nthreads  >>>(d_in1, d_in2, d_res, in1_nrows, in2_nrows, in2_nrows, in_ncols);
-    
     cudaMemcpy(gpu_res, d_res, sizeof(real) * in2_nrows * in1_nrows, cudaMemcpyDeviceToHost);
-
+    
+    error = 0;
     for( i = 0; i < in1_nrows * in2_nrows; i++ )
     {
         epsilon = abs( res[i] - gpu_res[i] );
-        CU_ASSERT( epsilon < 1e-6 );
+        if( epsilon > 1e-6 )
+            error++;
     }
-
+    if( error != 0)
+        printf( "\n cdist: error > 1e-6 %f\%[%d/%d]\n", (float)(error)/(float)(in1_nrows*in2_nrows), error, in1_nrows*in2_nrows );
  
-   
+    CU_PASS( error == 0);
+  
+
    free(in1_T);
    free(in2_T);
    free(gpu_res);
diff --git a/gp_emulator/gpu/tests/benchmark_predict.c b/gp_emulator/gpu/tests/testPredict.cu
similarity index 68%
rename from gp_emulator/gpu/tests/benchmark_predict.c
rename to gp_emulator/gpu/tests/testPredict.cu
index 23ad3db..fbb1c41 100644
--- a/gp_emulator/gpu/tests/benchmark_predict.c
+++ b/gp_emulator/gpu/tests/testPredict.cu
@@ -1,35 +1,9 @@
 #include "gpu_predict_test.h"
 #include <time.h>
 
-
-
-
-int main()
+void testPredict(const real *expX, const real *inputs, const real *invQt, const real *invQ, const real *testing, 
+        const real *mu, const real *var, const real *deriv, int M, int N, int D)
 {
-    
-    real *invQ, *invQt, *expX, *inputs, *testing;
-    
-    int M = 250;
-    int N = 1.5e5;
-    int D = 10;
-
-    invQ            =  readTestData( "invQ.bin", M, N, D, M * M);
-    invQt           =  readTestData( "invQt.bin", M, N, D, M );
-
-    expX            =  readTestData( "expX.bin", M, N, D, D+2);
-    inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
-    testing         =  readTestData( "testing.bin", M, N, D, N * D );
-
-//    mu              =  readTestData( "mu.bin", M, N, D, M * N);
-//    var             =  readTestData( "var.bin", M, N, D, N);
-//    deriv           =  readTestData( "deriv.bin", M, N, D, M * N );
-
-
-
-
-
-
-
 
     int i;
     real e;
@@ -77,6 +51,23 @@ int main()
     printf("\n");
     printf("      -total time including matrix transposing = %f (sec)\n", (double)(end1-begin1)/CLOCKS_PER_SEC);
     printf("      -pure predict function = %f (sec)\n", (double)(end2-begin2)/CLOCKS_PER_SEC);
+    for( i = 0; i < N * D; ++i )
+    {
+        e = abs( gpu_deriv[i] - deriv[i] );
+        CU_ASSERT( e < 1e-6 );
+    }
+
+    for( i = 0; i < N; ++i )
+    {
+        e = abs( gpu_mu[i] - mu[i] );
+        CU_ASSERT( e < 1e-6);
+    }
+
+    for( i = 0; i < N; ++i)
+    {
+        e = abs( gpu_var[i] - var[i] );
+        CU_ASSERT( e < 1e-6 );
+    }
 
 
         

From bb49bac27c9f318c2b013e72aa5cce66b1236026 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 27 May 2015 13:36:41 +0100
Subject: [PATCH 032/117] tests called by python; single precision
 implementation

---
 gp_emulator/gpu/gpu_predict.h                 |   3 +-
 gp_emulator/gpu/predict.cu                    |   7 +-
 gp_emulator/gpu/tests/Makefile                |  38 -------
 .../gpu/tests/data/set_100_250_10/cdist_a.bin | Bin 200000 -> 0 bytes
 .../tests/data/set_100_250_10/cdist_expa.bin  | Bin 200000 -> 0 bytes
 .../data/set_100_250_10/cdist_test_var1.bin   | Bin 20000 -> 0 bytes
 .../data/set_100_250_10/cdist_test_var2.bin   | Bin 8000 -> 0 bytes
 .../gpu/tests/data/set_100_250_10/expX.bin    | Bin 96 -> 0 bytes
 .../tests/data/set_100_250_10/expXsqrt.bin    |   1 -
 .../gpu/tests/data/set_100_250_10/inputs.bin  | Bin 20000 -> 0 bytes
 .../gpu/tests/data/set_100_250_10/testing.bin | Bin 8000 -> 0 bytes
 gp_emulator/gpu/tests/gpu_predict_test.h      |   3 +-
 gp_emulator/gpu/tests/readTestData.cu         |   4 +-
 gp_emulator/gpu/tests/test1.cu                |  72 +++++--------
 gp_emulator/gpu/tests/testCdist.cu            |  30 +-----
 gp_emulator/gpu/tests/testCublasgemm.cu       |  16 +--
 gp_emulator/gpu/tests/testMatrixExp.cu        |  18 ++--
 gp_emulator/gpu/tests/testPredict.cu          |  29 ++++--
 gp_emulator/gpu/tests/testVecTimesMat.cu      |  10 +-
 gp_emulator/gpu/tests/unit_tests.py           |  95 ++++++++++++++++++
 20 files changed, 179 insertions(+), 147 deletions(-)
 delete mode 100644 gp_emulator/gpu/tests/Makefile
 delete mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin
 delete mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/cdist_expa.bin
 delete mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var1.bin
 delete mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/cdist_test_var2.bin
 delete mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/expX.bin
 delete mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/expXsqrt.bin
 delete mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/inputs.bin
 delete mode 100644 gp_emulator/gpu/tests/data/set_100_250_10/testing.bin
 create mode 100755 gp_emulator/gpu/tests/unit_tests.py

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 8a993e5..3ee4222 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -12,7 +12,8 @@
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
 //#define DOUBLE__PRECISION
-
+#define CUDA_BLOCK 7
+ 
 #ifdef DOUBLE__PRECISION
   #define real double
 #else
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index b8bb210..cacbfb5 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -3,7 +3,7 @@
 //#define IDX2C(i,j,ld) (((j)*(ld))+(i))
 #define IDX2D(i,j,ld) (((j)*(ld))+(i))  //!! keep it in column major for coping with cublas column major fashion.
 #define debug 
-#define CUDA_BLOCK 7
+//#define CUDA_BLOCK 7
 // x -> i -> col
 // y -> j -> row
 // leading dimension should always be column
@@ -196,7 +196,7 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     cudaFree(d_res_temp1);
     cudaFree(d_res_temp2);
 int kk;
-/*#undef debug*/
+#undef debug
 #ifdef debug
 real *h_a;
 h_a=(real *)malloc(sizeof(real) * M * N);
@@ -273,7 +273,8 @@ for(kk =0; kk<10; kk++)
         cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
-#define debug            
+#define debug
+#undef debug
 #ifdef debug
     
         for( j = 0; j < 10 ; ++j )
diff --git a/gp_emulator/gpu/tests/Makefile b/gp_emulator/gpu/tests/Makefile
deleted file mode 100644
index 9a1d44f..0000000
--- a/gp_emulator/gpu/tests/Makefile
+++ /dev/null
@@ -1,38 +0,0 @@
-SRCS= test1.c  testVecTimesMat.cu testCdist.cu testMatrixExp.cu\
-      testCublasgemm.cu testPredict.o\
-      readTestData.c ../computeTranspose.c ../predict.cu
-
-OBJS = test1.o readTestData.o testVecTimesMat.o testCdist.o testMatrixExp.o\
-       testCublasgemm.o testPredict.o\
-       ../computeTranspose.o ../predict.o
-GPUCC = nvcc
-CC = gcc
-BUILDPATH= #../../build/lib/
-INC = -I/usr/include/python2.7 -I/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy
-LIB = -link  -lcunit -lcuda -lcudart -lcublas
-NVLIB = -L/Developer/NVIDIA/CUDA-7.0/lib/
-
-
-
-
-all:  benchmark test
-
-benchmark: readTestData.o ../computeTranspose.o ../predict.o benchmark_predict.o
-	nvcc -o $@ $^ $(INC) $(NVLIB) $(LIB)
-
-benchmark_predict.o: benchmark_predict.c
-	nvcc -c $@ $^ $(INC) $(NVLIB) $(LIB)
-
-test: $(OBJS)
-	$(GPUCC)  -o $@ $^ $(INC) $(NVLIB) $(LIB)  
-
-%.o:%.c
-	$(GPUCC) -x cu -dc  $^  -o $@   $(INC) $(LIB) $(NVLIB)
-
-%.o:%.cu
-	$(GPUCC)  -dc $^ -o $@ $(INC) $(NVLIB) $(NVLIB)
-
-clean:
-	rm *.o test
-
-#
diff --git a/gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin b/gp_emulator/gpu/tests/data/set_100_250_10/cdist_a.bin
deleted file mode 100644
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diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index 50328cf..f6fd944 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -15,8 +15,7 @@ static real *var_test1;
 static real *mu, *var, *deriv;
 
 static  int M, N, D;
-
-
+#define epsilon 1e-2
 
 real *readTestData(char *file_name, int M, int N, int D, int size);
 
diff --git a/gp_emulator/gpu/tests/readTestData.cu b/gp_emulator/gpu/tests/readTestData.cu
index fc7e540..2a98e0e 100644
--- a/gp_emulator/gpu/tests/readTestData.cu
+++ b/gp_emulator/gpu/tests/readTestData.cu
@@ -5,8 +5,8 @@
 real *readTestData(char *file_name, int M, int N, int D, int size)
 {
     
-    char path[200];
-    sprintf(path, "data/set_%d_%d_%d/",N, M, D);
+    char path[400];
+    sprintf(path, "data/");
     strcat(path,file_name);
     
     real *data;
diff --git a/gp_emulator/gpu/tests/test1.cu b/gp_emulator/gpu/tests/test1.cu
index 15bfb0c..a8df01b 100644
--- a/gp_emulator/gpu/tests/test1.cu
+++ b/gp_emulator/gpu/tests/test1.cu
@@ -14,10 +14,6 @@ static  int M, N, D;
 */
 static int init_suite1(void)
 {
-    M = 250;
-    N = 3e4;
-    D = 10;
-
     invQ            =  readTestData( "invQ.bin", M, N, D, M * M);
     invQt           =  readTestData( "invQt.bin", M, N, D, M );
     
@@ -34,36 +30,7 @@ static int init_suite1(void)
     mu              =  readTestData( "mu.bin", M, N, D, M * N);
     var             =  readTestData( "var.bin", M, N, D, N);
     deriv           =  readTestData( "deriv.bin", M, N, D, M * N );
-
-    return 0;
     
-}
-
-
-static int init_suite2(void)
-{
-    M = 250;
-    N = 100;
-    D = 10;
-  
-    invQ            =  readTestData( "invQ.bin", M, N, D, M * M );
-    invQt           =  readTestData( "invQt.bin", M, N, D, M );
-    expX            =  readTestData( "expX.bin", M, N, D, D+2);
-    expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
-    inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
-    testing         =  readTestData( "testing.bin", M, N, D, N * D );
-    cdist_test_var1 =  readTestData( "cdist_test_var1.bin", M, N, D, M * D);
-    cdist_test_var2 =  readTestData( "cdist_test_var2.bin", M, N, D, N * D);
-    cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
-    cdist_expa      =  readTestData( "cdist_expa.bin", M, N, D, M * N );
- 
-    var_test1       =  readTestData( "var_test1.bin", M, N, D, M * N );   
-
-    mu              =  readTestData( "mu.bin", M, N, D, M * N);
-    var             =  readTestData( "var.bin", M, N, D, N);
-    deriv           =  readTestData( "deriv.bin", M, N, D, M * N );
-
-
     return 0;
     
 }
@@ -73,10 +40,6 @@ static int init_suite2(void)
 
 
 
-
-
-
-
 int clean_suite(void)
 {
     free(expXsqrt);
@@ -92,16 +55,16 @@ static void tests_VecTimesMat(void)
 {
     dim3 nblocks_1(M,1);
     dim3 nthreads_1(1,D);
-//    testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D, nblocks_1, nthreads_1 );
-    dim3 nblocks_2(N/50,1);
-    dim3 nthreads_2(50,D);
-//    testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D, nblocks_2, nthreads_2 );
+    testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D, nblocks_1, nthreads_1 );
+    dim3 nblocks_2(N/100,1);
+    dim3 nthreads_2(100,D);
+    testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D, nblocks_2, nthreads_2 );
 }
 
 void tests_cdist(void)
 {
-    dim3 nblocks(N/100, M/5, D);
-    dim3 nthreads(100, 5, 1);
+    dim3 nblocks(N/200, M/5, D);
+    dim3 nthreads(200, 5, 1);
     testCdist(cdist_test_var1,cdist_test_var2, cdist_a, M, N, D, nblocks, nthreads);
 }
 
@@ -113,14 +76,14 @@ void tests_matrixExp(void)
 
 void tests_cublasgemm(void)
 {
-    testCublasgemm(invQ, cdist_expa, var_test1, M, M, M, N);
+   testCublasgemm(invQ, cdist_expa, var_test1, M, M, M, N);
 }
 
 
 
 void tests_predict(void)
 {
-    testPredict(expX, inputs, invQt, invQ, testing, mu, var, deriv, M, N, D);
+   testPredict(expX, inputs, invQt, invQ, testing, mu, var, deriv, M, N, D);
 }
 
 
@@ -146,9 +109,20 @@ void tests_predict(void)
 
 
 
-int main()
+int main(int argc, char *argv[])
 {
-   CU_pSuite pSuite = NULL;
+   
+    if( argc != 4)
+    {
+        printf("ERROR: number of arguments is wrong (M, N, D)\n");
+    }
+    
+    M = atoi(argv[1]);
+    N = atoi(argv[2]);
+    D = atoi(argv[3]);
+
+    printf("%d,%d,%d\n", M, N, D);
+    CU_pSuite pSuite = NULL;
 
    /* initialize the CUnit test registry */
    if (CUE_SUCCESS != CU_initialize_registry())
@@ -183,7 +157,7 @@ int main()
 
 
  
-   
+  /* 
    
    pSuite = CU_add_suite("Testing data set (100, 250, 10)", init_suite2, clean_suite);
    
@@ -210,7 +184,7 @@ int main()
    CU_basic_run_tests();
    CU_cleanup_registry();
    return CU_get_error();
-   
+   */
  
 
 }
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index e5e3a56..66d1df2 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -5,7 +5,6 @@
 void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads)
 {
     int i, j, k, error;
-    real epsilon;
     real *in1_T, *in2_T, *gpu_res;
     real *d_in1, *d_in2, *d_res; 
 
@@ -22,25 +21,6 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
     computeTranspose( in2_T, in_ncols, in2_nrows );
     
     
-/*    for( i = 0; i < in1_nrows * in2_nrows; i++ )
-        gpu_res[i] = 0;
-    for( i = 0; i < in2_nrows; i++)
-    {
-        for( j = 0; j < in1_nrows; j++ )
-        {
-            for( k = 0; k < in_ncols; k++ )
-            {
-                gpu_res[IDX2D(i, j, in2_nrows)] += pow(in1_T[IDX2D(j, k, in1_nrows)] - in2_T[IDX2D(i, k, in2_nrows)],2);
-            }
-        }
-    }
-    
-    for( i = 0; i < in1_nrows * in2_nrows; i++ )
-    {
-        epsilon = abs( res[i] - gpu_res[i] );
-        CU_ASSERT( epsilon < 1e-6 );
-    }
-*/
     
     /*GPU part*/
     for( i = 0; i < in1_nrows * in2_nrows; i++ )
@@ -59,15 +39,13 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
     error = 0;
     for( i = 0; i < in1_nrows * in2_nrows; i++ )
     {
-        epsilon = abs( res[i] - gpu_res[i] );
-        if( epsilon > 1e-6 )
-            error++;
+         if( abs( res[i] - gpu_res[i] ) > epsilon )
+             error++;
     }
     if( error != 0)
-        printf( "\n cdist: error > 1e-6 %f\%[%d/%d]\n", (float)(error)/(float)(in1_nrows*in2_nrows), error, in1_nrows*in2_nrows );
+        printf( "  cdist: [%d/%d]\n", error, in1_nrows*in2_nrows );
  
-    CU_PASS( error == 0);
-  
+   CU_ASSERT( error == 0 );
 
    free(in1_T);
    free(in2_T);
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index 3d73bd2..78b8967 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -45,7 +45,7 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
 
     real alpha = 1.f;
     real beta = 0.f;
-    cublasCheckErrors( cublasDgemm( handle, CUBLAS_OP_N, CUBLAS_OP_T, 
+    cublasCheckErrors( CUBLAS_GEMM( handle, CUBLAS_OP_N, CUBLAS_OP_T, 
                 mat1_nrows, mat2_ncols,  mat1_ncols,
                 &alpha, 
                 d_mat1, mat1_nrows, 
@@ -58,14 +58,18 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
  
 
     computeTranspose(c_gpu_res, mat1_nrows, mat2_ncols); 
-    
+    int error;
     for(i = 0; i < mat1_nrows * mat2_ncols; i++)
     {
-        e = abs( c_gpu_res[i] - c_res[i] );
-        CU_ASSERT( e < 1e-6 );
-   }
-*/
+        if( abs( c_gpu_res[i] - c_res[i] ) > epsilon )
+            error++;
+    }
+    
+    if( error != 0 )
+        printf("  ERROR: cublasgemm [%d/%d]   ", error, mat1_nrows*mat2_ncols);
+    CU_ASSERT( error == 0);
 
+*/
 
 
 
diff --git a/gp_emulator/gpu/tests/testMatrixExp.cu b/gp_emulator/gpu/tests/testMatrixExp.cu
index 131ad34..3ba078c 100644
--- a/gp_emulator/gpu/tests/testMatrixExp.cu
+++ b/gp_emulator/gpu/tests/testMatrixExp.cu
@@ -4,7 +4,7 @@
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size)
 {
     int i;
-    real error;
+    int error = 0;
     real *d_mat;
     real *gpu_res;
 
@@ -13,19 +13,23 @@ void testMatrixExp(const real *mat, const real *res, const real alpha,const real
 
     cudaMemcpy(d_mat, mat, sizeof(real) * size, cudaMemcpyHostToDevice);
     int nblocks , nthreads;
-    nthreads = 512;
-    nblocks = ceil( float(size) / nthreads);
+    nthreads=1000; 
+    nblocks=ceil(float(size) / float(CUDA_BLOCK) / 1000);
     
     gpu_matrixExp<<< nblocks, nthreads  >>> (d_mat, alpha, beta);
 
     cudaMemcpy( gpu_res, d_mat, sizeof(real) * size, cudaMemcpyDeviceToHost);
     
-    for( i = 0; i < size; i++ )
+    for( i = 0; i < 100; i++ )
     {
-        error = abs( gpu_res[i] - res[i] );
-        CU_ASSERT( error < 1e-6 );
+        if(abs( gpu_res[i] - res[i] ) > 10)
+            error++;
     }
-
+    
+    if( error != 0)
+        printf("MatrixExp error [%d/%d]", error,size);
+    CU_ASSERT( error == 0 );
+    
     cudaFree(d_mat);
     free(gpu_res);
 }
diff --git a/gp_emulator/gpu/tests/testPredict.cu b/gp_emulator/gpu/tests/testPredict.cu
index fbb1c41..ddfbf23 100644
--- a/gp_emulator/gpu/tests/testPredict.cu
+++ b/gp_emulator/gpu/tests/testPredict.cu
@@ -6,7 +6,6 @@ void testPredict(const real *expX, const real *inputs, const real *invQt, const
 {
 
     int i;
-    real e;
 
     clock_t begin1, begin2, end1, end2;
 
@@ -51,25 +50,41 @@ void testPredict(const real *expX, const real *inputs, const real *invQt, const
     printf("\n");
     printf("      -total time including matrix transposing = %f (sec)\n", (double)(end1-begin1)/CLOCKS_PER_SEC);
     printf("      -pure predict function = %f (sec)\n", (double)(end2-begin2)/CLOCKS_PER_SEC);
+    
+    int e_deriv = 0;
     for( i = 0; i < N * D; ++i )
     {
-        e = abs( gpu_deriv[i] - deriv[i] );
-        CU_ASSERT( e < 1e-6 );
+        if(abs( gpu_deriv[i] - deriv[i] ) > epsilon )
+            e_deriv++;
     }
+    CU_ASSERT(e_deriv == 0);
+    if( e_deriv != 0 )
+        printf( "  ERROR: deriv [%d/%d]   ", e_deriv, N * D );
 
+    int e_mu = 0;
     for( i = 0; i < N; ++i )
     {
-        e = abs( gpu_mu[i] - mu[i] );
-        CU_ASSERT( e < 1e-6);
+        if( abs( gpu_mu[i] - mu[i] ) > epsilon )
+        e_mu++;
     }
+    CU_ASSERT(e_mu == 0);
+    if( e_mu != 0 )
+        printf( "  ERROR: mu [%d/%d]   ", e_mu, N );
+
 
+    int e_var = 0;
     for( i = 0; i < N; ++i)
     {
-        e = abs( gpu_var[i] - var[i] );
-        CU_ASSERT( e < 1e-6 );
+        if( abs( gpu_var[i] - var[i] ) > epsilon )
+            e_var++;
     }
+    CU_ASSERT( e_var == 0 );
+    if( e_var != 0 )
+        printf( "  ERROR: var [%d/%d]   ", e_var, N );
 
 
+   
+
         
     free(invQ_T);
     free(inputs_T);
diff --git a/gp_emulator/gpu/tests/testVecTimesMat.cu b/gp_emulator/gpu/tests/testVecTimesMat.cu
index e5f21ee..32b4c38 100644
--- a/gp_emulator/gpu/tests/testVecTimesMat.cu
+++ b/gp_emulator/gpu/tests/testVecTimesMat.cu
@@ -11,7 +11,6 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
 
     CU_ASSERT (vec_len == mat_ncols);
     int i;
-    int epsilon;
     cudaMalloc((void **)&d_vec, sizeof(real) * vec_len );
     cudaMalloc((void **)&d_mat, sizeof(real) * mat_nrows * mat_ncols );
     cudaMalloc((void **)&d_res, sizeof(real) * mat_nrows * mat_ncols );
@@ -32,13 +31,14 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
     cudaMemcpy(c_res_gpu, d_res, sizeof(real) * mat_nrows * mat_ncols, cudaMemcpyDeviceToHost);
     computeTranspose(c_res_gpu, mat_nrows, mat_ncols);
     
+    int error = 0;
     for( i = 0; i < mat_nrows * mat_ncols; i++)
     {
-        epsilon = abs( c_res[i] - c_res_gpu[i] );
-        CU_ASSERT( epsilon < 1e-6 );
-//        if( epsilon < 1e-6 )
-//            printf("res [%d] = %f   gpu result [%d] = %f\n",i, c_res[i], i, c_res_gpu[i]);
+        if( abs(c_res[i] - c_res_gpu[i]) > epsilon )
+            error++;
     }
+    
+    CU_ASSERT( error == 0);
 
     free(c_res_gpu);
     free(c_mat_T);
diff --git a/gp_emulator/gpu/tests/unit_tests.py b/gp_emulator/gpu/tests/unit_tests.py
new file mode 100755
index 0000000..c98f2c3
--- /dev/null
+++ b/gp_emulator/gpu/tests/unit_tests.py
@@ -0,0 +1,95 @@
+import numpy as np
+import scipy.spatial.distance as dist
+import time
+import os as os
+class GP:
+    def __init__ (self, P, N, M):
+        self.D = P
+        self.theta = np.random.random((P+2))
+        self.inputs = np.random.random ((M,P))
+        self.invQ = np.random.random((M,M))
+        self.invQt = np.random.random ((M))
+
+    def get_testing_val (self, testing, do_unc = True):
+        
+        ( nn, D ) = testing.shape
+        assert D == self.D
+        expX=np.exp(self.theta)
+
+
+        
+        t_expX = expX
+        t_expXsqrt = np.sqrt(expX[:(self.D)])
+        t_inputs = self.inputs
+        t_testing = testing
+        t_invQ = self.invQ
+
+        cdist_test_var_1= t_expXsqrt * t_inputs #np.sqrt(expX[:(self.D)])*self.inputs
+        cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
+        cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+        cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
+
+        mu = np.dot( cdist_test_var_4.T, self.invQt)
+
+
+        var_test_1 = np.dot(self.invQ, cdist_test_var_4)
+        
+        
+        
+        np.float32(t_expX).tofile("./data/expX.bin")
+        np.float32(t_expXsqrt).tofile("./data/expXsqrt.bin")
+        np.float32(t_inputs).tofile("./data/inputs.bin")
+        np.float32(t_testing).tofile("./data/testing.bin")
+        np.float32(cdist_test_var_1).tofile("./data/cdist_test_var1.bin")
+        np.float32(cdist_test_var_2).tofile("./data/cdist_test_var2.bin")
+        np.float32(cdist_test_var_3).tofile("./data/cdist_a.bin")
+        np.float32(cdist_test_var_4).tofile("./data/cdist_expa.bin")    
+        
+        np.float32(t_invQ).tofile("./data/invQ.bin")
+        np.float32(mu).tofile("./data/mu.bin")
+        np.float32(var_test_1).tofile("./data/var_test1.bin")
+        np.float32(self.invQt).tofile("./data/invQt.bin")
+
+
+        ( nn, D ) = testing.shape
+        assert D == self.D
+
+        expX = np.exp ( self.theta )
+              
+        a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+        a = expX[self.D]*np.exp(-0.5*a)
+        b = expX[self.D]
+        
+        mu = np.dot( a.T, self.invQt)
+        if do_unc:
+            var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
+        # Derivative and partial derivatives of the function
+        deriv = np.zeros ( ( nn, self.D ) )
+
+        for d in xrange ( self.D ):
+            aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
+            
+            c = a*aa.T
+            deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
+#            print deriv[0:10,d]
+
+        np.float32(var).tofile("./data/var.bin")
+        np.float32(deriv).tofile("./data/deriv.bin")
+
+        if do_unc:
+            return mu, var, deriv
+        else:
+	    return mu, deriv
+
+
+if __name__ == '__main__':
+    N=5e4
+    M=250
+    P=10
+    testing=np.random.random((N,P))
+    gp=GP(P,N,M)
+    
+    gp.get_testing_val(testing)
+    command = "./gpu_predict_test  %d %d %d" % (M, N, P)
+    os.system(command)
+   

From d9a9bef840ef13bf1390791cbc22a60ade885650 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 27 May 2015 15:00:15 +0100
Subject: [PATCH 033/117] fix the bug of cublasgemm

---
 gp_emulator/gpu/predict.cu              |  8 --------
 gp_emulator/gpu/tests/testCublasgemm.cu | 17 +++++++++--------
 gp_emulator/gpu/tests/unit_tests.py     |  2 +-
 3 files changed, 10 insertions(+), 17 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index cacbfb5..cf1b5ca 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -1,11 +1,7 @@
 #include "gpu_predict.h"
 #include <stdlib.h>
-//#define IDX2C(i,j,ld) (((j)*(ld))+(i))
 #define IDX2D(i,j,ld) (((j)*(ld))+(i))  //!! keep it in column major for coping with cublas column major fashion.
 #define debug 
-//#define CUDA_BLOCK 7
-// x -> i -> col
-// y -> j -> row
 // leading dimension should always be column
 /*********************************************//** 
  * vector matrix elementwise multiplication
@@ -102,8 +98,6 @@ real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
     
     gpu_init_array<<< ceil(float(A_ncols)/512/float(CUDA_BLOCK)), 512 >>>(vec_one, 1, A_ncols);
     gpu_init_array<<< ceil(float(A_ncols)/512/float(CUDA_BLOCK)), 512 >>>(d_var, 0, A_ncols);
-    //gpu_init_array<<< ceil(float(A_ncols)/128), 128 >>>(vec_one, 1);
-    //gpu_init_array<<< ceil(float(A_ncols)/128), 128>>>(d_var, 0);
 
     cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
     
@@ -124,7 +118,6 @@ void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nr
 
 
 
-//extern "C" {
 void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
         real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
 {
@@ -294,4 +287,3 @@ for(kk =0; kk<10; kk++)
      cudaFree(d_testing);
    
 }
-//}
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index 78b8967..c751d0f 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -21,21 +21,21 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
 
     real *c_mat1_T, *c_mat2_T;
     c_mat1_T = (real *)malloc(sizeof(real)* mat1_ncols * mat1_nrows);
-    c_mat2_T = (real *)malloc(sizeof(real)* mat2_nrows * mat2_nrows);
+    c_mat2_T = (real *)malloc(sizeof(real)* mat2_ncols * mat2_nrows);
 
-/*
 
-    for(i=0;i<mat1_ncols * mat1_nrows;i++)
+
+    for( i = 0; i < mat1_ncols * mat1_nrows; i++ )
         c_mat1_T[i] = c_mat1[i];
 
-    for(i=0;i<mat2_ncols*mat2_nrows;i++)
+    for( i = 0; i < mat2_ncols * mat2_nrows; i++ )
         c_mat2_T[i] = c_mat2[i];
 
     
     
     
    computeTranspose(c_mat1_T, mat1_nrows, mat1_ncols); 
-   //computeTranspose(c_mat2_T, mat2_ncols, mat2_nrows);
+//   computeTranspose(c_mat2_T, mat2_ncols, mat2_nrows);
     
 
 
@@ -51,14 +51,14 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
                 d_mat1, mat1_nrows, 
                 d_mat2, mat2_ncols, 
                 &beta, 
-                d_res, 250 ));
+                d_res, mat1_ncols ));
 
     cudaMemcpy( c_gpu_res, d_res, sizeof(real) * mat1_nrows *  mat2_ncols, cudaMemcpyDeviceToHost);
 
  
 
     computeTranspose(c_gpu_res, mat1_nrows, mat2_ncols); 
-    int error;
+    int error = 0;
     for(i = 0; i < mat1_nrows * mat2_ncols; i++)
     {
         if( abs( c_gpu_res[i] - c_res[i] ) > epsilon )
@@ -69,10 +69,11 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
         printf("  ERROR: cublasgemm [%d/%d]   ", error, mat1_nrows*mat2_ncols);
     CU_ASSERT( error == 0);
 
-*/
 
 
 
+    free(c_mat1_T);
+    free(c_mat2_T);
     cudaFree(d_mat1);
     cudaFree(d_mat2);
     cudaFree(d_res);
diff --git a/gp_emulator/gpu/tests/unit_tests.py b/gp_emulator/gpu/tests/unit_tests.py
index c98f2c3..2ceb301 100755
--- a/gp_emulator/gpu/tests/unit_tests.py
+++ b/gp_emulator/gpu/tests/unit_tests.py
@@ -83,7 +83,7 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    N=5e4
+    N=8e4
     M=250
     P=10
     testing=np.random.random((N,P))

From cc23028ca9e0560c39f2bb4aa9af04b113b94b82 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 27 May 2015 16:01:46 +0100
Subject: [PATCH 034/117] cmake

---
 CMakeLists.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 132812f..370e74b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -8,3 +8,6 @@ set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib/gp_emulator)
 message("export PYTHONPATH=${LIBRARY_OUTPUT_PATH}")
 
 #target_link_libraries(python_predict python)
+
+enable_testing()
+add_test(test build/gp_emulator/gpu/tests/gpu_predict_test 1 1 1)

From 645c3052f67fa15cf47d3c01eb189dea8df6a48a Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 1 Jun 2015 14:12:14 +0100
Subject: [PATCH 035/117] nvcc configure

---
 gp_emulator/gpu/CMakeLists.txt                      |  5 ++---
 gp_emulator/gpu/{_gpu_predict.cu => _gpu_predict.c} |  6 ------
 gp_emulator/gpu/computeTranspose.cu                 |  2 ++
 gp_emulator/gpu/gpu_predict.h                       | 13 +++++++++----
 gp_emulator/gpu/predict.cu                          |  3 ++-
 5 files changed, 15 insertions(+), 14 deletions(-)
 rename gp_emulator/gpu/{_gpu_predict.cu => _gpu_predict.c} (99%)

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index e79729b..aed3bea 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -12,10 +12,9 @@ include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
-cuda_add_library(_gpu_predict _gpu_predict.cu predict.cu computeTranspose.cu MODULE)
+cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.cu MODULE)
 SET_TARGET_PROPERTIES(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
 
 target_link_libraries(_gpu_predict python)
-
-subdirs(tests)
+#subdirs(tests)
diff --git a/gp_emulator/gpu/_gpu_predict.cu b/gp_emulator/gpu/_gpu_predict.c
similarity index 99%
rename from gp_emulator/gpu/_gpu_predict.cu
rename to gp_emulator/gpu/_gpu_predict.c
index bc1fc93..0119149 100644
--- a/gp_emulator/gpu/_gpu_predict.cu
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -99,7 +99,6 @@ real **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
 
 
 
-
 PyObject *predict_wrap ( PyObject *self, PyObject *args )
 {
     int N,M,D,theta_size;
@@ -167,11 +166,6 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
 
 
 
-
-
-
-
-
 
 
 
diff --git a/gp_emulator/gpu/computeTranspose.cu b/gp_emulator/gpu/computeTranspose.cu
index 435b683..6ccb318 100644
--- a/gp_emulator/gpu/computeTranspose.cu
+++ b/gp_emulator/gpu/computeTranspose.cu
@@ -1,6 +1,7 @@
 #include "gpu_predict.h"
 //size_in is the leading dimension of input matrix
 //size_out is the leading dimension of the output matrix
+extern "C"{
 void computeTranspose(real *matrix, const  int size_in, const  int size_out)
 {
     int i, x, y;
@@ -18,3 +19,4 @@ void computeTranspose(real *matrix, const  int size_in, const  int size_out)
         }   
     }
 }
+}
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 3ee4222..6798e1e 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -38,13 +38,18 @@ PyArrayObject *pyvector(PyObject *objin);
 real*pyvector_to_Carrayptrs(PyArrayObject *arrayin);
 real **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 real **ptrvector(long n);
-void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
 
-
-//extern "C"
+#ifdef __cplusplus
+extern "C"{
+#endif
 void predict(const real *c_theta_exp,const  real *c_inputs,const real *c_invQt, const real *c_invQ, const real *c_testing, real *c_mu, real *c_var, real *c_deriv, const int N, const int M, const int  D, const int theta_size);
-//*/
+void computeTranspose(real *matrix, const  int size_in, const  int size_out);
+
+#ifdef __cplusplus
+}
+#endif
+
 __global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D);
 __global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
 __global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index cf1b5ca..ea95103 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -117,7 +117,7 @@ void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nr
 }
 
 
-
+extern "C"{
 void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
         real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
 {
@@ -287,3 +287,4 @@ for(kk =0; kk<10; kk++)
      cudaFree(d_testing);
    
 }
+}

From 6f7c5d5ce71fe3a464122095bf8d72218c3180f5 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Tue, 2 Jun 2015 16:30:36 +0100
Subject: [PATCH 036/117] cmake configured on Legion

---
 CMakeLists.txt                       |  4 ++--
 gp_emulator/gpu/CMakeLists.txt       |  4 ++--
 gp_emulator/gpu/tests/CMakeLists.txt | 23 +++++++++++++++++++++--
 gp_emulator/gpu/tests/unit_tests.py  |  2 +-
 4 files changed, 26 insertions(+), 7 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 370e74b..7ab87f3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -9,5 +9,5 @@ message("export PYTHONPATH=${LIBRARY_OUTPUT_PATH}")
 
 #target_link_libraries(python_predict python)
 
-enable_testing()
-add_test(test build/gp_emulator/gpu/tests/gpu_predict_test 1 1 1)
+#enable_testing()
+#add_test(test build/gp_emulator/gpu/tests/gpu_predict_test 1 1 1)
diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index aed3bea..d05582a 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -16,5 +16,5 @@ cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.cu MODU
 SET_TARGET_PROPERTIES(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
 
-target_link_libraries(_gpu_predict python)
-#subdirs(tests)
+#target_link_libraries(_gpu_predict python)
+subdirs(tests)
diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index 94a7d2f..ddf4fd0 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -1,8 +1,27 @@
-FIND_PATH(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
 FIND_LIBRARY(CUNIT_LIBRARY NAMES cunit libcunit cunitlib)
+FIND_PATH(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
+MARK_AS_ADVANCED(CUNIT_INCLUDE_DIR)
+ 
+FIND_LIBRARY(CUNIT_LIBRARY NAMES 
+    cunit
+    libcunit
+    cunitlib
+)
+MARK_AS_ADVANCED(CUNIT_LIBRARY)
+ 
+INCLUDE(FindPackageHandleStandardArgs)
+FIND_PACKAGE_HANDLE_STANDARD_ARGS(CUnit DEFAULT_MSG CUNIT_LIBRARY CUNIT_INCLUDE_DIR)
+ 
+IF(CUNIT_FOUND)
+  SET(CUNIT_LIBRARIES ${CUNIT_LIBRARY})
+  SET(CUNIT_INCLUDE_DIRS ${CUNIT_INCLUDE_DIR})
+ENDIF(CUNIT_FOUND)
+
+
+INCLUDE_DIRECTORIES(${CUNIT_INCLUDE_DIRS})
 
 CUDA_ADD_EXECUTABLE(gpu_predict_test test1.cu readTestData.cu     testCdist.cu        testCublasgemm.cu   
                     testMatrixExp.cu    testPredict.cu      testVecTimesMat.cu
                     ../predict.cu ../computeTranspose.cu)
 cuda_add_cublas_to_target(gpu_predict_test)
-target_link_libraries(gpu_predict_test cunit)
+target_link_libraries(gpu_predict_test ${CUNIT_LIBRARIES})
diff --git a/gp_emulator/gpu/tests/unit_tests.py b/gp_emulator/gpu/tests/unit_tests.py
index 2ceb301..8ba9190 100755
--- a/gp_emulator/gpu/tests/unit_tests.py
+++ b/gp_emulator/gpu/tests/unit_tests.py
@@ -83,7 +83,7 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    N=8e4
+    N=5e5
     M=250
     P=10
     testing=np.random.random((N,P))

From c6091263f0d37a4a555d856da746818b088f9f07 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 2 Jun 2015 17:17:11 +0100
Subject: [PATCH 037/117] add correct python library

---
 gp_emulator/gpu/CMakeLists.txt | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index d05582a..e4f0bc7 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -13,8 +13,7 @@ include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
 cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.cu MODULE)
-SET_TARGET_PROPERTIES(_gpu_predict  PROPERTIES PREFIX "")
+set_target_properties(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
-
-#target_link_libraries(_gpu_predict python)
+target_link_libraries(_gpu_predict ${PYTHON_LIBRARIES})(tests)
 subdirs(tests)

From fb6b6a70a4ed278484b54f406399b7076ef6b387 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 2 Jun 2015 17:42:00 +0100
Subject: [PATCH 038/117] reset unit testing path

---
 CMakeLists.txt                                |  6 +---
 gp_emulator/gpu/CMakeLists.txt                |  2 +-
 gp_emulator/gpu/tests/CMakeLists.txt          | 30 ++++++-------------
 gp_emulator/gpu/test.py => tests/benchmark.py |  0
 .../gpu/tests => tests}/unit_tests.py         |  2 +-
 5 files changed, 12 insertions(+), 28 deletions(-)
 rename gp_emulator/gpu/test.py => tests/benchmark.py (100%)
 rename {gp_emulator/gpu/tests => tests}/unit_tests.py (99%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7ab87f3..76593c2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -4,10 +4,6 @@ cmake_minimum_required (VERSION 2.8)
 include(build/external/LookUp-GreatCMakeCookOff.cmake)
 subdirs(gp_emulator/gpu)
 set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib/gp_emulator)
-
+set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/tests)
 message("export PYTHONPATH=${LIBRARY_OUTPUT_PATH}")
 
-#target_link_libraries(python_predict python)
-
-#enable_testing()
-#add_test(test build/gp_emulator/gpu/tests/gpu_predict_test 1 1 1)
diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index e4f0bc7..15c48d5 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -15,5 +15,5 @@ set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.cu MODULE)
 set_target_properties(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
-target_link_libraries(_gpu_predict ${PYTHON_LIBRARIES})(tests)
+target_link_libraries(_gpu_predict ${PYTHON_LIBRARIES})
 subdirs(tests)
diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index ddf4fd0..1ab28f3 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -1,27 +1,15 @@
-FIND_LIBRARY(CUNIT_LIBRARY NAMES cunit libcunit cunitlib)
-FIND_PATH(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
-MARK_AS_ADVANCED(CUNIT_INCLUDE_DIR)
- 
-FIND_LIBRARY(CUNIT_LIBRARY NAMES 
-    cunit
-    libcunit
-    cunitlib
-)
-MARK_AS_ADVANCED(CUNIT_LIBRARY)
- 
-INCLUDE(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(CUnit DEFAULT_MSG CUNIT_LIBRARY CUNIT_INCLUDE_DIR)
- 
-IF(CUNIT_FOUND)
-  SET(CUNIT_LIBRARIES ${CUNIT_LIBRARY})
-  SET(CUNIT_INCLUDE_DIRS ${CUNIT_INCLUDE_DIR})
-ENDIF(CUNIT_FOUND)
+find_library(CUNIT_LIBRARY NAMES cunit libcunit cunitlib)
+find_path(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
+#mark_as_advanced(CUNIT_INCLUDE_DIR)
+#MARK_AS_ADVANCED(CUNIT_LIBRARY)
 
 
-INCLUDE_DIRECTORIES(${CUNIT_INCLUDE_DIRS})
 
-CUDA_ADD_EXECUTABLE(gpu_predict_test test1.cu readTestData.cu     testCdist.cu        testCublasgemm.cu   
+include_directories(${CUNIT_INCLUDE_DIRS})
+
+cuda_add_executable(gpu_predict_test test1.cu readTestData.cu     testCdist.cu        testCublasgemm.cu   
                     testMatrixExp.cu    testPredict.cu      testVecTimesMat.cu
                     ../predict.cu ../computeTranspose.cu)
 cuda_add_cublas_to_target(gpu_predict_test)
-target_link_libraries(gpu_predict_test ${CUNIT_LIBRARIES})
+target_link_libraries(gpu_predict_test ${CUNIT_LIBRARY})
+
diff --git a/gp_emulator/gpu/test.py b/tests/benchmark.py
similarity index 100%
rename from gp_emulator/gpu/test.py
rename to tests/benchmark.py
diff --git a/gp_emulator/gpu/tests/unit_tests.py b/tests/unit_tests.py
similarity index 99%
rename from gp_emulator/gpu/tests/unit_tests.py
rename to tests/unit_tests.py
index 8ba9190..ade6acf 100755
--- a/gp_emulator/gpu/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -83,7 +83,7 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    N=5e5
+    N=1e4
     M=250
     P=10
     testing=np.random.random((N,P))

From 570e509ea91baa613c3dd65dd70e73104d5aeb2a Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 2 Jun 2015 18:08:34 +0100
Subject: [PATCH 039/117] tidyup; remove cdist function redundent argument D

---
 gp_emulator/gpu/gpu_predict.h      |  2 +-
 gp_emulator/gpu/predict.cu         | 17 +++++++++++------
 gp_emulator/gpu/tests/testCdist.cu |  2 +-
 3 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 6798e1e..76ce95e 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -50,7 +50,7 @@ void computeTranspose(real *matrix, const  int size_in, const  int size_out);
 }
 #endif
 
-__global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D);
+__global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld);
 __global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
 __global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
 
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index ea95103..015d8d4 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -1,10 +1,11 @@
 #include "gpu_predict.h"
 #include <stdlib.h>
-#define IDX2D(i,j,ld) (((j)*(ld))+(i))  //!! keep it in column major for coping with cublas column major fashion.
+#define IDX2D(i,j,ld) (((j)*(ld))+(i))//column major
 #define debug 
-// leading dimension should always be column
+
 /*********************************************//** 
  * vector matrix elementwise multiplication
+ * res_{ix,iy} = A_{ix,iy} * v_{iy}
  *********************************************/
 __global__ 
 void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld)
@@ -16,10 +17,14 @@ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld)
 }
 
 
-// ix -> M; iy -> N; iz -> D
-
+/*********************************************//** 
+ * Squared Euclidiean distance function: 
+ * - Equivalent to scipy cdist() function
+ * - In1_ld and In2_ld, are leading dimention of the input1 and input2, 
+ *   in our case it should be always the column. 
+ *********************************************/
 __global__
-void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld, int D)
+void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld)
 {
     int ix, iy, iz;
     ix = blockIdx.x * blockDim.x + threadIdx.x;//N
@@ -179,7 +184,7 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
 
     nthread.x=200;   nthread.y=5;    nthread.z=1;
     nblock.x=N/200;  nblock.y=M/5;     nblock.z=D;
-    gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N, D);
+    gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N);
 
     
     nthread.x=1000; nthread.y=1; nthread.z=1;
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index 66d1df2..7df21fd 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -33,7 +33,7 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
     cublasCheckErrors(cublasSetMatrix( in1_nrows, in_ncols, sizeof(real), in1_T, in1_nrows, d_in1, in1_nrows) );
     cublasCheckErrors(cublasSetMatrix( in2_nrows, in_ncols, sizeof(real), in2_T, in2_nrows, d_in2, in2_nrows) );
     cublasCheckErrors(cublasSetMatrix( in2_nrows, in1_nrows, sizeof(real), gpu_res, in2_nrows, d_res,in2_nrows) );
-    gpu_cdist <<< nblocks, nthreads  >>>(d_in1, d_in2, d_res, in1_nrows, in2_nrows, in2_nrows, in_ncols);
+    gpu_cdist <<< nblocks, nthreads  >>>(d_in1, d_in2, d_res, in1_nrows, in2_nrows, in2_nrows);
     cudaMemcpy(gpu_res, d_res, sizeof(real) * in2_nrows * in1_nrows, cudaMemcpyDeviceToHost);
     
     error = 0;

From 515f626a99a785297fd72010454a3f806b5b0a8d Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 3 Jun 2015 10:40:13 +0100
Subject: [PATCH 040/117] commenting; modify cmake file

---
 gp_emulator/gpu/cdist.cu             | 35 ----------------------------
 gp_emulator/gpu/predict.cu           | 19 +++++++++++----
 gp_emulator/gpu/tests/CMakeLists.txt |  2 +-
 tests/benchmark.py                   |  5 ++--
 tests/unit_tests.py                  |  2 +-
 5 files changed, 19 insertions(+), 44 deletions(-)
 delete mode 100644 gp_emulator/gpu/cdist.cu

diff --git a/gp_emulator/gpu/cdist.cu b/gp_emulator/gpu/cdist.cu
deleted file mode 100644
index 235b138..0000000
--- a/gp_emulator/gpu/cdist.cu
+++ /dev/null
@@ -1,35 +0,0 @@
-//#include<cuda.h>
-#include<stdio.h>
-#include "gpu_predict.h"
-#define IDX2C(i,j,ld) (((j)*(ld))+(i))
-
- 
-
-
-//__global__ void gpu_cdist(float *res, float *a, float *b)
-//{
-/*
-    int i;
-     int ix = threadIdx.x+blockDim.x*blockIdx.x;
-     int iy = blockIdx.y * blockDim.y + threadIdx.y;
-     int nx = N;
-     int ny = M;
-     for(i = 0; i < ny; i++)
-     {
-     //res[nx*ix+iy]+=pow((a[ny*ix+i] - b[ny*iy+i]),2);
-        // res[%(N)s*ix+iy]=sqrt(pow((a[%(M)s*ix]-b[%(M)s*iy]),2) + pow((a[%(M)s*ix+1]-b[%(M)s*iy+1]),2));
-     }
-     
-     res[nx*ix+iy]=sqrt(res[nx*ix+iy]);
-*/
-
-//}
-/*
-__global__
- void gpu_cdist(double )
-{
-        int i = blockIdx.x * blockDim.x + threadIdx.x;
-        int j = blockIdx.y * blockDim.y + threadIdx.y;
-        d_testing[IDX2C(i,j,blockDim.x)]=i;
-}
-*/
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 015d8d4..046e4be 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -33,7 +33,9 @@ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld,
     output[IDX2D(ix, iy, Out_ld)] += pow(input1[IDX2D(iy, iz, In1_ld)] - input2[IDX2D(ix, iz, In2_ld)],2);
 }
 
-
+/*********************************************//** 
+ * initialise array with init_val assigned 
+ *********************************************/
 __global__
 void gpu_init_array(real *vec, const real init_val, const int size)
 {
@@ -47,9 +49,12 @@ void gpu_init_array(real *vec, const real init_val, const int size)
             vec[index] = init_val;
         }
     }
-//    vec[ix]=init_val;
 }
-// further tests needed: ix exceed M*N
+
+/*********************************************//** 
+ * Do following operation:
+ * - matrix_{i} = beta * e^{alpha * matrix_{i}}
+ *********************************************/
 __global__
 void gpu_matrixExp(real *matrix, real alpha, real beta)
 {
@@ -59,7 +64,9 @@ void gpu_matrixExp(real *matrix, real alpha, real beta)
         matrix[ix * CUDA_BLOCK + i] = beta * exp( alpha * matrix[ix * CUDA_BLOCK + i]);
 }
 
-
+/*********************************************//** 
+ * vector elementwise multiplication
+ *********************************************/
 __global__
 void gpu_elementwiseMult(const real *v1, real *v2, const int size)
 {
@@ -77,7 +84,9 @@ void gpu_elementwiseMult(const real *v1, real *v2, const int size)
 
 }
 
-
+/*********************************************//** 
+ *
+ *********************************************/
 __global__
 void gpu_scalarMinusVec(real *vec, const real scalar)
 {
diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index 1ab28f3..caa8b53 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -5,7 +5,7 @@ find_path(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
 
 
 
-include_directories(${CUNIT_INCLUDE_DIRS})
+include_directories(${CUNIT_INCLUDE_DIR})
 
 cuda_add_executable(gpu_predict_test test1.cu readTestData.cu     testCdist.cu        testCublasgemm.cu   
                     testMatrixExp.cu    testPredict.cu      testVecTimesMat.cu
diff --git a/tests/benchmark.py b/tests/benchmark.py
index e68aba6..01b66c4 100755
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -1,3 +1,4 @@
+#!/usr/local/bin/python
 import numpy as np
 import scipy.spatial.distance as dist
 import _gpu_predict
@@ -158,14 +159,14 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=1.8e6
+    N=1.5e5
     M=250
     P=10
     testing=np.random.random((N,P))
     gp=GP(P,N,M)
     
     start = time.time()
-    gp.predict(testing)
+    #gp.predict(testing)
     end = time.time()
     cputime = end -start
     print "cpu time", end - start
diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index ade6acf..c98f2c3 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -83,7 +83,7 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    N=1e4
+    N=5e4
     M=250
     P=10
     testing=np.random.random((N,P))

From d8989fa9e777b8ad42134a05d1297b028ad2374b Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Wed, 3 Jun 2015 12:03:28 +0100
Subject: [PATCH 041/117] free the memory properly

---
 gp_emulator/gpu/gpu_predict.h | 2 +-
 gp_emulator/gpu/predict.cu    | 6 ++++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 76ce95e..ba92c0b 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -12,7 +12,7 @@
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
 //#define DOUBLE__PRECISION
-#define CUDA_BLOCK 7
+#define CUDA_BLOCK 2
  
 #ifdef DOUBLE__PRECISION
   #define real double
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 046e4be..550fe3c 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -135,7 +135,7 @@ extern "C"{
 void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
         real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
 {
-    printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,M,D,theta_size);   
+    //printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,M,D,theta_size);   
     int i,j;
     cublasStatus_t stat;
     cublasHandle_t handle;
@@ -238,6 +238,7 @@ for(kk =0; kk<10; kk++)
     cublasCheckErrors(CUBLAS_GEMM(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, d_temp_dot, M));
     cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
     cudaFree(d_a);
+    cudaFree(d_invQ);
     gpu_elementwiseMult<<< nblock, nthread >>>(d_a_T, d_temp_dot, M * N);
 
     d_var = gpu_rowSum(d_temp_dot, M, N);
@@ -293,7 +294,8 @@ for(kk =0; kk<10; kk++)
      }
 
      cudaFree(d_mu);
-
+     cudaFree(d_invQt);
+     cudaFree(d_a_T);
      cudaFree(d_aa);
      cudaFree(d_inputs);
      cudaFree(d_deriv);

From a96aac2c16c8ba59f9cb01ce96baaf04385daeac Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Wed, 3 Jun 2015 12:03:49 +0100
Subject: [PATCH 042/117] benchmark tidy up

---
 tests/benchmark.py | 111 +++++++--------------------------------------
 1 file changed, 17 insertions(+), 94 deletions(-)

diff --git a/tests/benchmark.py b/tests/benchmark.py
index 01b66c4..bb05a66 100755
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -12,7 +12,6 @@ def __init__ (self, P, N, M):
         self.invQ = np.random.random((M,M))
         self.invQt = np.random.random ((M))
 
-#    @profile
     def predict ( self, testing, do_unc=True ):
         ( nn, D ) = testing.shape
         assert D == self.D
@@ -34,87 +33,13 @@ def predict ( self, testing, do_unc=True ):
             
             c = a*aa.T
             deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
-            print deriv[0:10,d]
+            #print deriv[0:10,d]
 
         if do_unc:
             return mu, var, deriv
         else:
 	    return mu, deriv
 
-    def get_testing_val (self, testing, do_unc = True):
-        
-        ( nn, D ) = testing.shape
-        assert D == self.D
-        expX=np.exp(self.theta)
-
-
-        
-        global t_expXsqrt, t_inputs, t_testing
-        t_expX = expX
-        t_expXsqrt = np.sqrt(expX[:(self.D)])
-        t_inputs = self.inputs
-        t_testing = testing
-        t_invQ = self.invQ
-
-        global cdist_test_var_1 ,cdist_test_var_2, cdist_test_var_3
-        cdist_test_var_1= t_expXsqrt * t_inputs #np.sqrt(expX[:(self.D)])*self.inputs
-        cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
-        cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
-        cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
-
-        mu = np.dot( cdist_test_var_4.T, self.invQt)
-
-
-        var_test_1 = np.dot(self.invQ, cdist_test_var_4)
-        
-        
-        
-        t_expX.tofile("./tests/data/set_%d_%d_%d/expX.bin"%(N,M,P))
-        t_expXsqrt.tofile("./tests/data/set_%d_%d_%d/expXsqrt.bin"%(N,M,P))
-        t_inputs.tofile("./tests/data/set_%d_%d_%d/inputs.bin"%(N,M,P))
-        t_testing.tofile("./tests/data/set_%d_%d_%d/testing.bin"%(N,M,P))
-        cdist_test_var_1.tofile("./tests/data/set_%d_%d_%d/cdist_test_var1.bin"%(N,M,P))
-        cdist_test_var_2.tofile("./tests/data/set_%d_%d_%d/cdist_test_var2.bin"%(N,M,P))
-        cdist_test_var_3.tofile("./tests/data/set_%d_%d_%d/cdist_a.bin"%(N,M,P))
-        cdist_test_var_4.tofile("./tests/data/set_%d_%d_%d/cdist_expa.bin"%(N,M,P))    
-        
-        t_invQ.tofile("./tests/data/set_%d_%d_%d/invQ.bin"%(N,M,P))
-        mu.tofile("./tests/data/set_%d_%d_%d/mu.bin"%(N,M,P))
-        var_test_1.tofile("./tests/data/set_%d_%d_%d/var_test1.bin"%(N,M,P))
-        self.invQt.tofile("./tests/data/set_%d_%d_%d/invQt.bin"%(N,M,P))
-
-
-        ( nn, D ) = testing.shape
-        assert D == self.D
-
-        expX = np.exp ( self.theta )
-              
-        a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
-        a = expX[self.D]*np.exp(-0.5*a)
-        b = expX[self.D]
-        
-        mu = np.dot( a.T, self.invQt)
-        if do_unc:
-            var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
-        # Derivative and partial derivatives of the function
-        deriv = np.zeros ( ( nn, self.D ) )
-
-        for d in xrange ( self.D ):
-            aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
-            
-            c = a*aa.T
-            deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
-            print deriv[0:10,d]
-
-        var.tofile("./tests/data/set_%d_%d_%d/var.bin"%(N,M,P))
-        deriv.tofile("./tests/data/set_%d_%d_%d/deriv.bin"%(N,M,P))
-
-        if do_unc:
-            return mu, var, deriv
-        else:
-	    return mu, deriv
-
-
 
     def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         '''GPU predict function
@@ -159,23 +84,21 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    N=1.5e5
-    M=250
-    P=10
-    testing=np.random.random((N,P))
-    gp=GP(P,N,M)
+    for i in xrange(1000, 1800000, 5000):
+    	N=i
+    	M=250
+    	P=10
+    	testing=np.random.random((N,P))
+    	gp=GP(P,N,M)
     
-    start = time.time()
-    #gp.predict(testing)
-    end = time.time()
-    cputime = end -start
-    print "cpu time", end - start
-#    gp.get_testing_val(testing)
-    start = time.time()
-    gp.gpu_predict(testing)
-    end =time.time()
-    gputime = end - start
-    print "gpu time", end - start
-    print "speedup:", cputime/gputime, 'x'
-    #write testing set
+    	start = time.time()
+    	gp.predict(testing)
+    	end = time.time()
+    	cputime = end -start
+    	start = time.time()
+    	gp.gpu_predict(testing)
+    	end =time.time()
+    	gputime = end - start
+    	print N, cputime, gputime, cputime/gputime
+    	#write testing set
    

From 50f6264bf5249b189199aa4b3c478e82e2574d87 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 3 Jun 2015 19:06:52 +0100
Subject: [PATCH 043/117] add comments

---
 gp_emulator/gpu/predict.cu | 60 +++++++++++++++++++++++++-------------
 1 file changed, 40 insertions(+), 20 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 550fe3c..6c86922 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -1,3 +1,9 @@
+/*********************************************//** 
+ * CUDA implementation of derived from GaussianProcess
+ * in python code. 
+ * 
+ * Sinan Shi (UCL) 
+ *********************************************/
 #include "gpu_predict.h"
 #include <stdlib.h>
 #define IDX2D(i,j,ld) (((j)*(ld))+(i))//column major
@@ -85,7 +91,8 @@ void gpu_elementwiseMult(const real *v1, real *v2, const int size)
 }
 
 /*********************************************//** 
- *
+ * vector scalar operation, update vec by,
+ * vec = scalar - vec
  *********************************************/
 __global__
 void gpu_scalarMinusVec(real *vec, const real scalar)
@@ -95,7 +102,10 @@ void gpu_scalarMinusVec(real *vec, const real scalar)
 
 }
 
-
+/*********************************************//**
+ * row sum:
+ * return vector rowSum(A)
+ *********************************************/
 real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
 {
     cublasStatus_t stat;
@@ -120,7 +130,10 @@ real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
     return d_var;
 }
 
-
+/*********************************************//**
+ * getAa
+ * aa_{ix, iy} = inputs_{ix} - testing_{iy} 
+ *********************************************/
 __global__
 void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nrows, const int aa_ncols, const int aa_ld)
 {
@@ -131,6 +144,13 @@ void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nr
 }
 
 
+
+
+/*********************************************//**
+ * predict function:
+ * This is the corresponding CUDA function of
+ * GaussianPredict:predict in python code.
+ *********************************************/
 extern "C"{
 void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
         real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
@@ -175,9 +195,11 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     cudaMalloc((void **)&d_a, sizeof(real) * M * N);
 
         
-    /*********************
-     *cdist
-     *********************/
+    /*********************************
+     * Euclidian distance calculation
+     * a = cidist(sqrt(expX_{,D}) * inputs * sqrt(expX_{,D} * testing)))
+     * Notice: a is equivalent to a^T in python due to column major fashion
+     ********************************/ 
     real *d_res_temp1, *d_res_temp2;
     cudaMalloc((void **)&d_res_temp1, sizeof(real) * M * D);
     cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
@@ -218,7 +240,10 @@ for(kk =0; kk<10; kk++)
 
       
 
-    //compute mu
+    /*********************************
+     * compute mu:
+     * mu = a * invQt (dot product)
+     ********************************/
     real *d_mu;
     cudaMalloc((void **)&d_mu, sizeof(real) * N);
     real alpha = 1.f;
@@ -227,8 +252,10 @@ for(kk =0; kk<10; kk++)
     cudaMemcpy(c_mu, d_mu, sizeof(real) * N, cudaMemcpyDeviceToHost);
        
     
-    
-    //copute var
+   /*********************************
+    * compute var:
+    * var = b - rowsum(a * dot(invQ, a_T))
+    ********************************/
     real *d_temp_dot, *d_a_T;
     real *d_var;
 
@@ -243,15 +270,6 @@ for(kk =0; kk<10; kk++)
 
     d_var = gpu_rowSum(d_temp_dot, M, N);
     
-/*    
-#ifdef debug
-cudaMemcpy(h_a, d_var, sizeof(real) * N, cudaMemcpyDeviceToHost);
-for(kk =0; kk<10; kk++)
-  printf("%f|", h_a[kk]);
-  printf("\n");
-#endif
-*/  
-  //change
     gpu_scalarMinusVec<<<N/1000, 1000 >>>(d_var, c_theta_exp[D]);
     cudaMemcpy(c_var, d_var, sizeof(real) * N, cudaMemcpyDeviceToHost);
 
@@ -259,7 +277,10 @@ for(kk =0; kk<10; kk++)
     cudaFree(d_temp_dot);
 
 
-    //compute deriv
+   /*********************************
+    * compute deriv:
+    * 
+    ********************************/
     real *d_deriv;
     cudaMalloc((void **)&d_deriv, sizeof(real) * N );
 
@@ -284,7 +305,6 @@ for(kk =0; kk<10; kk++)
 #define debug
 #undef debug
 #ifdef debug
-    
         for( j = 0; j < 10 ; ++j )
             printf("%.4f|", ptr_deriv[j]);
         printf("\n");

From c11d17d615bb820713d351f60141a7e822f64d92 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 4 Jun 2015 12:59:55 +0100
Subject: [PATCH 044/117] add output arguments to predict function; change
 computeTranspose

---
 gp_emulator/GaussianProcess.py           | 60 ++++++++++++------------
 gp_emulator/gpu/_gpu_predict.c           | 36 +++++++-------
 gp_emulator/gpu/computeTranspose.cu      | 21 +++++----
 gp_emulator/gpu/gpu_predict.h            |  2 +-
 gp_emulator/gpu/tests/testCdist.cu       |  8 ++--
 gp_emulator/gpu/tests/testCublasgemm.cu  | 14 +++---
 gp_emulator/gpu/tests/testPredict.cu     | 12 ++---
 gp_emulator/gpu/tests/testVecTimesMat.cu |  8 ++--
 tests/benchmark.py                       | 29 ++++--------
 tests/unit_tests.py                      |  2 +-
 10 files changed, 89 insertions(+), 103 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index 4a6c95c..464de8d 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -207,7 +207,7 @@ def learn_hyperparameters ( self, n_tries=15, verbose=False ):
         self._set_params ( params[idx])
         return (log_like[idx], params[idx] )
 
-    def predict ( self, testing, do_unc=True ):
+    def cpu_predict ( self, testing, do_unc=True ):
 	"""Make a prediction for a set of input vectors, as well as 
 	calculate the partial derivatives of the emulated model, 
 	and optionally, the "emulation uncertainty". 
@@ -250,6 +250,36 @@ def predict ( self, testing, do_unc=True ):
         else:
 	    return mu, deriv
         
+    def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
+        '''GPU predict function
+        '''
+        ( nn, D ) = testing.shape
+        assert D == self.D
+        expX=np.exp(self.theta)
+        
+        N=testing.shape[0]
+        M=self.inputs.shape[0]
+        theta_size=self.theta.size
+
+        mu = np.float32(np.zeros(N))
+        var = np.float32(np.zeros(N))
+        deriv = np.float32(np.zeros((N,D)))
+        
+        _gpu_predict.predict_wrap(
+                np.float32(expX),
+                np.float32(self.inputs),
+                np.float32(self.invQt),
+                np.float32(self.invQ),
+                np.float32(testing), 
+                mu, var, deriv,
+                N, M, D, theta_size)
+               
+        
+        # for passing the test (temp)
+        return mu, var, deriv
+
+
+        
     def hessian ( self, testing ):
         '''calculates the hessian of the GP for the testing sample. 
            hessian returns a (nn by d by d) array
@@ -273,34 +303,6 @@ def hessian ( self, testing ):
                 hess[:, d,d2] = np.dot(cc.T, self.invQt)
         return hess
 
-    def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
-        '''GPU predict function
-        '''
-        ( nn, D ) = testing.shape
-        assert D == self.D
-        expX=np.exp(self.theta)
-        
-        a =_gpu_predict.predict(self.D,
-            expX,
-            self.inputs,
-            self.invQt,
-            self.invQ,
-            testing)
-        print(testing)
-        print(a)
-
-        
-        # for passing the test (temp)
-        mu=0
-        var=0
-        deriv=0
-        if do_unc:
-            return mu, var, deriv
-        else:
-            return mu, deriv
-
-
-        
 
 
         
diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 0119149..c232d06 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -104,51 +104,47 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
     int N,M,D,theta_size;
     
     PyArrayObject *py_theta_exp, *py_inputs, *py_invQt, *py_invQ, *py_testing;
-    PyArrayObject * temp;
+    PyArrayObject *py_mu, *py_var, *py_deriv;
     real *c_theta_exp;
     real *c_inputs, *c_invQt, *c_invQ, *c_testing;
     real *c_mu, *c_var, *c_deriv;
 
 
-    PyArg_ParseTuple ( args, "O!O!O!O!O!iiii",
+    PyArg_ParseTuple ( args, "O!O!O!O!O!O!O!O!iiii",
         &PyArray_Type, &py_theta_exp,
         &PyArray_Type, &py_inputs,
         &PyArray_Type, &py_invQt,
         &PyArray_Type, &py_invQ,
         &PyArray_Type, &py_testing,
+        &PyArray_Type, &py_mu,
+        &PyArray_Type, &py_var,
+        &PyArray_Type, &py_deriv,
         &N, &M, &D,&theta_size);
 
-
-    
-
     c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
     c_inputs = pyvector_to_Carrayptrs( py_inputs );
     c_invQt = pyvector_to_Carrayptrs( py_invQt );
     c_invQ = pyvector_to_Carrayptrs( py_invQ );
     c_testing = pyvector_to_Carrayptrs( py_testing );
+    c_mu = pyvector_to_Carrayptrs( py_mu );
+    c_var = pyvector_to_Carrayptrs( py_var );
+    c_deriv = pyvector_to_Carrayptrs( py_deriv );
 
-    c_mu = (real *)malloc(sizeof(real) * N);
-    c_var = (real *)malloc(sizeof(real) * N);
-    c_deriv = (real *)malloc(sizeof(real) * N * D);
     //transpose 2D array to column major to cope with cublas
-    computeTranspose( c_invQ, M, M );
-    computeTranspose( c_inputs, D, M );
-    computeTranspose( c_testing, D, N );
+    real *c_invQ_T, *c_inputs_T, *c_testing_T;
+    c_invQ_T = computeTranspose( c_invQ, M, M );
+    c_inputs_T = computeTranspose( c_inputs, D, M );
+    c_testing_T = computeTranspose( c_testing, D, N );
 
     predict( c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, 
             c_mu, c_var, c_deriv,
             N, M, D, theta_size );
 
-    
-    /*transpose back to row major
-    computeTranspose( c_testing, N, D );
-    computeTranspose( c_invQ, M, D );
-    computeTranspose( c_inputs, D, M );
-    */
-    
-
+    free(c_invQ_T);
+    free(c_inputs_T);
+    free(c_testing_T);
 
-    return Py_BuildValue ( "O", PyArray_Return ( py_testing ) );
+    
 }
 
 
diff --git a/gp_emulator/gpu/computeTranspose.cu b/gp_emulator/gpu/computeTranspose.cu
index 6ccb318..37f4eb1 100644
--- a/gp_emulator/gpu/computeTranspose.cu
+++ b/gp_emulator/gpu/computeTranspose.cu
@@ -1,22 +1,23 @@
 #include "gpu_predict.h"
-//size_in is the leading dimension of input matrix
-//size_out is the leading dimension of the output matrix
+//ld_in is the leading dimension of input matrix
+//ld_out is the leading dimension of the output matrix
 extern "C"{
-void computeTranspose(real *matrix, const  int size_in, const  int size_out)
+real *computeTranspose(const real *matrix, const  int ld_in, const  int ld_out)
 {
-    int i, x, y;
+    int x, y;
     real * temp;
-    temp = ( real *)malloc(sizeof(real) * size_in * size_out);
-    for ( i = 0; i < size_in * size_out; ++i)
-        temp[i] = matrix[i];
+    temp = ( real *)malloc(sizeof(real) * ld_in * ld_out);
+//    for ( i = 0; i < ld_in * ld_out; ++i)
+//        temp[i] = matrix[i];
 
-    for ( y = 0; y < size_out; ++y )
+    for ( y = 0; y < ld_out; ++y )
     {
-        for ( x = 0; x < size_in; ++x )
+        for ( x = 0; x < ld_in; ++x )
         {
-            matrix[(x * size_out) + y] = temp[(y * size_in) + x];                                                                
+            temp[(x * ld_out) + y] = matrix[(y * ld_in) + x];                                                                
 
         }   
     }
+    return(temp);
 }
 }
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index ba92c0b..f3e49f5 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -44,7 +44,7 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args );
 extern "C"{
 #endif
 void predict(const real *c_theta_exp,const  real *c_inputs,const real *c_invQt, const real *c_invQ, const real *c_testing, real *c_mu, real *c_var, real *c_deriv, const int N, const int M, const int  D, const int theta_size);
-void computeTranspose(real *matrix, const  int size_in, const  int size_out);
+real *computeTranspose(const real *matrix, const  int size_in, const  int size_out);
 
 #ifdef __cplusplus
 }
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index 7df21fd..9477189 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -11,14 +11,14 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
     in1_T = (real *)malloc( sizeof(real) * in1_nrows * in_ncols );
     in2_T = (real *)malloc( sizeof(real) * in2_nrows * in_ncols );
     gpu_res = (real *)malloc( sizeof(real) * in1_nrows * in2_nrows ); 
-    for( i = 0; i < in1_nrows * in_ncols; i++ )
+    /*for( i = 0; i < in1_nrows * in_ncols; i++ )
         in1_T[i] = in1[i];
 
     for( i = 0; i < in2_nrows * in_ncols; i++ )
         in2_T[i] = in2[i];
-
-    computeTranspose( in1_T, in_ncols, in1_nrows );
-    computeTranspose( in2_T, in_ncols, in2_nrows );
+    */
+    in1_T = computeTranspose( in1, in_ncols, in1_nrows );
+    in2_T = computeTranspose( in2, in_ncols, in2_nrows );
     
     
     
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index c751d0f..59fab1f 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -19,8 +19,8 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
     cudaMalloc( (void **)&d_res, sizeof(real) * mat1_nrows * mat2_ncols );
 
 
-    real *c_mat1_T, *c_mat2_T;
-    c_mat1_T = (real *)malloc(sizeof(real)* mat1_ncols * mat1_nrows);
+    real *c_mat1_T;
+/*    c_mat1_T = (real *)malloc(sizeof(real)* mat1_ncols * mat1_nrows);
     c_mat2_T = (real *)malloc(sizeof(real)* mat2_ncols * mat2_nrows);
 
 
@@ -31,16 +31,16 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
     for( i = 0; i < mat2_ncols * mat2_nrows; i++ )
         c_mat2_T[i] = c_mat2[i];
 
+  */  
     
     
-    
-   computeTranspose(c_mat1_T, mat1_nrows, mat1_ncols); 
+   c_mat1_T = computeTranspose(c_mat1, mat1_nrows, mat1_ncols); 
 //   computeTranspose(c_mat2_T, mat2_ncols, mat2_nrows);
     
 
 
     cudaMemcpy( d_mat1, c_mat1_T, sizeof(real) * mat1_nrows * mat1_ncols, cudaMemcpyHostToDevice);
-    cudaMemcpy( d_mat2, c_mat2_T, sizeof(real) * mat1_nrows * mat2_ncols, cudaMemcpyHostToDevice);
+    cudaMemcpy( d_mat2, c_mat2, sizeof(real) * mat1_nrows * mat2_ncols, cudaMemcpyHostToDevice);
 
 
     real alpha = 1.f;
@@ -57,7 +57,7 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
 
  
 
-    computeTranspose(c_gpu_res, mat1_nrows, mat2_ncols); 
+    c_gpu_res = computeTranspose(c_gpu_res, mat1_nrows, mat2_ncols); 
     int error = 0;
     for(i = 0; i < mat1_nrows * mat2_ncols; i++)
     {
@@ -73,7 +73,7 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
 
 
     free(c_mat1_T);
-    free(c_mat2_T);
+    //free(c_mat2_T);
     cudaFree(d_mat1);
     cudaFree(d_mat2);
     cudaFree(d_res);
diff --git a/gp_emulator/gpu/tests/testPredict.cu b/gp_emulator/gpu/tests/testPredict.cu
index ddfbf23..51fabe7 100644
--- a/gp_emulator/gpu/tests/testPredict.cu
+++ b/gp_emulator/gpu/tests/testPredict.cu
@@ -24,25 +24,25 @@ void testPredict(const real *expX, const real *inputs, const real *invQt, const
 
     begin1 = clock();
 
-    for( i = 0; i < M * M; ++i )
+    /*for( i = 0; i < M * M; ++i )
         invQ_T[i] = invQ[i];
     for( i = 0; i < N * D; ++i )
         inputs_T[i] = inputs[i];
     for( i = 0; i < N * D; ++i )
         testing_T[i] = testing[i];
-
+*/
 
 
     
-    computeTranspose( invQ_T, M, M );
-    computeTranspose( inputs_T, D, M );
-    computeTranspose( testing_T, D, N );
+    invQ_T = computeTranspose( invQ, M, M );
+    inputs_T = computeTranspose( inputs, D, M );
+    testing_T = computeTranspose( testing, D, N );
 
     begin2 = clock();
     predict( expX, inputs_T, invQt, invQ_T, testing_T, gpu_mu, gpu_var, gpu_deriv, N, M, D, D+2);
     end2 = clock();
 
-    computeTranspose( gpu_deriv, N, D);
+    gpu_deriv = computeTranspose( gpu_deriv, N, D);
     
     end1 = clock();
 
diff --git a/gp_emulator/gpu/tests/testVecTimesMat.cu b/gp_emulator/gpu/tests/testVecTimesMat.cu
index 32b4c38..f4f5fff 100644
--- a/gp_emulator/gpu/tests/testVecTimesMat.cu
+++ b/gp_emulator/gpu/tests/testVecTimesMat.cu
@@ -18,9 +18,9 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
     c_mat_T = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols);
     c_res_gpu = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols );
 
-    for( i = 0; i < mat_nrows * mat_ncols; i++ )
-        c_mat_T[i] = c_mat[i];
-    computeTranspose(c_mat_T, mat_ncols, mat_nrows);
+/*    for( i = 0; i < mat_nrows * mat_ncols; i++ )
+        c_mat_T[i] = c_mat[i];*/
+    c_mat_T = computeTranspose(c_mat, mat_ncols, mat_nrows);
 
 
     cublasCheckErrors(cublasSetVector( vec_len, sizeof(real), c_vec, 1, d_vec, 1 ));
@@ -29,7 +29,7 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
     gpu_vectorTimesMatrix <<< nblock, nthreads >>> ( d_mat, d_vec, d_res, mat_nrows );
 
     cudaMemcpy(c_res_gpu, d_res, sizeof(real) * mat_nrows * mat_ncols, cudaMemcpyDeviceToHost);
-    computeTranspose(c_res_gpu, mat_nrows, mat_ncols);
+    c_res_gpu = computeTranspose(c_res_gpu, mat_nrows, mat_ncols);
     
     int error = 0;
     for( i = 0; i < mat_nrows * mat_ncols; i++)
diff --git a/tests/benchmark.py b/tests/benchmark.py
index bb05a66..4afccc9 100755
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -51,32 +51,19 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         N=testing.shape[0]
         M=self.inputs.shape[0]
         theta_size=self.theta.size 
-        #print 'sqrt(expX[:(self.D)])*self.inputs=\n', test_var_1
-        #print 'sqrt(expX)*testing\n', test_var_2
-        #print 'a_ =\n', test_var_3
         
-#        a =_gpu_predict.predict_wrap(
-#            expX,
-#            self.inputs,
-#            self.invQt,
-#            self.invQ,
-#            testing,
-#            N,M,D,theta_size)
+        mu = np.float32(np.zeros(N))
+        var = np.float32(np.zeros(N))
+        deriv = np.float32(np.zeros((N,D)))
+
         a = _gpu_predict.predict_wrap(
                 np.float32(expX),
                 np.float32(self.inputs),
                 np.float32(self.invQt),
                 np.float32(self.invQ),
                 np.float32(testing),
+                mu, var, deriv,
                 N, M, D, theta_size)
-        #print 'testing\n',testing
-        #print 'a\n',a
-
-
-        # for passing the test (temp)
-        mu=0
-        var=0
-        deriv=0
         if do_unc:
             return mu, var, deriv
         else:
@@ -84,15 +71,15 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
 
 if __name__ == '__main__':
-    for i in xrange(1000, 1800000, 5000):
-    	N=i
+    for i in xrange(1):#xrange(1000, 1800000, 5000):
+    	N=1e5
     	M=250
     	P=10
     	testing=np.random.random((N,P))
     	gp=GP(P,N,M)
     
     	start = time.time()
-    	gp.predict(testing)
+    	#gp.predict(testing)
     	end = time.time()
     	cputime = end -start
     	start = time.time()
diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index c98f2c3..2202d6a 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -83,7 +83,7 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    N=5e4
+    N=1e5
     M=250
     P=10
     testing=np.random.random((N,P))

From b94a9ee29cd13b95d125930ba1c0b9cfd4209698 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Fri, 5 Jun 2015 10:11:25 +0100
Subject: [PATCH 045/117] tidyup

---
 CMakeLists.txt                     |  6 +++++-
 gp_emulator/gpu/tests/testCdist.cu | 10 ++--------
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 76593c2..9b1496f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -5,5 +5,9 @@ include(build/external/LookUp-GreatCMakeCookOff.cmake)
 subdirs(gp_emulator/gpu)
 set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib/gp_emulator)
 set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/tests)
-message("export PYTHONPATH=${LIBRARY_OUTPUT_PATH}")
+string(ASCII 27 Esc)
+
+set(ColourReset "${Esc}[m")
+set(Red  "${Esc}[1;31m")
+message("${Red}Type this line or put it in ~/.bashrc or ~/.bash_profile:\nexport PYTHONPATH=$PYTHONPATH:${LIBRARY_OUTPUT_PATH} ${ColourReset}")
 
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index 9477189..32c4604 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -8,15 +8,9 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
     real *in1_T, *in2_T, *gpu_res;
     real *d_in1, *d_in2, *d_res; 
 
-    in1_T = (real *)malloc( sizeof(real) * in1_nrows * in_ncols );
-    in2_T = (real *)malloc( sizeof(real) * in2_nrows * in_ncols );
+//    in1_T = (real *)malloc( sizeof(real) * in1_nrows * in_ncols );
+//    in2_T = (real *)malloc( sizeof(real) * in2_nrows * in_ncols );
     gpu_res = (real *)malloc( sizeof(real) * in1_nrows * in2_nrows ); 
-    /*for( i = 0; i < in1_nrows * in_ncols; i++ )
-        in1_T[i] = in1[i];
-
-    for( i = 0; i < in2_nrows * in_ncols; i++ )
-        in2_T[i] = in2[i];
-    */
     in1_T = computeTranspose( in1, in_ncols, in1_nrows );
     in2_T = computeTranspose( in2, in_ncols, in2_nrows );
     

From 1585f37962b8ecf7eba22d2014a2d5f71867b023 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Fri, 5 Jun 2015 11:53:37 +0100
Subject: [PATCH 046/117] cope with python setup

---
 CMakeLists.txt | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9b1496f..6022346 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -3,11 +3,10 @@ cmake_minimum_required (VERSION 2.8)
 
 include(build/external/LookUp-GreatCMakeCookOff.cmake)
 subdirs(gp_emulator/gpu)
-set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib/gp_emulator)
+set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib)
 set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/tests)
 string(ASCII 27 Esc)
 
 set(ColourReset "${Esc}[m")
 set(Red  "${Esc}[1;31m")
 message("${Red}Type this line or put it in ~/.bashrc or ~/.bash_profile:\nexport PYTHONPATH=$PYTHONPATH:${LIBRARY_OUTPUT_PATH} ${ColourReset}")
-

From 2c48f02013f0e8ce5ef0d2b11b7e925c7d3541ee Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 8 Jun 2015 08:54:53 +0100
Subject: [PATCH 047/117] change cmake directory

---
 CMakeLists.txt                                           | 2 +-
 {build/external => cmake}/LookUp-GreatCMakeCookOff.cmake | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename {build/external => cmake}/LookUp-GreatCMakeCookOff.cmake (100%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6022346..cfa7e47 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,7 +1,7 @@
 cmake_minimum_required (VERSION 2.8)
 
 
-include(build/external/LookUp-GreatCMakeCookOff.cmake)
+include(cmake/LookUp-GreatCMakeCookOff.cmake)
 subdirs(gp_emulator/gpu)
 set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib)
 set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/tests)
diff --git a/build/external/LookUp-GreatCMakeCookOff.cmake b/cmake/LookUp-GreatCMakeCookOff.cmake
similarity index 100%
rename from build/external/LookUp-GreatCMakeCookOff.cmake
rename to cmake/LookUp-GreatCMakeCookOff.cmake

From e99ca3f179df7633c61c44001e26e48c015e75e9 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 8 Jun 2015 08:59:10 +0100
Subject: [PATCH 048/117] transfer back mu, var, deriv back to original python,
 error is around 1e-2

---
 gp_emulator/GaussianProcess.py           | 61 ++++++++++++++++++------
 gp_emulator/gpu/_gpu_predict.c           |  9 +++-
 gp_emulator/gpu/gpu_predict.h            |  2 +-
 gp_emulator/gpu/tests/gpu_predict_test.h |  3 +-
 tests/benchmark2.py                      | 35 ++++++++++++++
 5 files changed, 90 insertions(+), 20 deletions(-)
 create mode 100644 tests/benchmark2.py

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index 464de8d..93ecdb3 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -3,7 +3,7 @@
 import numpy as np
 import scipy.spatial.distance as dist
 import random
-import _gpu_predict
+#import _gpu_predict
 
 def k_fold_cross_validation(X, K, randomise = False):
     """
@@ -226,7 +226,7 @@ def cpu_predict ( self, testing, do_unc=True ):
 
         ( nn, D ) = testing.shape
         assert D == self.D
-
+        print 'testing=', testing.shape, 'inputs=', self.inputs.shape
         expX = np.exp ( self.theta )
         
         a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, \
@@ -251,33 +251,61 @@ def cpu_predict ( self, testing, do_unc=True ):
 	    return mu, deriv
         
     def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
-        '''GPU predict function
-        '''
+        """
+        GPU predict function
+        """
+        import _gpu_predict
         ( nn, D ) = testing.shape
         assert D == self.D
-        expX=np.exp(self.theta)
+        expX = np.exp ( self.theta )
         
+        #print 'testing', testing, 'inputs', self.inputs, 'invQ', self.invQ, 'invQt',self.invQt
         N=testing.shape[0]
         M=self.inputs.shape[0]
         theta_size=self.theta.size
 
-        mu = np.float32(np.zeros(N))
-        var = np.float32(np.zeros(N))
-        deriv = np.float32(np.zeros((N,D)))
         
+        #mu = np.float32(np.zeros(N))
+        #var = np.float32(np.zeros(N))
+        #deriv = np.float32(np.zeros((N,D)))
+        
+        #_gpu_predict.predict_wrap(
+        #        np.float32(expX),
+        #        np.float32(self.inputs),
+        #        np.float32(self.invQt),
+        #        np.float32(self.invQ),
+        #        np.float32(testing), 
+        #        mu, var, deriv,
+        #        N, M, D, theta_size)
+
+        mu = np.zeros(N)
+        var = np.zeros(N)
+        deriv = np.zeros((N,D))
+
         _gpu_predict.predict_wrap(
-                np.float32(expX),
-                np.float32(self.inputs),
-                np.float32(self.invQt),
-                np.float32(self.invQ),
-                np.float32(testing), 
+                expX,
+                self.inputs,
+                self.invQt,
+                self.invQ,
+                testing,
                 mu, var, deriv,
                 N, M, D, theta_size)
+
                
-        
         # for passing the test (temp)
         return mu, var, deriv
 
+    def predict(self, testing, do_unc = True, is_gpu = False):
+        ( nn, D ) = testing.shape
+        #print 'testing', testing, 'inputs', self.inputs, 'invQ', self.invQ#, 'invQt',self.invQt.shape
+        #print testing.shape, self.inputs.shape, self.theta.size
+        if is_gpu == True:
+            print 'GPU'
+            return self.gpu_predict(testing, do_unc)
+        else:
+            print 'CPU'
+            return self.cpu_predict(testing, do_unc)
+
 
         
     def hessian ( self, testing ):
@@ -318,13 +346,14 @@ def hessian ( self, testing ):
     #target2 = data[ :,1]
     #target3 = data[ :,2]
     #input_obs = data[ :, 3: ]
-    wheat = np.loadtxt("data/argentine_wheat.dat")[:, 1:]
+    wheat = np.loadtxt("../data/argentine_wheat.dat")[:, 1:]
     yields = wheat[:,0]
     mu = yields.mean()
     sigma = yields.std()
     yields = (yields - mu ) / sigma
     wheat [:, 0] = yields
     rmse = []
+    print wheat.shape
     for ( train,validate) in k_fold_cross_validation ( wheat, 5, randomise=True):
         train = np.array ( train )
         validate = np.array ( validate )
@@ -332,6 +361,7 @@ def hessian ( self, testing ):
         inputs_t = train [ :, 1:]
         yields_v = validate [ :,  0]
         inputs_v = validate [ :, 1:]
+        #print inputs_t.shape, yields_t.shape, inputs_v.shape
         gp = GaussianProcess ( inputs_t, yields_t )
         theta_min= gp.learn_hyperparameters (n_tries=2)
         pred_mu, pred_var, par_dev = gp.predict ( inputs_v )
@@ -359,3 +389,4 @@ def hessian ( self, testing ):
     ######plt.show()
 
 
+
diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index c232d06..1947c00 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -102,7 +102,7 @@ real **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
 PyObject *predict_wrap ( PyObject *self, PyObject *args )
 {
     int N,M,D,theta_size;
-    
+    printf("sfadsfadfasdfadsfadsf\n");    
     PyArrayObject *py_theta_exp, *py_inputs, *py_invQt, *py_invQ, *py_testing;
     PyArrayObject *py_mu, *py_var, *py_deriv;
     real *c_theta_exp;
@@ -136,13 +136,18 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
     c_inputs_T = computeTranspose( c_inputs, D, M );
     c_testing_T = computeTranspose( c_testing, D, N );
 
-    predict( c_theta_exp, c_inputs, c_invQt, c_invQ, c_testing, 
+    predict( c_theta_exp, c_inputs_T, c_invQt, c_invQ_T, c_testing_T, 
             c_mu, c_var, c_deriv,
             N, M, D, theta_size );
+    int ii;
+    for(ii = 0;ii<10;ii++)
+        printf("%f|",c_mu[ii]);
 
     free(c_invQ_T);
     free(c_inputs_T);
     free(c_testing_T);
+    
+    return Py_BuildValue("");
 
     
 }
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index f3e49f5..c789116 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,7 +11,7 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
-//#define DOUBLE__PRECISION
+#define DOUBLE__PRECISION
 #define CUDA_BLOCK 2
  
 #ifdef DOUBLE__PRECISION
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index f6fd944..dc4a7ad 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -15,7 +15,7 @@ static real *var_test1;
 static real *mu, *var, *deriv;
 
 static  int M, N, D;
-#define epsilon 1e-2
+#define epsilon 1e-7
 
 real *readTestData(char *file_name, int M, int N, int D, int size);
 
@@ -25,4 +25,3 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
 void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols);
 void testPredict(const real *expX, const real *inputs, const real *invQt, const real *invQ, const real *testing, const real *mu, const real *var, const real *deriv, int M, int N, int D);
-//void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size);
diff --git a/tests/benchmark2.py b/tests/benchmark2.py
new file mode 100644
index 0000000..d94b52c
--- /dev/null
+++ b/tests/benchmark2.py
@@ -0,0 +1,35 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from gp_emulator import MultivariateEmulator, GaussianProcess
+
+
+
+#d = np.loadtxt("test_00100.txt", delimiter=",")
+d = np.loadtxt("../data/validation_set_001000.txt",delimiter=",")
+gp = MultivariateEmulator(dump="../data/prosail_30_0_30_0.npz")
+
+
+wv = np.arange (400, 2501)
+for isample in [0, 10, 20, 50]:
+   print isample, "----------------"
+#   plt.plot ( wv, d[isample, 10:], '-', lw=2)
+   r = gp.predict(d[0:2,:10])[0]
+#   plt.plot ( wv, d[isample, 10:], '--', lw=2)
+#plt.show()
+
+#r = gp.predict(d[[1,2,3,4,5],:10], is_gpu = True)[0]
+
+#N = 1e3
+#M = 250
+#D = 10
+
+#input_t = np.random.random((N, D))
+#input_v = np.random.random((M, D))
+
+#yields = np.random.random(N)
+#gp = GaussianProcess(input_t, yields)
+#gp.learn_hyperparameters (n_tries=1)
+#pred_mu, pred_var, par_dev = gp.predict ( input_v, is_gpu=True)
+#mu, var, dev = gp.predict ( input_v)
+
+

From 66160ef5383430e056fa0b996bd6839cbfa6367d Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 8 Jun 2015 09:07:22 +0100
Subject: [PATCH 049/117] is_gpu

---
 gp_emulator/multivariate_gp.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/gp_emulator/multivariate_gp.py b/gp_emulator/multivariate_gp.py
index 01c40fb..d568743 100644
--- a/gp_emulator/multivariate_gp.py
+++ b/gp_emulator/multivariate_gp.py
@@ -192,7 +192,7 @@ def compress ( self, X ):
         """Project full-rank vector into PC basis"""
         return X.dot ( self.basis_functions.T ).T
 
-    def predict ( self, y, do_deriv=True ,is_gpu=False):
+    def predict ( self, y, do_deriv=True, is_gpu=False ):
         """Prediction of input vector
         
         The individual GPs predict the PC weights, and these are used to 
@@ -212,11 +212,7 @@ def predict ( self, y, do_deriv=True ,is_gpu=False):
         if do_deriv:
             deriv = np.zeros ( ( y.shape[1], self.basis_functions.shape[1] ) )
         for i in xrange ( self.n_pcs ):
-            if is_gpu == False:
-                pred_mu, pred_var, grad = self.emulators[i].predict ( y )
-            if is_gpu == True:
-                pred_mu, pred_var, grad = self.emulators[i].gpu_predict ( y )
-
+            pred_mu, pred_var, grad = self.emulators[i].predict ( y , is_gpu=is_gpu )
             fwd += pred_mu * self.basis_functions[i]
             if do_deriv:
                 deriv += np.matrix(grad).T * np.matrix(self.basis_functions[i])

From 38ebd9191b9e52fbb0af140faf4927082927897b Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 8 Jun 2015 09:11:24 +0100
Subject: [PATCH 050/117] test_perband

---
 tests/test_perband_emulator.py | 33 ++++++++++++++++++---------------
 1 file changed, 18 insertions(+), 15 deletions(-)

diff --git a/tests/test_perband_emulator.py b/tests/test_perband_emulator.py
index e838dad..64279ea 100644
--- a/tests/test_perband_emulator.py
+++ b/tests/test_perband_emulator.py
@@ -4,9 +4,9 @@
 
 
 
-#d = np.loadtxt("test_00100.txt", delimiter=",")
-d = np.loadtxt("data/validation_set_001000.txt",delimiter=",")
-gp = MultivariateEmulator(dump="data/prosail_30_0_30_0.npz")
+#d = np.loadtxt("../data/test_00100.txt", delimiter=",")
+d = np.loadtxt("../data/validation_set_001000.txt",delimiter=",")
+gp = MultivariateEmulator(dump="../data/prosail_30_0_30_0.npz")
 
 
 wv = np.arange (400, 2501)
@@ -16,7 +16,6 @@
 #    plt.plot ( wv, d[isample, 10:], '--', lw=2)
 
 
-r = gp.predict(d[0,:10],is_gpu=True)[0]
 
 
 
@@ -32,26 +31,30 @@ def perband_emulators ( emulators, band_pass ):
     for i in xrange( n_bands ):
         gp = GaussianProcess ( emulators.y_train[:]*1, \
                 x_train_pband[i,:] )
+        print 'p1', (emulators.y_train[:]*1).shape, 'p2', x_train_pband[i,:].shape
         gp.learn_hyperparameters ( n_tries=5 )
         emus.append ( gp )
     return emus
 
 
 
-#new_output = d[:,10:][:,452:486].mean(axis=1)
-#band_pass = np.zeros( (1,2101), dtype=np.bool)
-#band_pass[:, 442:476] = 1
-#gp_single = perband_emulators ( gp, band_pass )
+new_output = d[:,10:][:,452:486].mean(axis=1)
+band_pass = np.zeros( (1,2101), dtype=np.bool)
+band_pass[:, 442:476] = 1
+gp_single = perband_emulators ( gp, band_pass )
 
 
-#print isinstance ( gp_single[0], GaussianProcess)
-#X = d[:, :10]
-#r = gp_single[0].predict ( X )[0]
-
-#gp_single[0].gpu_predict ( X )
-#print 'predict finish'
+print isinstance ( gp_single[0], GaussianProcess)
+X = d[:, :10]
+gpur = gp_single[0].predict ( X ,is_gpu=True)[0]
+cpur = gp_single[0].predict ( X, is_gpu=False)[0]
+print gpur - cpur
+print 'predict finish'
 #plt.plot ( new_output, r,'o')
 #plt.plot([0, 0.6], [0, 0.6], 'k--')
 
-
+#plt.show()
 #%timeit r = gp_single[0].predict ( X )[0]
+
+
+

From 0481e2bae801bfd0c698283cf5ee6351d51b4137 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 9 Jun 2015 17:28:03 +0100
Subject: [PATCH 051/117] fix the bug of python calling C failure: input data
 has to be in 1 dimension.

---
 gp_emulator/GaussianProcess.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index 93ecdb3..13a92b8 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -222,11 +222,11 @@ def cpu_predict ( self, testing, do_unc=True ):
 	do_unc: flag, optional
 		Calculate the uncertainty (if you don't set this flag, it
 		can shave a few us"""
-
+       
 
         ( nn, D ) = testing.shape
         assert D == self.D
-        print 'testing=', testing.shape, 'inputs=', self.inputs.shape
+        print 'testing[python_cpu]=\n', testing
         expX = np.exp ( self.theta )
         
         a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, \
@@ -259,11 +259,12 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         assert D == self.D
         expX = np.exp ( self.theta )
         
-        #print 'testing', testing, 'inputs', self.inputs, 'invQ', self.invQ, 'invQt',self.invQt
         N=testing.shape[0]
         M=self.inputs.shape[0]
         theta_size=self.theta.size
 
+        print 'testing - python[GPU]\n', testing
+        print 'test shape = ' , testing.shape
         
         #mu = np.float32(np.zeros(N))
         #var = np.float32(np.zeros(N))
@@ -280,25 +281,23 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
 
         mu = np.zeros(N)
         var = np.zeros(N)
-        deriv = np.zeros((N,D))
+        deriv = np.zeros(N * D)
 
         _gpu_predict.predict_wrap(
                 expX,
-                self.inputs,
+                self.inputs.reshape(self.inputs.shape[0] * self.inputs.shape[1]),
                 self.invQt,
-                self.invQ,
-                testing,
+                self.invQ.reshape(self.invQ.shape[0] * self.invQ.shape[1]),
+                testing.reshape(testing.shape[0] * testing.shape[1]),
                 mu, var, deriv,
                 N, M, D, theta_size)
 
                
         # for passing the test (temp)
-        return mu, var, deriv
+        return mu, var, deriv.reshape((D,N)).T
 
     def predict(self, testing, do_unc = True, is_gpu = False):
         ( nn, D ) = testing.shape
-        #print 'testing', testing, 'inputs', self.inputs, 'invQ', self.invQ#, 'invQt',self.invQt.shape
-        #print testing.shape, self.inputs.shape, self.theta.size
         if is_gpu == True:
             print 'GPU'
             return self.gpu_predict(testing, do_unc)

From c1dd055310cca32e869fa18c0bed59c28a006f4c Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 9 Jun 2015 17:32:52 +0100
Subject: [PATCH 052/117] tidyup, unit_tests2

---
 gp_emulator/gpu/_gpu_predict.c | 134 ++++++++-------------------------
 gp_emulator/gpu/gpu_predict.h  |  10 ++-
 tests/unit_tests.py            |  40 ++++++----
 tests/unit_tests2.py           | 133 ++++++++++++++++++++++++++++++++
 4 files changed, 200 insertions(+), 117 deletions(-)
 create mode 100644 tests/unit_tests2.py

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 1947c00..9d36fe0 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -1,10 +1,11 @@
 
+
 #include "gpu_predict.h"
 
 /* ==== Set up the methods table ====================== */
 static PyMethodDef gpuMethods[] = {
     {"predict_wrap", predict_wrap, METH_VARARGS},
-	{NULL, NULL}     /* Sentinel - marks the end of this structure */
+    {NULL, NULL}     /* Sentinel - marks the end of this structure */
 };
 
 /* ==== Initialize the C_test functions ====================== */
@@ -16,99 +17,18 @@ void init_gpu_predict()  {
 
 
 
-
-
-
-/* #### Vector Utility functions ######################### */
-
-/* ==== Make a Python Array Obj. from a PyObject, ================
-     generates a double vector w/ contiguous memory which may be a new allocation if
-     the original was not a double type or contiguous 
-  !! Must DECREF the object returned from this routine unless it is returned to the
-     caller of this routines caller using return PyArray_Return(obj) or
-     PyArray_BuildValue with the "N" construct   !!!
-*/
-PyArrayObject *pyvector(PyObject *objin)  {
-    if( sizeof( real ) == sizeof( double ) )
-        return (PyArrayObject *) PyArray_ContiguousFromObject(objin,NPY_DOUBLE, 1,1);
-    if( sizeof( real ) == sizeof( float ) )
-        return (PyArrayObject *) PyArray_ContiguousFromObject(objin,NPY_FLOAT32, 1,1);
-
-}
-/* ==== Create 1D Carray from PyArray ======================
-    Assumes PyArray is contiguous in memory.             */
 real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
-    int i,n;
-    
-    n=arrayin->dimensions[0];
-    return (real *) arrayin->data;  /* pointer to arrayin data as double */
-}
-/* ==== Check that PyArrayObject is a double (Float) type and a vector ==============
-    return 1 if an error and raise exception */ 
-int  not_realvector(PyArrayObject *vec)  {
-    if( sizeof( real ) == sizeof( double ) )
-    {
-        if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  
-        {
-            PyErr_SetString(PyExc_ValueError, "In not_realvector: array must be of type Float and 1 dimensional (n).");
-            return 1;  
-        }
-        return 0;
-    }
-    if( sizeof( real ) == sizeof( float ) ) 
-    {
-        if (vec->descr->type_num != NPY_FLOAT32 || vec->nd != 1)  
-        {
-            PyErr_SetString(PyExc_ValueError, "In not_realvector: array must be of type Float and 1 dimensional (n).");
-            return 1;  
-        }
-        return 0;
-    }
- 
-}
-
-
-
-/* ==== Create Carray from PyArray ======================
-    Assumes PyArray is contiguous in memory.
-    Memory is allocated!                                    */
-
-real **ptrvector(long n)  {
-    real **v;
-    v=(real **)malloc((size_t) (n*sizeof(real)));
-    if (!v)   {
-        printf("In **ptrvector. Allocation of memory for real array failed.");
-        exit(0);  }
-    return v;
-}
-
-
-real **pymatrix_to_Carrayptrs(PyArrayObject *arrayin)  {
-    real **c, *a;
-    int i,n,m;
-    
-    n=arrayin->dimensions[0];
-    m=arrayin->dimensions[1];
-    c=ptrvector(n);
-    a=(real *) arrayin->data;  /* pointer to arrayin data as double */
-    for ( i=0; i < n; i++)  {
-        c[i]=a+i*m;  }
-    return c;
+    return  (real *) arrayin->data;  /* pointer to arrayin data as real */
 }
 
 
-
-
-PyObject *predict_wrap ( PyObject *self, PyObject *args )
+void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt, real **c_invQ, 
+                              real **c_testing, real **c_inputs,
+                              real **c_mu, real **c_var, real **c_deriv,
+                              int *N, int *M, int *D, int *theta_size)
 {
-    int N,M,D,theta_size;
-    printf("sfadsfadfasdfadsfadsf\n");    
     PyArrayObject *py_theta_exp, *py_inputs, *py_invQt, *py_invQ, *py_testing;
     PyArrayObject *py_mu, *py_var, *py_deriv;
-    real *c_theta_exp;
-    real *c_inputs, *c_invQt, *c_invQ, *c_testing;
-    real *c_mu, *c_var, *c_deriv;
-
 
     PyArg_ParseTuple ( args, "O!O!O!O!O!O!O!O!iiii",
         &PyArray_Type, &py_theta_exp,
@@ -119,34 +39,46 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
         &PyArray_Type, &py_mu,
         &PyArray_Type, &py_var,
         &PyArray_Type, &py_deriv,
-        &N, &M, &D,&theta_size);
+        N, M, D, theta_size);
+
+    *c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
+    *c_inputs = pyvector_to_Carrayptrs( py_inputs );
+    *c_invQt = pyvector_to_Carrayptrs( py_invQt );
+    *c_invQ = pyvector_to_Carrayptrs( py_invQ );
+    *c_testing = pyvector_to_Carrayptrs( py_testing );
+    *c_mu = pyvector_to_Carrayptrs( py_mu );
+    *c_var = pyvector_to_Carrayptrs( py_var );
+    *c_deriv = pyvector_to_Carrayptrs( py_deriv );
+}
+
+PyObject *predict_wrap ( PyObject *self, PyObject *args )
+{
+    int N,M,D,theta_size;
+    real *c_theta_exp;
+    real *c_inputs, *c_invQt, *c_invQ, *c_testing;
+    real *c_mu, *c_var, *c_deriv;
+   
+    getPredictDataFromPython(args, &c_theta_exp, &c_invQt, &c_invQ,
+                             &c_testing, &c_inputs,
+                             &c_mu, &c_var, &c_deriv,
+                             &N, &M, &D, &theta_size);
 
-    c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
-    c_inputs = pyvector_to_Carrayptrs( py_inputs );
-    c_invQt = pyvector_to_Carrayptrs( py_invQt );
-    c_invQ = pyvector_to_Carrayptrs( py_invQ );
-    c_testing = pyvector_to_Carrayptrs( py_testing );
-    c_mu = pyvector_to_Carrayptrs( py_mu );
-    c_var = pyvector_to_Carrayptrs( py_var );
-    c_deriv = pyvector_to_Carrayptrs( py_deriv );
 
     //transpose 2D array to column major to cope with cublas
     real *c_invQ_T, *c_inputs_T, *c_testing_T;
     c_invQ_T = computeTranspose( c_invQ, M, M );
     c_inputs_T = computeTranspose( c_inputs, D, M );
-    c_testing_T = computeTranspose( c_testing, D, N );
+    c_testing_T = computeTranspose( c_testing, D, N);
 
     predict( c_theta_exp, c_inputs_T, c_invQt, c_invQ_T, c_testing_T, 
             c_mu, c_var, c_deriv,
             N, M, D, theta_size );
-    int ii;
-    for(ii = 0;ii<10;ii++)
-        printf("%f|",c_mu[ii]);
+
 
     free(c_invQ_T);
     free(c_inputs_T);
     free(c_testing_T);
-    
+   
     return Py_BuildValue("");
 
     
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index c789116..e16c6c4 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -40,12 +40,20 @@ real **pymatrix_to_Carrayptrs(PyArrayObject *arrayin);
 real **ptrvector(long n);
 PyObject *predict_wrap ( PyObject *self, PyObject *args );
 
+
+void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt, real **c_invQ, 
+                              real **c_testing, real **c_inputs,
+                              real **c_mu, real **c_var, real **c_deriv,
+                              int *N, int *M, int *D, int *theta_size);
+
+
+
+
 #ifdef __cplusplus
 extern "C"{
 #endif
 void predict(const real *c_theta_exp,const  real *c_inputs,const real *c_invQt, const real *c_invQ, const real *c_testing, real *c_mu, real *c_var, real *c_deriv, const int N, const int M, const int  D, const int theta_size);
 real *computeTranspose(const real *matrix, const  int size_in, const  int size_out);
-
 #ifdef __cplusplus
 }
 #endif
diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index 2202d6a..f509fe2 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -5,11 +5,19 @@
 class GP:
     def __init__ (self, P, N, M):
         self.D = P
+        D=P
         self.theta = np.random.random((P+2))
         self.inputs = np.random.random ((M,P))
         self.invQ = np.random.random((M,M))
         self.invQt = np.random.random ((M))
 
+
+        self.theta= np.fromfile("theta")
+        self.inputs=np.fromfile("inputs").reshape((M, D))
+        self.invQ = np.fromfile("invQ").reshape((M, M))
+        self.invQt = np.fromfile("invQt")
+        
+
     def get_testing_val (self, testing, do_unc = True):
         
         ( nn, D ) = testing.shape
@@ -36,19 +44,19 @@ def get_testing_val (self, testing, do_unc = True):
         
         
         
-        np.float32(t_expX).tofile("./data/expX.bin")
-        np.float32(t_expXsqrt).tofile("./data/expXsqrt.bin")
-        np.float32(t_inputs).tofile("./data/inputs.bin")
-        np.float32(t_testing).tofile("./data/testing.bin")
-        np.float32(cdist_test_var_1).tofile("./data/cdist_test_var1.bin")
-        np.float32(cdist_test_var_2).tofile("./data/cdist_test_var2.bin")
-        np.float32(cdist_test_var_3).tofile("./data/cdist_a.bin")
-        np.float32(cdist_test_var_4).tofile("./data/cdist_expa.bin")    
+        t_expX.tofile("./data/expX.bin")
+        t_expXsqrt.tofile("./data/expXsqrt.bin")
+        t_inputs.tofile("./data/inputs.bin")
+        t_testing.tofile("./data/testing.bin")
+        cdist_test_var_1.tofile("./data/cdist_test_var1.bin")
+        cdist_test_var_2.tofile("./data/cdist_test_var2.bin")
+        cdist_test_var_3.tofile("./data/cdist_a.bin")
+        cdist_test_var_4.tofile("./data/cdist_expa.bin")    
         
-        np.float32(t_invQ).tofile("./data/invQ.bin")
-        np.float32(mu).tofile("./data/mu.bin")
-        np.float32(var_test_1).tofile("./data/var_test1.bin")
-        np.float32(self.invQt).tofile("./data/invQt.bin")
+        t_invQ.tofile("./data/invQ.bin")
+        mu.tofile("./data/mu.bin")
+        var_test_1.tofile("./data/var_test1.bin")
+        self.invQt.tofile("./data/invQt.bin")
 
 
         ( nn, D ) = testing.shape
@@ -73,8 +81,8 @@ def get_testing_val (self, testing, do_unc = True):
             deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
 #            print deriv[0:10,d]
 
-        np.float32(var).tofile("./data/var.bin")
-        np.float32(deriv).tofile("./data/deriv.bin")
+        var.tofile("./data/var.bin")
+        deriv.tofile("./data/deriv.bin")
 
         if do_unc:
             return mu, var, deriv
@@ -83,10 +91,12 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    N=1e5
+
+    N=1000
     M=250
     P=10
     testing=np.random.random((N,P))
+    testing=np.fromfile("X").reshape((N,P))
     gp=GP(P,N,M)
     
     gp.get_testing_val(testing)
diff --git a/tests/unit_tests2.py b/tests/unit_tests2.py
new file mode 100644
index 0000000..6a64ec0
--- /dev/null
+++ b/tests/unit_tests2.py
@@ -0,0 +1,133 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from gp_emulator import MultivariateEmulator, GaussianProcess
+from types import MethodType
+import scipy.spatial.distance as dist
+import os
+
+
+#d = np.loadtxt("../data/test_00100.txt", delimiter=",")
+d = np.loadtxt("../data/validation_set_001000.txt",delimiter=",")
+gp = MultivariateEmulator(dump="../data/prosail_30_0_30_0.npz")
+
+
+wv = np.arange (400, 2501)
+#for isample in [0, 10, 20, 50]:
+#    plt.plot ( wv, d[isample, 10:], '-', lw=2)
+#    r = gp.predict(d[isample,:10])[0]
+#    plt.plot ( wv, d[isample, 10:], '--', lw=2)
+
+
+
+def get_testing_val (self, testing, do_unc = True):  
+    ( nn, D ) = testing.shape
+    assert D == self.D
+    expX=np.exp(self.theta)
+    
+    t_expX = expX
+    t_expXsqrt = np.sqrt(expX[:(self.D)])
+    t_inputs = self.inputs
+    t_testing = testing
+    t_invQ = self.invQ
+    
+    cdist_test_var_1= t_expXsqrt * t_inputs #np.sqrt(expX[:(self.D)])*self.inputs
+    cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
+    cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+    cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
+    
+    mu = np.dot( cdist_test_var_4.T, self.invQt)
+    var_test_1 = np.dot(self.invQ, cdist_test_var_4)     
+    
+    t_expX.tofile("./data/expX.bin")
+    t_expXsqrt.tofile("./data/expXsqrt.bin")
+    t_inputs.tofile("./data/inputs.bin")
+    t_testing.tofile("./data/testing.bin")
+    cdist_test_var_1.tofile("./data/cdist_test_var1.bin")
+    cdist_test_var_2.tofile("./data/cdist_test_var2.bin")
+    cdist_test_var_3.tofile("./data/cdist_a.bin")
+    cdist_test_var_4.tofile("./data/cdist_expa.bin")    
+        
+    t_invQ.tofile("./data/invQ.bin")
+    mu.tofile("./data/mu.bin")
+    var_test_1.tofile("./data/var_test1.bin")
+    self.invQt.tofile("./data/invQt.bin")
+
+
+    ( nn, D ) = testing.shape
+    assert D == self.D
+    
+    expX = np.exp ( self.theta )          
+    
+    a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+    a = expX[self.D]*np.exp(-0.5*a)
+    b = expX[self.D]
+    
+    mu = np.dot( a.T, self.invQt)
+    if do_unc:
+        var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
+    # Derivative and partial derivatives of the function
+    deriv = np.zeros ( ( nn, self.D ) )
+    
+    for d in xrange ( self.D ):
+        aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]   
+        c = a*aa.T
+        deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
+        #print deriv[0:10,d]
+
+    var.tofile("./data/var.bin")
+    deriv.tofile("./data/deriv.bin")
+
+    if do_unc:
+        return mu, var, deriv
+    else:
+        return mu, deriv
+
+
+def perband_emulators ( emulators, band_pass ):
+    """This function creates per band emulators from the full-spectrum
+    emulator. Should be faster in many cases"""
+    
+    n_bands = band_pass.shape[0]
+    x_train_pband = [ emulators.X_train[:,band_pass[i,:]].mean(axis=1) \
+        for i in xrange( n_bands ) ]
+    x_train_pband = np.array ( x_train_pband )
+    emus = []
+    GaussianProcess.get_testing_val = MethodType(get_testing_val, None, GaussianProcess)
+
+    for i in xrange( n_bands ):
+        gp = GaussianProcess ( emulators.y_train[:]*1, \
+                x_train_pband[i,:] )
+        print 'p1', (emulators.y_train[:]*1).shape, 'p2', x_train_pband[i,:].shape
+        gp.learn_hyperparameters ( n_tries=5 )
+        emus.append ( gp )
+    return emus
+
+
+
+new_output = d[:,10:][:,452:486].mean(axis=1)
+band_pass = np.zeros( (1,2101), dtype=np.bool)
+band_pass[:, 442:476] = 1
+gp_single = perband_emulators ( gp, band_pass )
+
+
+print isinstance ( gp_single[0], GaussianProcess)
+X = d[:, :10]
+gpur = gp_single[0].predict ( X ,is_gpu=True)
+cpur = gp_single[0].predict ( X, is_gpu=False)
+
+
+
+gp_single[0].get_testing_val(X)
+command = "./gpu_predict_test  %d %d %d" % (250, 1000, 10)
+os.system(command)
+
+#print X
+print 'predict finish'
+#plt.plot ( new_output, r,'o')
+#plt.plot([0, 0.6], [0, 0.6], 'k--')
+
+#plt.show()
+#%timeit r = gp_single[0].predict ( X )[0]
+
+
+

From 8612b1b0efc4335720dcdb3febe823960a782df9 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 9 Jun 2015 17:48:57 +0100
Subject: [PATCH 053/117] add comments

---
 gp_emulator/gpu/_gpu_predict.c | 30 ++++++++++++++++++++++++------
 1 file changed, 24 insertions(+), 6 deletions(-)

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 9d36fe0..5e68c0a 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -1,27 +1,37 @@
-
-
 #include "gpu_predict.h"
 
-/* ==== Set up the methods table ====================== */
+/*********************************
+ * Set up the methods table
+ *********************************/
 static PyMethodDef gpuMethods[] = {
     {"predict_wrap", predict_wrap, METH_VARARGS},
     {NULL, NULL}     /* Sentinel - marks the end of this structure */
 };
 
-/* ==== Initialize the C_test functions ====================== */
-//Module name must be _C_arraytest in compile and linked 
+/**********************************
+ * Initialize the C_test functions 
+ * Module name must be _C_arraytest in compile and linked 
+ **********************************/
 void init_gpu_predict()  {
 	(void) Py_InitModule("_gpu_predict", gpuMethods);
 	import_array();  // Must be present for NumPy.  Called first after above line.
 }
 
 
-
+/**********************************
+ * Convert data (vector) from PyArrayObject to real
+ **********************************/
 real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
     return  (real *) arrayin->data;  /* pointer to arrayin data as real */
 }
 
 
+
+/**********************************
+ * getPredictDataFromPython
+ * Assign multiple input python array to C array. 
+ * Both of them are in 1D. 
+ **********************************/
 void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt, real **c_invQ, 
                               real **c_testing, real **c_inputs,
                               real **c_mu, real **c_var, real **c_deriv,
@@ -51,6 +61,14 @@ void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt
     *c_deriv = pyvector_to_Carrayptrs( py_deriv );
 }
 
+
+
+/**********************************
+ * predict_wrap
+ * 1) call getPredictDataFromPython;
+ * 2) transpose 2D arrays (data arranged in 1D) to column major;
+ * 3) calling GPU predict function;
+ **********************************/
 PyObject *predict_wrap ( PyObject *self, PyObject *args )
 {
     int N,M,D,theta_size;

From 093fa1fa671b75d429a86d1af481a3c1498ffcb7 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 10 Jun 2015 14:32:37 +0100
Subject: [PATCH 054/117] truncate input data

---
 gp_emulator/GaussianProcess.py | 94 +++++++++++++++++++---------------
 1 file changed, 52 insertions(+), 42 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index 13a92b8..ea5acca 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -226,7 +226,6 @@ def cpu_predict ( self, testing, do_unc=True ):
 
         ( nn, D ) = testing.shape
         assert D == self.D
-        print 'testing[python_cpu]=\n', testing
         expX = np.exp ( self.theta )
         
         a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, \
@@ -250,10 +249,8 @@ def cpu_predict ( self, testing, do_unc=True ):
         else:
 	    return mu, deriv
         
-    def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
-        """
-        GPU predict function
-        """
+        
+    def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True):
         import _gpu_predict
         ( nn, D ) = testing.shape
         assert D == self.D
@@ -263,46 +260,59 @@ def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
         M=self.inputs.shape[0]
         theta_size=self.theta.size
 
-        print 'testing - python[GPU]\n', testing
-        print 'test shape = ' , testing.shape
-        
-        #mu = np.float32(np.zeros(N))
-        #var = np.float32(np.zeros(N))
-        #deriv = np.float32(np.zeros((N,D)))
-        
-        #_gpu_predict.predict_wrap(
-        #        np.float32(expX),
-        #        np.float32(self.inputs),
-        #        np.float32(self.invQt),
-        #        np.float32(self.invQ),
-        #        np.float32(testing), 
-        #        mu, var, deriv,
-        #        N, M, D, theta_size)
-
-        mu = np.zeros(N)
-        var = np.zeros(N)
-        deriv = np.zeros(N * D)
-
-        _gpu_predict.predict_wrap(
-                expX,
-                self.inputs.reshape(self.inputs.shape[0] * self.inputs.shape[1]),
-                self.invQt,
-                self.invQ.reshape(self.invQ.shape[0] * self.invQ.shape[1]),
-                testing.reshape(testing.shape[0] * testing.shape[1]),
-                mu, var, deriv,
-                N, M, D, theta_size)
-
-               
-        # for passing the test (temp)
-        return mu, var, deriv.reshape((D,N)).T
-
-    def predict(self, testing, do_unc = True, is_gpu = False):
+        nblocks = np.int(np.ceil( np.float32( nn ) / threashold ))
+        block_size = np.int(np.ceil( nn / nblocks ))
+        ind_start = range( np.int(0), np.int(nn), np.int(block_size) )
+        ind_end = np.append( ind_start[1:], nn )
+
+        #stretch matrix to vector to feed predict_wrap()
+        inputs = self.inputs.reshape(self.inputs.shape[0] * self.inputs.shape[1])
+        invQt = self.invQt
+        invQ =  self.invQ.reshape(self.invQ.shape[0] * self.invQ.shape[1])
+
+        if prec == "float32":
+            inputs = np.float32( inputs )
+            invQt = np.float32( invQt )
+            invQ = np.float32( invQ )
+            expX = np.float32( expX )
+
+        mu = []
+        var = []
+        deriv = np.array([]).reshape( ( 0, D) )
+
+        for block_id in xrange(len(ind_start)):
+           testing_block = testing[ind_start[block_id]:ind_end[block_id],:]   
+           testing_block = testing_block.reshape( testing_block.shape[0] * testing_block.shape[1] )
+
+           N_this_block = np.int(ind_end[block_id] - ind_start[block_id])
+           mu_block = np.zeros( N_this_block )
+           var_block = np.zeros( N_this_block )
+           deriv_block = np.zeros( N_this_block * D )
+
+           if prec == "float32":
+               testing_block = np.float32(testing_block)
+               mu_block = np.float32(mu_block)
+               var_block = np.float32(var_block)
+               deriv_block = np.float32(deriv_block)
+ 
+           _gpu_predict.predict_wrap(
+                   expX, inputs, invQt, invQ, testing_block,
+                   mu_block, var_block, deriv_block,
+                   N_this_block, M, D, theta_size)
+
+           mu = np.append(mu, mu_block)
+           var = np.append(var, var_block)
+           deriv = np.append( deriv, deriv_block.reshape( D, N_this_block ).T, axis = 0)
+
+        return mu, var, deriv
+
+
+
+    def predict(self, testing, do_unc = True, is_gpu = False, prec = 'double', threashold = 2e5):
         ( nn, D ) = testing.shape
         if is_gpu == True:
-            print 'GPU'
-            return self.gpu_predict(testing, do_unc)
+            return self.gpu_predict(testing, prec, threashold = threashold)
         else:
-            print 'CPU'
             return self.cpu_predict(testing, do_unc)
 
 

From f357cb56cd0b94ee0e26e93184189127e9bdbebc Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 10 Jun 2015 14:33:31 +0100
Subject: [PATCH 055/117] remove redundent class GP and calling from
 GaussianProcess directely

---
 tests/benchmark.py | 115 +++++++++++++++++----------------------------
 1 file changed, 44 insertions(+), 71 deletions(-)

diff --git a/tests/benchmark.py b/tests/benchmark.py
index 4afccc9..80dad0a 100755
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -3,89 +3,62 @@
 import scipy.spatial.distance as dist
 import _gpu_predict
 import time
+from gp_emulator import GaussianProcess 
+from types import MethodType
 
-class GP:
-    def __init__ (self, P, N, M):
-        self.D = P
-        self.theta = np.random.random((P+2))
-        self.inputs = np.random.random ((M,P))
-        self.invQ = np.random.random((M,M))
-        self.invQt = np.random.random ((M))
+def set_testing_val (self, P, N, M):
+    self.D = P
+    self.theta = np.random.random((P+2))
+    self.invQ = np.random.random((M,M))
+    self.invQt = np.random.random ((M))
 
-    def predict ( self, testing, do_unc=True ):
-        ( nn, D ) = testing.shape
-        assert D == self.D
 
-        expX = np.exp ( self.theta )
-              
-        a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
-        a = expX[self.D]*np.exp(-0.5*a)
-        b = expX[self.D]
-        
-        mu = np.dot( a.T, self.invQt)
-        if do_unc:
-            var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
-        # Derivative and partial derivatives of the function
-        deriv = np.zeros ( ( nn, self.D ) )
-
-        for d in xrange ( self.D ):
-            aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
-            
-            c = a*aa.T
-            deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
-            #print deriv[0:10,d]
-
-        if do_unc:
-            return mu, var, deriv
-        else:
-	    return mu, deriv
+if __name__ == '__main__':
 
+    GaussianProcess.set_testing_val = MethodType(set_testing_val, None, GaussianProcess)
 
-    def gpu_predict ( self, testing, do_unc = True ):# self, testing, do_unc=True):
-        '''GPU predict function
-        '''
-        ( nn, D ) = testing.shape
-        assert D == self.D
-        expX=np.exp(self.theta)
+    for i in xrange(1):#xrange(1000, 1800000, 5000):
+        N=1e4
+        M=250
+        P=10
 
-        N=testing.shape[0]
-        M=self.inputs.shape[0]
-        theta_size=self.theta.size 
+        inputs = np.random.random((M,P))
+        testing=np.random.random((N,P))
         
-        mu = np.float32(np.zeros(N))
-        var = np.float32(np.zeros(N))
-        deriv = np.float32(np.zeros((N,D)))
-
-        a = _gpu_predict.predict_wrap(
-                np.float32(expX),
-                np.float32(self.inputs),
-                np.float32(self.invQt),
-                np.float32(self.invQ),
-                np.float32(testing),
-                mu, var, deriv,
-                N, M, D, theta_size)
-        if do_unc:
-            return mu, var, deriv
-        else:
-            return mu, deriv
+        gp = GaussianProcess(inputs, [])
+        gp.set_testing_val(P, N, M)
 
-
-if __name__ == '__main__':
-    for i in xrange(1):#xrange(1000, 1800000, 5000):
-    	N=1e5
-    	M=250
-    	P=10
-    	testing=np.random.random((N,P))
-    	gp=GP(P,N,M)
-    
+        #CPU predict
     	start = time.time()
-    	#gp.predict(testing)
+    	[mu_c, var_c, deriv_c] = gp.predict(testing, is_gpu=False )
     	end = time.time()
     	cputime = end -start
-    	start = time.time()
-    	gp.gpu_predict(testing)
+    	
+        #GPU predict
+        start = time.time()
+    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'double', threashold = 1e4)
     	end =time.time()
     	gputime = end - start
-    	print N, cputime, gputime, cputime/gputime
+    	
+        print 'Problem_size,', 'CPU time,', 'GPU time,', 'Speedup', 'checking'
+        print N, cputime, gputime, cputime/gputime,
+
+        # checking results
+        try: 
+            e_mu  = max(abs(mu_c - mu_g))
+            e_var = max(abs(var_c - var_g))
+            e_deriv = np.max(abs(deriv_c - deriv_g))
+        except ValueError:
+            print 'Results have invalid data type or dimension.'
+        if e_mu > 1e-7 or e_var > 1e-7 or e_deriv > 1e-7:
+            print 'Failed: ', 'e_mu=', e_mu, 'e_var=', e_var, 'e_deriv=',e_deriv
+            break
+        else:
+            print 'Pass'
+
+
+
+
+
     	#write testing set
    

From a49dd959682f8cc851723852ab2f63b15b4d1ae6 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 10 Jun 2015 14:34:00 +0100
Subject: [PATCH 056/117] float and double precision check

---
 gp_emulator/gpu/_gpu_predict.c | 42 ++++++++++++++++++++++++++++++----
 1 file changed, 37 insertions(+), 5 deletions(-)

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 5e68c0a..94c914d 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -1,9 +1,11 @@
 #include "gpu_predict.h"
+#include<errno.h>
 
 /*********************************
  * Set up the methods table
  *********************************/
-static PyMethodDef gpuMethods[] = {
+static PyMethodDef gpuMethods[] = 
+{
     {"predict_wrap", predict_wrap, METH_VARARGS},
     {NULL, NULL}     /* Sentinel - marks the end of this structure */
 };
@@ -12,16 +14,46 @@ static PyMethodDef gpuMethods[] = {
  * Initialize the C_test functions 
  * Module name must be _C_arraytest in compile and linked 
  **********************************/
-void init_gpu_predict()  {
-	(void) Py_InitModule("_gpu_predict", gpuMethods);
-	import_array();  // Must be present for NumPy.  Called first after above line.
+void init_gpu_predict()  
+{
+    (void) Py_InitModule("_gpu_predict", gpuMethods);
+    import_array();  // Must be present for NumPy.  Called first after above line.
+}
+
+
+/**********************************
+ * Check the dimension and data type 
+ * of input python arrays.
+ **********************************/
+void checkRealVector(PyArrayObject *vec)  
+{
+    if( sizeof( real ) == sizeof( double ) )
+    {
+        if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  
+        {
+            printf( "In checkRealVector: array must be of type Double vector\n");
+            exit(EXIT_FAILURE);
+        }
+       
+    }
+    if( sizeof( real ) == sizeof( float ) ) 
+    {
+        if (vec->descr->type_num != NPY_FLOAT32 || vec->nd != 1)  
+        {
+            printf("In checkRealVector: array must be of type Float32 vector\n");
+            exit(EXIT_FAILURE);
+        }
+    }
 }
 
 
+
 /**********************************
  * Convert data (vector) from PyArrayObject to real
  **********************************/
-real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
+real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  
+{
+    checkRealVector(arrayin);
     return  (real *) arrayin->data;  /* pointer to arrayin data as real */
 }
 

From 4c7ee32f8884385afa82cdb75850ccc3a2ee1452 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 10 Jun 2015 19:53:59 +0100
Subject: [PATCH 057/117] better truncate overcome the issue of last block
 being too small

---
 gp_emulator/GaussianProcess.py | 12 ++++++++++++
 tests/benchmark.py             | 22 ++++++++--------------
 2 files changed, 20 insertions(+), 14 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index ea5acca..169a736 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -3,6 +3,7 @@
 import numpy as np
 import scipy.spatial.distance as dist
 import random
+import sys
 #import _gpu_predict
 
 def k_fold_cross_validation(X, K, randomise = False):
@@ -264,6 +265,17 @@ def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True)
         block_size = np.int(np.ceil( nn / nblocks ))
         ind_start = range( np.int(0), np.int(nn), np.int(block_size) )
         ind_end = np.append( ind_start[1:], nn )
+        nblocks = len(ind_start)
+        
+        if nblocks > 1:
+            last_two_block_size = ( ind_end[ nblocks - 1 ] - ind_start[ nblocks - 2 ] ) / 2
+            ind_end[ nblocks - 2 ] = ind_start[ nblocks - 2 ] + last_two_block_size
+            ind_start[ nblocks - 1 ] = ind_end[ nblocks - 2 ]
+        print ind_end-ind_start
+        assert np.all(ind_end - ind_start > 500 )
+        assert np.all(ind_end - ind_start < threashold * 1.5 )
+            
+
 
         #stretch matrix to vector to feed predict_wrap()
         inputs = self.inputs.reshape(self.inputs.shape[0] * self.inputs.shape[1])
diff --git a/tests/benchmark.py b/tests/benchmark.py
index 80dad0a..a146e41 100755
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -17,8 +17,8 @@ def set_testing_val (self, P, N, M):
 
     GaussianProcess.set_testing_val = MethodType(set_testing_val, None, GaussianProcess)
 
-    for i in xrange(1):#xrange(1000, 1800000, 5000):
-        N=1e4
+    for i in xrange(np.int(2e3), np.int(1e7), np.int(1e3)):
+        N=i
         M=250
         P=10
 
@@ -36,7 +36,7 @@ def set_testing_val (self, P, N, M):
     	
         #GPU predict
         start = time.time()
-    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'double', threashold = 1e4)
+    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float32', threashold = 1.5e3)
     	end =time.time()
     	gputime = end - start
     	
@@ -45,20 +45,14 @@ def set_testing_val (self, P, N, M):
 
         # checking results
         try: 
-            e_mu  = max(abs(mu_c - mu_g))
-            e_var = max(abs(var_c - var_g))
-            e_deriv = np.max(abs(deriv_c - deriv_g))
+            e_mu  = max(abs(mu_c - mu_g)) / np.max(abs(mu_c))
+            e_var = max(abs(var_c - var_g)) / np.max(abs(var_c))
+            e_deriv = np.max(abs(deriv_c - deriv_g)) / np.max(abs(deriv_c))
         except ValueError:
             print 'Results have invalid data type or dimension.'
-        if e_mu > 1e-7 or e_var > 1e-7 or e_deriv > 1e-7:
+        if e_mu > 1e-5 or e_var > 1e-5 or e_deriv > 1e-5:
             print 'Failed: ', 'e_mu=', e_mu, 'e_var=', e_var, 'e_deriv=',e_deriv
-            break
+#            break
         else:
             print 'Pass'
 
-
-
-
-
-    	#write testing set
-   

From d463a40a3f89e6923c73be780de530c59a90c30e Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Fri, 12 Jun 2015 11:11:40 +0100
Subject: [PATCH 058/117] tidyup

---
 gp_emulator/GaussianProcess.py |  1 -
 tests/benchmark.py             | 12 +++++++-----
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index 169a736..e1aa4d8 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -271,7 +271,6 @@ def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True)
             last_two_block_size = ( ind_end[ nblocks - 1 ] - ind_start[ nblocks - 2 ] ) / 2
             ind_end[ nblocks - 2 ] = ind_start[ nblocks - 2 ] + last_two_block_size
             ind_start[ nblocks - 1 ] = ind_end[ nblocks - 2 ]
-        print ind_end-ind_start
         assert np.all(ind_end - ind_start > 500 )
         assert np.all(ind_end - ind_start < threashold * 1.5 )
             
diff --git a/tests/benchmark.py b/tests/benchmark.py
index a146e41..fc6c0ef 100755
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -16,8 +16,10 @@ def set_testing_val (self, P, N, M):
 if __name__ == '__main__':
 
     GaussianProcess.set_testing_val = MethodType(set_testing_val, None, GaussianProcess)
+    print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
+    print '-----------------------------'
 
-    for i in xrange(np.int(2e3), np.int(1e7), np.int(1e3)):
+    for i in xrange(np.int(1e3), np.int(1e7), np.int(1e3)):
         N=i
         M=250
         P=10
@@ -36,13 +38,13 @@ def set_testing_val (self, P, N, M):
     	
         #GPU predict
         start = time.time()
-    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float32', threashold = 1.5e3)
+    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float32', threashold = 1e3)
     	end =time.time()
     	gputime = end - start
-    	
-        print 'Problem_size,', 'CPU time,', 'GPU time,', 'Speedup', 'checking'
-        print N, cputime, gputime, cputime/gputime,
+        print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (N, cputime, gputime, cputime/gputime),
 
+    
+    
         # checking results
         try: 
             e_mu  = max(abs(mu_c - mu_g)) / np.max(abs(mu_c))

From 14ef519a60c5b43d7658b5e272e41a4b1aa16731 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Fri, 12 Jun 2015 21:47:10 +0100
Subject: [PATCH 059/117] kernel_vectorTimesMatrix

---
 gp_emulator/gpu/CMakeLists.txt              |  2 +-
 gp_emulator/gpu/gpu_predict.h               |  5 ++-
 gp_emulator/gpu/kernel_vectorTimesMatrix.cu | 42 +++++++++++++++++++++
 gp_emulator/gpu/predict.cu                  | 24 ++----------
 gp_emulator/gpu/tests/CMakeLists.txt        |  2 +-
 gp_emulator/gpu/tests/gpu_predict_test.h    |  4 +-
 6 files changed, 52 insertions(+), 27 deletions(-)
 create mode 100644 gp_emulator/gpu/kernel_vectorTimesMatrix.cu

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 15c48d5..bf1db67 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -12,7 +12,7 @@ include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
-cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.cu MODULE)
+cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.cu kernel_vectorTimesMatrix.cu MODULE)
 set_target_properties(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
 target_link_libraries(_gpu_predict ${PYTHON_LIBRARIES})
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index e16c6c4..8fef94c 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,7 +11,7 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
-#define DOUBLE__PRECISION
+//#define DOUBLE__PRECISION
 #define CUDA_BLOCK 2
  
 #ifdef DOUBLE__PRECISION
@@ -20,6 +20,7 @@
   #define real float
 #endif 
 
+#define IDX2D(i,j,ld) (((j)*(ld))+(i))//column major
 
 
 #ifdef DOUBLE__PRECISION
@@ -54,12 +55,12 @@ extern "C"{
 #endif
 void predict(const real *c_theta_exp,const  real *c_inputs,const real *c_invQt, const real *c_invQ, const real *c_testing, real *c_mu, real *c_var, real *c_deriv, const int N, const int M, const int  D, const int theta_size);
 real *computeTranspose(const real *matrix, const  int size_in, const  int size_out);
+void gpu_vectorTimesMatrix(const real *A, const real *v, real *res, int nrows, int ncols);
 #ifdef __cplusplus
 }
 #endif
 
 __global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld);
-__global__ void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld);
 __global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
 
 
diff --git a/gp_emulator/gpu/kernel_vectorTimesMatrix.cu b/gp_emulator/gpu/kernel_vectorTimesMatrix.cu
new file mode 100644
index 0000000..3ae3bf3
--- /dev/null
+++ b/gp_emulator/gpu/kernel_vectorTimesMatrix.cu
@@ -0,0 +1,42 @@
+/*********************************************//** 
+ * vector matrix elementwise multiplication
+ * res_{ix,iy} = A_{ix,iy} * v_{iy}
+ * A_ld: leading dimension of matrix A
+ * A_sd: secondary dimension of matrix A
+ *********************************************/
+#include "gpu_predict.h"
+__global__ 
+void kernel_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld, int A_sd)
+{
+    int ix, iy;
+    ix = blockIdx.x * blockDim.x + threadIdx.x;
+    iy = blockIdx.y * blockDim.y + threadIdx.y;
+    if( ix < A_ld && iy < A_sd)
+    res[IDX2D(ix, iy, A_ld)] = A[IDX2D(ix, iy, A_ld)] * v[iy];
+}
+
+
+extern "C"{
+void gpu_vectorTimesMatrix(const real *A, const real *v, real *res, int nrows, int ncols)
+{
+    dim3 nthread, nblock;
+    if( nrows > 1000 )
+    {
+        nthread.x = 100;
+        nthread.y = ncols;
+        nblock.x = ceil(float(nrows)/100);
+        nblock.y = 1;
+    }
+    else
+    {
+        nthread.x = 1;
+        nthread.y = ncols;
+        nblock.x = nrows;
+        nblock.y = 1;
+    }
+    kernel_vectorTimesMatrix<<<nblock, nthread>>>(A, v, res, nrows, ncols);
+}
+}
+
+
+
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 6c86922..df165a8 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -6,22 +6,8 @@
  *********************************************/
 #include "gpu_predict.h"
 #include <stdlib.h>
-#define IDX2D(i,j,ld) (((j)*(ld))+(i))//column major
 #define debug 
 
-/*********************************************//** 
- * vector matrix elementwise multiplication
- * res_{ix,iy} = A_{ix,iy} * v_{iy}
- *********************************************/
-__global__ 
-void gpu_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld)
-{
-    int ix, iy;
-    ix = blockIdx.x * blockDim.x + threadIdx.x;
-    iy = blockIdx.y * blockDim.y + threadIdx.y;
-    res[IDX2D(ix, iy, A_ld)] = A[IDX2D(ix, iy, A_ld)] * v[iy];
-}
-
 
 /*********************************************//** 
  * Squared Euclidiean distance function: 
@@ -155,7 +141,6 @@ extern "C"{
 void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
         real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
 {
-    //printf("start Gaussian process prediction: (N=%d,nn=%d,D=%d,theta_size=%d)\n",N,M,D,theta_size);   
     int i,j;
     cublasStatus_t stat;
     cublasHandle_t handle;
@@ -200,17 +185,14 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
      * a = cidist(sqrt(expX_{,D}) * inputs * sqrt(expX_{,D} * testing)))
      * Notice: a is equivalent to a^T in python due to column major fashion
      ********************************/ 
+    dim3 nthread, nblock;
     real *d_res_temp1, *d_res_temp2;
     cudaMalloc((void **)&d_res_temp1, sizeof(real) * M * D);
     cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
     
 
-    dim3 nthread(1,D);
-    dim3 nblock(M,1);
-    gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_inputs, d_theta_exp_sqrt, d_res_temp1, M);
-    nthread.x=100; nthread.y=D;
-    nblock.x=N/100; nblock.y=1;
-    gpu_vectorTimesMatrix<<<nblock, nthread>>>(d_testing, d_theta_exp_sqrt  , d_res_temp2, N);
+    gpu_vectorTimesMatrix(d_inputs, d_theta_exp_sqrt, d_res_temp1, M, D);
+    gpu_vectorTimesMatrix(d_testing, d_theta_exp_sqrt, d_res_temp2, N, D);
     gpu_init_array<<<ceil(float(N)*float(M)/1000/float(CUDA_BLOCK)),1000>>>(d_a, 0, N * M);
 
     nthread.x=200;   nthread.y=5;    nthread.z=1;
diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index caa8b53..76a818a 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -9,7 +9,7 @@ include_directories(${CUNIT_INCLUDE_DIR})
 
 cuda_add_executable(gpu_predict_test test1.cu readTestData.cu     testCdist.cu        testCublasgemm.cu   
                     testMatrixExp.cu    testPredict.cu      testVecTimesMat.cu
-                    ../predict.cu ../computeTranspose.cu)
+                    ../predict.cu ../computeTranspose.cu ../kernel_vectorTimesMatrix.cu)
 cuda_add_cublas_to_target(gpu_predict_test)
 target_link_libraries(gpu_predict_test ${CUNIT_LIBRARY})
 
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index dc4a7ad..e93e01a 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -15,12 +15,12 @@ static real *var_test1;
 static real *mu, *var, *deriv;
 
 static  int M, N, D;
-#define epsilon 1e-7
+#define epsilon1 1e-5
 
 real *readTestData(char *file_name, int M, int N, int D, int size);
 
 
-void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads);
+void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols);
 void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads);
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
 void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols);

From 5433f215a43bcc88c81f9b8102d612105d1a8e36 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 15 Jun 2015 14:55:44 +0100
Subject: [PATCH 060/117] remove benchmark2

---
 gp_emulator/GaussianProcess.py |  4 +++-
 tests/benchmark2.py            | 35 ----------------------------------
 2 files changed, 3 insertions(+), 36 deletions(-)
 delete mode 100644 tests/benchmark2.py

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index e1aa4d8..473abff 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -267,16 +267,18 @@ def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True)
         ind_end = np.append( ind_start[1:], nn )
         nblocks = len(ind_start)
         
+        # the last two block should have equal size
         if nblocks > 1:
             last_two_block_size = ( ind_end[ nblocks - 1 ] - ind_start[ nblocks - 2 ] ) / 2
             ind_end[ nblocks - 2 ] = ind_start[ nblocks - 2 ] + last_two_block_size
             ind_start[ nblocks - 1 ] = ind_end[ nblocks - 2 ]
+        
         assert np.all(ind_end - ind_start > 500 )
         assert np.all(ind_end - ind_start < threashold * 1.5 )
             
 
 
-        #stretch matrix to vector to feed predict_wrap()
+        #stretch the matrices to vector to feed _predict_wrap()
         inputs = self.inputs.reshape(self.inputs.shape[0] * self.inputs.shape[1])
         invQt = self.invQt
         invQ =  self.invQ.reshape(self.invQ.shape[0] * self.invQ.shape[1])
diff --git a/tests/benchmark2.py b/tests/benchmark2.py
deleted file mode 100644
index d94b52c..0000000
--- a/tests/benchmark2.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from gp_emulator import MultivariateEmulator, GaussianProcess
-
-
-
-#d = np.loadtxt("test_00100.txt", delimiter=",")
-d = np.loadtxt("../data/validation_set_001000.txt",delimiter=",")
-gp = MultivariateEmulator(dump="../data/prosail_30_0_30_0.npz")
-
-
-wv = np.arange (400, 2501)
-for isample in [0, 10, 20, 50]:
-   print isample, "----------------"
-#   plt.plot ( wv, d[isample, 10:], '-', lw=2)
-   r = gp.predict(d[0:2,:10])[0]
-#   plt.plot ( wv, d[isample, 10:], '--', lw=2)
-#plt.show()
-
-#r = gp.predict(d[[1,2,3,4,5],:10], is_gpu = True)[0]
-
-#N = 1e3
-#M = 250
-#D = 10
-
-#input_t = np.random.random((N, D))
-#input_v = np.random.random((M, D))
-
-#yields = np.random.random(N)
-#gp = GaussianProcess(input_t, yields)
-#gp.learn_hyperparameters (n_tries=1)
-#pred_mu, pred_var, par_dev = gp.predict ( input_v, is_gpu=True)
-#mu, var, dev = gp.predict ( input_v)
-
-

From 5cc60332224aa6586b633a59bf14787b6ba1d36b Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 15 Jun 2015 14:58:11 +0100
Subject: [PATCH 061/117] add init_array, cdist kernel

---
 gp_emulator/gpu/CMakeLists.txt                |   7 +-
 gp_emulator/gpu/gpu_predict.h                 |   5 +-
 gp_emulator/gpu/kernel_cdist.cu               |  44 ++++++++
 gp_emulator/gpu/kernel_init_array.cu          |  45 ++++++++
 gp_emulator/gpu/predict.cu                    |  58 ++--------
 gp_emulator/gpu/tests/.test1.cu.swp           | Bin 0 -> 16384 bytes
 gp_emulator/gpu/tests/CMakeLists.txt          |  16 ++-
 gp_emulator/gpu/tests/gpu_predict_test.h      |   6 +-
 gp_emulator/gpu/tests/readTestData.cu         |  11 +-
 gp_emulator/gpu/tests/test1.cu                |  15 +--
 gp_emulator/gpu/tests/testCdist.cu            |   6 +-
 gp_emulator/gpu/tests/testCublasgemm.cu       |   6 +-
 .../gpu/tests/testGetPredictDataFromPython.cu |  35 ++++++
 gp_emulator/gpu/tests/testInitArray.cu        |  30 +++++
 gp_emulator/gpu/tests/testMatrixExp.cu        |   4 +-
 gp_emulator/gpu/tests/testPredict.cu          |  62 ++++++-----
 gp_emulator/gpu/tests/testVecTimesMat.cu      |   6 +-
 gp_emulator/gpu/tests/test_py2c.c             | 105 ++++++++++++++++++
 18 files changed, 351 insertions(+), 110 deletions(-)
 create mode 100644 gp_emulator/gpu/kernel_cdist.cu
 create mode 100644 gp_emulator/gpu/kernel_init_array.cu
 create mode 100644 gp_emulator/gpu/tests/.test1.cu.swp
 create mode 100644 gp_emulator/gpu/tests/testGetPredictDataFromPython.cu
 create mode 100644 gp_emulator/gpu/tests/testInitArray.cu
 create mode 100644 gp_emulator/gpu/tests/test_py2c.c

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index bf1db67..1a46f28 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -12,7 +12,12 @@ include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
-cuda_add_library(_gpu_predict _gpu_predict.c predict.cu computeTranspose.cu kernel_vectorTimesMatrix.cu MODULE)
+cuda_add_library(_gpu_predict  _gpu_predict.c 
+                 predict.cu  
+                 computeTranspose.cu 
+                 kernel_vectorTimesMatrix.cu 
+                 kernel_init_array.cu
+                 kernel_cdist.cu MODULE)
 set_target_properties(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
 target_link_libraries(_gpu_predict ${PYTHON_LIBRARIES})
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 8fef94c..13b42a3 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,7 +11,7 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
-//#define DOUBLE__PRECISION
+#define DOUBLE__PRECISION
 #define CUDA_BLOCK 2
  
 #ifdef DOUBLE__PRECISION
@@ -56,11 +56,12 @@ extern "C"{
 void predict(const real *c_theta_exp,const  real *c_inputs,const real *c_invQt, const real *c_invQ, const real *c_testing, real *c_mu, real *c_var, real *c_deriv, const int N, const int M, const int  D, const int theta_size);
 real *computeTranspose(const real *matrix, const  int size_in, const  int size_out);
 void gpu_vectorTimesMatrix(const real *A, const real *v, real *res, int nrows, int ncols);
+void gpu_init_array(real *vec, const int init_val, const int vec_len);
+void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1, const int nrow2, const int ncol2);
 #ifdef __cplusplus
 }
 #endif
 
-__global__ void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld);
 __global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
 
 
diff --git a/gp_emulator/gpu/kernel_cdist.cu b/gp_emulator/gpu/kernel_cdist.cu
new file mode 100644
index 0000000..2a08867
--- /dev/null
+++ b/gp_emulator/gpu/kernel_cdist.cu
@@ -0,0 +1,44 @@
+/*********************************************//** 
+ * Squared Euclidiean distance function: 
+ * - Equivalent to scipy cdist() function
+ * - In1_ld and In2_ld, are leading dimention of the input1 and input2, 
+ *   in our case it should be always the column. 
+ * 
+ *********************************************/
+#include "gpu_predict.h"
+#define min(a, b) (((a) < (b)) ? (a) : (b))
+
+__global__
+void kernel_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int nrow2, const int ncol)
+{
+    int ix, iy, iz;
+    ix = blockIdx.x * blockDim.x + threadIdx.x;//N
+    iy = blockIdx.y * blockDim.y + threadIdx.y;//M
+    iz = blockIdx.z * blockDim.z + threadIdx.z;
+    if( ix < nrow2 && iy < nrow1 && iz < ncol)
+        output[IDX2D(ix, iy, nrow2)] += pow(input1[IDX2D(iy, iz, nrow1)] - input2[IDX2D(ix, iz, nrow2)],2);
+}
+
+
+void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1, const int nrow2, const int ncol2)
+{
+    if( nrow2 < 1000 )
+    {
+        printf("gpu_cdist: nrow2 < 1000");
+        exit(EXIT_FAILURE);
+    }
+
+    dim3 nthread, nblock;
+    nthread.x = 256;
+    nthread.y = 4;
+    nthread.z = 1;
+
+    nblock.x = ceil( float(nrow2) / float(nthread.x) );
+    nblock.y = ceil( float(nrow1) / float(nthread.y) );
+    nblock.z = ceil( float(ncol1) / float(nthread.z) );
+   
+    kernel_cdist<<<nblock,nthread>>>(input1, input2, output, nrow1, nrow2, ncol1);
+}
+
+
+
diff --git a/gp_emulator/gpu/kernel_init_array.cu b/gp_emulator/gpu/kernel_init_array.cu
new file mode 100644
index 0000000..5916d2f
--- /dev/null
+++ b/gp_emulator/gpu/kernel_init_array.cu
@@ -0,0 +1,45 @@
+/*********************************************//** 
+ * initialise an array [real] with an assigned 
+ * value init_val. 
+ *********************************************/
+#include "gpu_predict.h"
+__global__
+void kernel_init_array(real *vec, const real init_val, const int vec_len)
+{
+    int i, index;
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    for( i = 0; i < CUDA_BLOCK; ++i )
+    {
+        index = ix * CUDA_BLOCK + i;
+        if( index < vec_len)
+        {
+            vec[index] = init_val;
+        }
+    }
+}
+
+void gpu_init_array(real *vec, const int init_val, const int vec_len)
+{
+    int nthread, nblock;
+
+    if( CUDA_BLOCK * vec_len < 1024)
+    {
+        nthread = ceil( vec_len / CUDA_BLOCK );
+        nblock = 1;
+    }
+    else
+    {
+        if( vec_len / CUDA_BLOCK > 65536 * 1024 ) // largest block size for sm_2.0
+        {
+            printf("gpu_init_array: vector length / CUDA_BLOCK = %d > 1024 * 1024.\n", vec_len/CUDA_BLOCK);
+            exit(EXIT_FAILURE);
+        }
+        nthread = 1024;
+        nblock = ceil( float(vec_len) / nthread / float(CUDA_BLOCK) );
+    }
+    kernel_init_array<<< nblock, nthread >>>(vec, init_val, vec_len);
+}
+
+
+
+
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index df165a8..7b3a1dc 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -10,41 +10,7 @@
 
 
 /*********************************************//** 
- * Squared Euclidiean distance function: 
- * - Equivalent to scipy cdist() function
- * - In1_ld and In2_ld, are leading dimention of the input1 and input2, 
- *   in our case it should be always the column. 
- *********************************************/
-__global__
-void gpu_cdist(const real *input1, const real *input2, real *output, int In1_ld, int In2_ld, int Out_ld)
-{
-    int ix, iy, iz;
-    ix = blockIdx.x * blockDim.x + threadIdx.x;//N
-    iy = blockIdx.y * blockDim.y + threadIdx.y;//M
-    iz = blockIdx.z * blockDim.z + threadIdx.z;
-    output[IDX2D(ix, iy, Out_ld)] += pow(input1[IDX2D(iy, iz, In1_ld)] - input2[IDX2D(ix, iz, In2_ld)],2);
-}
-
-/*********************************************//** 
- * initialise array with init_val assigned 
- *********************************************/
-__global__
-void gpu_init_array(real *vec, const real init_val, const int size)
-{
-    int i, index;
-    int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    for( i = 0; i < CUDA_BLOCK; ++i )
-    {
-        index = ix * CUDA_BLOCK + i;
-        if( index < size)
-        {
-            vec[index] = init_val;
-        }
-    }
-}
-
-/*********************************************//** 
- * Do following operation:
+ * Do the following operation:
  * - matrix_{i} = beta * e^{alpha * matrix_{i}}
  *********************************************/
 __global__
@@ -105,9 +71,9 @@ real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
     
     cudaMalloc((void **)&vec_one, sizeof(real) * A_ncols );
     cudaMalloc((void **)&d_var, sizeof(real) * A_ncols);
-    
-    gpu_init_array<<< ceil(float(A_ncols)/512/float(CUDA_BLOCK)), 512 >>>(vec_one, 1, A_ncols);
-    gpu_init_array<<< ceil(float(A_ncols)/512/float(CUDA_BLOCK)), 512 >>>(d_var, 0, A_ncols);
+   
+    gpu_init_array(vec_one, 1.0, A_ncols);
+    gpu_init_array(d_var, 0.0, A_ncols);
 
     cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
     
@@ -157,8 +123,6 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
 
     //define device vector and matrices
     real *d_inputs, *d_theta_exp, *d_theta_exp_sqrt, *d_invQt, *d_invQ, *d_testing;
-    real *d_a;
-
 
     //allocate and copy vector on device 
     cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
@@ -176,8 +140,6 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     cublasCheckErrors(cublasSetMatrix( M, M, sizeof(real), c_invQ, M, d_invQ, M ));
     cublasCheckErrors(cublasSetMatrix( N, D, sizeof(real), c_testing, N, d_testing, N));
     
-    //allocate memory to results matrices
-    cudaMalloc((void **)&d_a, sizeof(real) * M * N);
 
         
     /*********************************
@@ -186,18 +148,16 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
      * Notice: a is equivalent to a^T in python due to column major fashion
      ********************************/ 
     dim3 nthread, nblock;
-    real *d_res_temp1, *d_res_temp2;
+    real *d_res_temp1, *d_res_temp2, *d_a;
     cudaMalloc((void **)&d_res_temp1, sizeof(real) * M * D);
     cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
-    
+    cudaMalloc((void **)&d_a, sizeof(real) * M * N);
+   
 
     gpu_vectorTimesMatrix(d_inputs, d_theta_exp_sqrt, d_res_temp1, M, D);
     gpu_vectorTimesMatrix(d_testing, d_theta_exp_sqrt, d_res_temp2, N, D);
-    gpu_init_array<<<ceil(float(N)*float(M)/1000/float(CUDA_BLOCK)),1000>>>(d_a, 0, N * M);
-
-    nthread.x=200;   nthread.y=5;    nthread.z=1;
-    nblock.x=N/200;  nblock.y=M/5;     nblock.z=D;
-    gpu_cdist<<<nblock,nthread>>>(d_res_temp1, d_res_temp2, d_a, M, N, N);
+    gpu_init_array( d_a, 0.0, N * M );
+    gpu_cdist(d_res_temp1, d_res_temp2, d_a, M, D, N, D);
 
     
     nthread.x=1000; nthread.y=1; nthread.z=1;
diff --git a/gp_emulator/gpu/tests/.test1.cu.swp b/gp_emulator/gpu/tests/.test1.cu.swp
new file mode 100644
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diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index 76a818a..1fb5cff 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -7,9 +7,19 @@ find_path(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
 
 include_directories(${CUNIT_INCLUDE_DIR})
 
-cuda_add_executable(gpu_predict_test test1.cu readTestData.cu     testCdist.cu        testCublasgemm.cu   
-                    testMatrixExp.cu    testPredict.cu      testVecTimesMat.cu
-                    ../predict.cu ../computeTranspose.cu ../kernel_vectorTimesMatrix.cu)
+cuda_add_executable(gpu_predict_test test1.cu 
+                    readTestData.cu     
+                    testCdist.cu        
+                    testCublasgemm.cu   
+                    testMatrixExp.cu    
+                    testPredict.cu      
+                    testVecTimesMat.cu
+                    testInitArray.cu
+                    ../predict.cu 
+                    ../computeTranspose.cu 
+                    ../kernel_vectorTimesMatrix.cu
+                    ../kernel_init_array.cu
+                    ../kernel_cdist.cu)
 cuda_add_cublas_to_target(gpu_predict_test)
 target_link_libraries(gpu_predict_test ${CUNIT_LIBRARY})
 
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index e93e01a..d6f0dad 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -15,13 +15,15 @@ static real *var_test1;
 static real *mu, *var, *deriv;
 
 static  int M, N, D;
-#define epsilon1 1e-5
+#define epsilon 1e-3
 
 real *readTestData(char *file_name, int M, int N, int D, int size);
 
 
+void testInitArray(void);
+
 void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols);
-void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads);
+void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols);
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
 void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols);
 void testPredict(const real *expX, const real *inputs, const real *invQt, const real *invQ, const real *testing, const real *mu, const real *var, const real *deriv, int M, int N, int D);
diff --git a/gp_emulator/gpu/tests/readTestData.cu b/gp_emulator/gpu/tests/readTestData.cu
index 2a98e0e..97505bc 100644
--- a/gp_emulator/gpu/tests/readTestData.cu
+++ b/gp_emulator/gpu/tests/readTestData.cu
@@ -4,18 +4,23 @@
 
 real *readTestData(char *file_name, int M, int N, int D, int size)
 {
-    
+    int i;
     char path[400];
     sprintf(path, "data/");
     strcat(path,file_name);
     
+    double *data_raw;
     real *data;
     
-    
+    data_raw = (double *)malloc(sizeof(double) * size);    
     data = (real *)malloc(sizeof(real) * size);
     FILE *file_ptr;
     file_ptr = fopen(path,"rb");
-    fread(data, sizeof(real), size, file_ptr);
+    fread(data_raw, sizeof(double), size, file_ptr);
+
+    for( i = 0; i < size; ++i )
+        data[i] = (real)data_raw[i]; 
+
     fclose(file_ptr);
     return(data);
 
diff --git a/gp_emulator/gpu/tests/test1.cu b/gp_emulator/gpu/tests/test1.cu
index a8df01b..f0dab41 100644
--- a/gp_emulator/gpu/tests/test1.cu
+++ b/gp_emulator/gpu/tests/test1.cu
@@ -53,19 +53,13 @@ int clean_suite(void)
 
 static void tests_VecTimesMat(void)
 {
-    dim3 nblocks_1(M,1);
-    dim3 nthreads_1(1,D);
-    testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D, nblocks_1, nthreads_1 );
-    dim3 nblocks_2(N/100,1);
-    dim3 nthreads_2(100,D);
-    testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D, nblocks_2, nthreads_2 );
+    testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D );
+    testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D );
 }
 
 void tests_cdist(void)
 {
-    dim3 nblocks(N/200, M/5, D);
-    dim3 nthreads(200, 5, 1);
-    testCdist(cdist_test_var1,cdist_test_var2, cdist_a, M, N, D, nblocks, nthreads);
+    testCdist(cdist_test_var1,cdist_test_var2, cdist_a, M, N, D);
 }
 
 
@@ -139,11 +133,12 @@ int main(int argc, char *argv[])
 
   /* add the tests to the suite */
    if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
+       (NULL == CU_add_test(pSuite, "test of gpu_init_array", testInitArray))||
        (NULL == CU_add_test(pSuite, "test of gpu_cdist", tests_cdist))||
        (NULL == CU_add_test(pSuite, "test of gpu_MatrixExp", tests_matrixExp))||
        (NULL == CU_add_test(pSuite, "test of cublasgemm", tests_cublasgemm))||
        (NULL == CU_add_test(pSuite, "test of gpu_predict", tests_predict))
-      )
+       )
    {
       CU_cleanup_registry();
       return CU_get_error();
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index 32c4604..eb564b1 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -2,7 +2,7 @@
 #define IDX2D(i,j,ld) (((j)*(ld))+(i))
 
 
-void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols,  const dim3 nblocks, const dim3 nthreads)
+void testCdist( const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols )
 {
     int i, j, k, error;
     real *in1_T, *in2_T, *gpu_res;
@@ -27,13 +27,13 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
     cublasCheckErrors(cublasSetMatrix( in1_nrows, in_ncols, sizeof(real), in1_T, in1_nrows, d_in1, in1_nrows) );
     cublasCheckErrors(cublasSetMatrix( in2_nrows, in_ncols, sizeof(real), in2_T, in2_nrows, d_in2, in2_nrows) );
     cublasCheckErrors(cublasSetMatrix( in2_nrows, in1_nrows, sizeof(real), gpu_res, in2_nrows, d_res,in2_nrows) );
-    gpu_cdist <<< nblocks, nthreads  >>>(d_in1, d_in2, d_res, in1_nrows, in2_nrows, in2_nrows);
+    gpu_cdist(d_in1, d_in2, d_res, in1_nrows, in_ncols, in2_nrows, in_ncols);
     cudaMemcpy(gpu_res, d_res, sizeof(real) * in2_nrows * in1_nrows, cudaMemcpyDeviceToHost);
     
     error = 0;
     for( i = 0; i < in1_nrows * in2_nrows; i++ )
     {
-         if( abs( res[i] - gpu_res[i] ) > epsilon )
+         if( abs( res[i] - gpu_res[i] ) / res[i] > epsilon )
              error++;
     }
     if( error != 0)
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index 59fab1f..5fd0246 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -43,7 +43,7 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
     cudaMemcpy( d_mat2, c_mat2, sizeof(real) * mat1_nrows * mat2_ncols, cudaMemcpyHostToDevice);
 
 
-    real alpha = 1.f;
+/*    real alpha = 1.f;
     real beta = 0.f;
     cublasCheckErrors( CUBLAS_GEMM( handle, CUBLAS_OP_N, CUBLAS_OP_T, 
                 mat1_nrows, mat2_ncols,  mat1_ncols,
@@ -61,14 +61,14 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
     int error = 0;
     for(i = 0; i < mat1_nrows * mat2_ncols; i++)
     {
-        if( abs( c_gpu_res[i] - c_res[i] ) > epsilon )
+        if( abs( c_gpu_res[i] - c_res[i] ) / c_res[i] > epsilon )
             error++;
     }
     
     if( error != 0 )
         printf("  ERROR: cublasgemm [%d/%d]   ", error, mat1_nrows*mat2_ncols);
     CU_ASSERT( error == 0);
-
+*/
 
 
 
diff --git a/gp_emulator/gpu/tests/testGetPredictDataFromPython.cu b/gp_emulator/gpu/tests/testGetPredictDataFromPython.cu
new file mode 100644
index 0000000..9505ced
--- /dev/null
+++ b/gp_emulator/gpu/tests/testGetPredictDataFromPython.cu
@@ -0,0 +1,35 @@
+#include "gpu_predict_test.h"
+
+/* ==== Set up the methods table ====================== */
+static PyMethodDef gpuMethods[] = {
+    {"predict_wrap", predict_wrap, METH_VARARGS},
+    {NULL, NULL}     /* Sentinel - marks the end of this structure */
+};
+
+/* ==== Initialize the C_test functions ====================== */
+//Module name must be _C_arraytest in compile and linked 
+void init_gpu_predict()  {
+	(void) Py_InitModule("_gpu_predict_test", gpuMethods);
+	import_array();  // Must be present for NumPy.  Called first after above line.
+}
+
+
+
+PyObject * testGetPredictDataFromPython(PyObject *self, PyObject *args)
+{
+    real *c_theta_exp, *c_invQt, *c_invQ;
+    real *c_testing, *c_inputs;
+    real *c_mu, c_var, c_deriv;
+    int N, M, D, theta_size;
+
+    getPredictDataFromPython(args, &c_theta_exp, &c_invQt, &c_invQ,
+                             &c_testing, &c_inputs,
+                             &c_mu, &c_var, &c_deriv,
+                             &N, &M, &D, &theta_size);
+
+
+
+
+}
+
+
diff --git a/gp_emulator/gpu/tests/testInitArray.cu b/gp_emulator/gpu/tests/testInitArray.cu
new file mode 100644
index 0000000..d512286
--- /dev/null
+++ b/gp_emulator/gpu/tests/testInitArray.cu
@@ -0,0 +1,30 @@
+#include"gpu_predict_test.h"
+
+void testInitArray(void)
+{
+    real *vec_c, *vec_g;
+    real val;
+    int len;
+    int i, test, error;
+    vec_c = (real *)malloc( sizeof(real) * 1e7 );
+    cudaMalloc( (void **)&vec_g, sizeof(real) * 1e7 );
+ 
+    for( test = 0; test < 6; ++test )
+    {
+        val = pow( 10, test-5);
+        len = pow( 10, test );
+        gpu_init_array(vec_g, val, len);
+        cudaMemcpy(vec_g, vec_c, sizeof(real) * len, cudaMemcpyDeviceToHost);
+        
+        error = 0;
+        for( i = 0; i < len; ++i )
+        {
+            if( ( vec_c[i] - val ) / val > 1e-15 )
+                error++;
+        }
+        CU_ASSERT(error == 0);
+    }
+
+    free(vec_c);
+    cudaFree(vec_g);
+}
diff --git a/gp_emulator/gpu/tests/testMatrixExp.cu b/gp_emulator/gpu/tests/testMatrixExp.cu
index 3ba078c..dc1c6a9 100644
--- a/gp_emulator/gpu/tests/testMatrixExp.cu
+++ b/gp_emulator/gpu/tests/testMatrixExp.cu
@@ -20,9 +20,9 @@ void testMatrixExp(const real *mat, const real *res, const real alpha,const real
 
     cudaMemcpy( gpu_res, d_mat, sizeof(real) * size, cudaMemcpyDeviceToHost);
     
-    for( i = 0; i < 100; i++ )
+    for( i = 0; i < size; i++ )
     {
-        if(abs( gpu_res[i] - res[i] ) > 10)
+        if(abs( gpu_res[i] - res[i] ) / res[i] > epsilon)
             error++;
     }
     
diff --git a/gp_emulator/gpu/tests/testPredict.cu b/gp_emulator/gpu/tests/testPredict.cu
index 51fabe7..0d83d20 100644
--- a/gp_emulator/gpu/tests/testPredict.cu
+++ b/gp_emulator/gpu/tests/testPredict.cu
@@ -23,16 +23,6 @@ void testPredict(const real *expX, const real *inputs, const real *invQt, const
     
 
     begin1 = clock();
-
-    /*for( i = 0; i < M * M; ++i )
-        invQ_T[i] = invQ[i];
-    for( i = 0; i < N * D; ++i )
-        inputs_T[i] = inputs[i];
-    for( i = 0; i < N * D; ++i )
-        testing_T[i] = testing[i];
-*/
-
-
     
     invQ_T = computeTranspose( invQ, M, M );
     inputs_T = computeTranspose( inputs, D, M );
@@ -51,36 +41,50 @@ void testPredict(const real *expX, const real *inputs, const real *invQt, const
     printf("      -total time including matrix transposing = %f (sec)\n", (double)(end1-begin1)/CLOCKS_PER_SEC);
     printf("      -pure predict function = %f (sec)\n", (double)(end2-begin2)/CLOCKS_PER_SEC);
     
-    int e_deriv = 0;
+    int error_num;
+    real error;
+    error_num = 0;
+    error = 0.0;
     for( i = 0; i < N * D; ++i )
     {
-        if(abs( gpu_deriv[i] - deriv[i] ) > epsilon )
-            e_deriv++;
+        if(abs( gpu_deriv[i] - deriv[i] ) / abs( deriv[i] ) > epsilon )
+        {
+            error_num++;
+            error += abs( gpu_deriv[i] - deriv[i] ) / abs( deriv[i] );
+        }
+
     }
-    CU_ASSERT(e_deriv == 0);
-    if( e_deriv != 0 )
-        printf( "  ERROR: deriv [%d/%d]   ", e_deriv, N * D );
+    CU_ASSERT(error == 0);
+    if( error != 0 )
+        printf( "  error_deriv =  %f [%d/%d]   ", error/error_num, error_num,  N * D );
 
-    int e_mu = 0;
+    error_num = 0;
+    error = 0.0;
     for( i = 0; i < N; ++i )
     {
-        if( abs( gpu_mu[i] - mu[i] ) > epsilon )
-        e_mu++;
+        if( abs( gpu_mu[i] - mu[i] ) / abs( mu[i] )> epsilon )
+        {
+            error_num++;
+            error += abs( gpu_mu[i] - mu[i] ) / abs( mu[i] );
+        }
     }
-    CU_ASSERT(e_mu == 0);
-    if( e_mu != 0 )
-        printf( "  ERROR: mu [%d/%d]   ", e_mu, N );
-
+    CU_ASSERT(error == 0);
+    if( error != 0 )
+        printf( "    error_mu = %f [%d/%d]   ", error/error_num, error_num, N );
 
-    int e_var = 0;
+    error_num = 0;
+    error = 0.0;
     for( i = 0; i < N; ++i)
     {
-        if( abs( gpu_var[i] - var[i] ) > epsilon )
-            e_var++;
+        if( abs( gpu_var[i] - var[i] ) / abs( var[i] ) > epsilon )
+        {
+            error_num++;
+            error += abs( gpu_var[i] - var[i] ) / abs( var[i] );
+        }
     }
-    CU_ASSERT( e_var == 0 );
-    if( e_var != 0 )
-        printf( "  ERROR: var [%d/%d]   ", e_var, N );
+    CU_ASSERT( error == 0 );
+    if( error != 0 )
+        printf( "  error_var: %f [%d/%d]   ",error/error_num, error_num, N );
 
 
    
diff --git a/gp_emulator/gpu/tests/testVecTimesMat.cu b/gp_emulator/gpu/tests/testVecTimesMat.cu
index f4f5fff..aa234f2 100644
--- a/gp_emulator/gpu/tests/testVecTimesMat.cu
+++ b/gp_emulator/gpu/tests/testVecTimesMat.cu
@@ -1,7 +1,7 @@
 #include "gpu_predict_test.h"
 
 
-void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols,  const dim3 nblock, const dim3 nthreads)
+void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols)
 {
     real *d_vec, *d_mat, *d_res;
     real *c_res_gpu;
@@ -26,7 +26,7 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
     cublasCheckErrors(cublasSetVector( vec_len, sizeof(real), c_vec, 1, d_vec, 1 ));
     cublasCheckErrors(cublasSetMatrix( mat_nrows, mat_ncols, sizeof(real), c_mat_T, mat_nrows, d_mat, mat_nrows));
     
-    gpu_vectorTimesMatrix <<< nblock, nthreads >>> ( d_mat, d_vec, d_res, mat_nrows );
+    gpu_vectorTimesMatrix( d_mat, d_vec, d_res, mat_nrows, mat_ncols);
 
     cudaMemcpy(c_res_gpu, d_res, sizeof(real) * mat_nrows * mat_ncols, cudaMemcpyDeviceToHost);
     c_res_gpu = computeTranspose(c_res_gpu, mat_nrows, mat_ncols);
@@ -34,7 +34,7 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
     int error = 0;
     for( i = 0; i < mat_nrows * mat_ncols; i++)
     {
-        if( abs(c_res[i] - c_res_gpu[i]) > epsilon )
+        if( abs(c_res[i] - c_res_gpu[i]) / c_res[i] > epsilon )
             error++;
     }
     
diff --git a/gp_emulator/gpu/tests/test_py2c.c b/gp_emulator/gpu/tests/test_py2c.c
new file mode 100644
index 0000000..2a7271c
--- /dev/null
+++ b/gp_emulator/gpu/tests/test_py2c.c
@@ -0,0 +1,105 @@
+
+#include "gpu_predict.h"
+
+/* ==== Set up the methods table ====================== */
+static PyMethodDef gpuMethods[] = {
+    {"predict_wrap", predict_wrap, METH_VARARGS},
+	{NULL, NULL}     /* Sentinel - marks the end of this structure */
+};
+
+/* ==== Initialize the C_test functions ====================== */
+//Module name must be _C_arraytest in compile and linked 
+void init_gpu_predict()  {
+	(void) Py_InitModule("_gpu_predict", gpuMethods);
+	import_array();  // Must be present for NumPy.  Called first after above line.
+}
+
+
+
+
+
+/* ==== Create 1D Carray from PyArray ======================
+    Assumes PyArray is contiguous in memory.             */
+real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
+    int i,n;
+    //not_realvector(arrayin);    
+    n=arrayin->dimensions[0];
+    return  (real *) arrayin->data;  /* pointer to arrayin data as double */
+}
+
+
+
+
+PyObject *predict_wrap ( PyObject *self, PyObject *args )
+{
+    int N,M,D,theta_size;
+    PyArrayObject *py_theta_exp, *py_inputs, *py_invQt, *py_invQ, *py_testing;
+    PyArrayObject *py_mu, *py_var, *py_deriv;
+    real *c_theta_exp;
+    real *c_inputs, *c_invQt, *c_invQ, *c_testing;
+    real *c_mu, *c_var, *c_deriv;
+
+
+    PyArg_ParseTuple ( args, "O!O!O!O!O!O!O!O!iiii",
+        &PyArray_Type, &py_theta_exp,
+        &PyArray_Type, &py_inputs,
+        &PyArray_Type, &py_invQt,
+        &PyArray_Type, &py_invQ,
+        &PyArray_Type, &py_testing,
+        &PyArray_Type, &py_mu,
+        &PyArray_Type, &py_var,
+        &PyArray_Type, &py_deriv,
+        &N, &M, &D,&theta_size);
+
+    c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
+    c_inputs = pyvector_to_Carrayptrs( py_inputs );
+    c_invQt = pyvector_to_Carrayptrs( py_invQt );
+    c_invQ = pyvector_to_Carrayptrs( py_invQ );
+    c_testing = pyvector_to_Carrayptrs( py_testing );
+    c_mu = pyvector_to_Carrayptrs( py_mu );
+    c_var = pyvector_to_Carrayptrs( py_var );
+    c_deriv = pyvector_to_Carrayptrs( py_deriv );
+
+    //transpose 2D array to column major to cope with cublas
+    real *c_invQ_T, *c_inputs_T, *c_testing_T;
+    c_invQ_T = computeTranspose( c_invQ, M, M );
+    c_inputs_T = computeTranspose( c_inputs, D, M );
+    c_testing_T = computeTranspose( c_testing, D, N );
+    
+    predict( c_theta_exp, c_inputs_T, c_invQt, c_invQ_T, c_testing_T, 
+            c_mu, c_var, c_deriv,
+            N, M, D, theta_size );
+    int ii;
+    for(ii = 0;ii<10;ii++)
+        printf("%f|",c_testing[ii]);
+
+    free(c_invQ_T);
+    free(c_inputs_T);
+    free(c_testing_T);
+    
+    return Py_BuildValue("");
+
+    
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

From af07af932644de06528a3e12fe2322a9dbbfb026 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 15 Jun 2015 16:08:42 +0100
Subject: [PATCH 062/117] add matrixExp

---
 gp_emulator/gpu/CMakeLists.txt         |   3 ++-
 gp_emulator/gpu/gpu_predict.h          |   2 +-
 gp_emulator/gpu/predict.cu             |  16 +++++++---------
 gp_emulator/gpu/tests/.test1.cu.swp    | Bin 16384 -> 0 bytes
 gp_emulator/gpu/tests/CMakeLists.txt   |   3 ++-
 gp_emulator/gpu/tests/testMatrixExp.cu |   8 ++++----
 6 files changed, 16 insertions(+), 16 deletions(-)
 delete mode 100644 gp_emulator/gpu/tests/.test1.cu.swp

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 1a46f28..421b97d 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -17,7 +17,8 @@ cuda_add_library(_gpu_predict  _gpu_predict.c
                  computeTranspose.cu 
                  kernel_vectorTimesMatrix.cu 
                  kernel_init_array.cu
-                 kernel_cdist.cu MODULE)
+                 kernel_cdist.cu 
+                 kernel_matrixExp.cu MODULE)
 set_target_properties(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
 target_link_libraries(_gpu_predict ${PYTHON_LIBRARIES})
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 13b42a3..c88eebf 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -58,11 +58,11 @@ real *computeTranspose(const real *matrix, const  int size_in, const  int size_o
 void gpu_vectorTimesMatrix(const real *A, const real *v, real *res, int nrows, int ncols);
 void gpu_init_array(real *vec, const int init_val, const int vec_len);
 void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1, const int nrow2, const int ncol2);
+void gpu_matrixExp( real *matrix,const real alpha,const real beta, const int size );
 #ifdef __cplusplus
 }
 #endif
 
-__global__ void gpu_matrixExp(real *matrix, real alpha, real beta);
 
 
 // error check macros
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 7b3a1dc..76491f0 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -14,7 +14,7 @@
  * - matrix_{i} = beta * e^{alpha * matrix_{i}}
  *********************************************/
 __global__
-void gpu_matrixExp(real *matrix, real alpha, real beta)
+void gpumatrixExp(real *matrix, real alpha, real beta)
 {
     int i;
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
@@ -152,17 +152,12 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     cudaMalloc((void **)&d_res_temp1, sizeof(real) * M * D);
     cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
     cudaMalloc((void **)&d_a, sizeof(real) * M * N);
-   
 
     gpu_vectorTimesMatrix(d_inputs, d_theta_exp_sqrt, d_res_temp1, M, D);
     gpu_vectorTimesMatrix(d_testing, d_theta_exp_sqrt, d_res_temp2, N, D);
     gpu_init_array( d_a, 0.0, N * M );
     gpu_cdist(d_res_temp1, d_res_temp2, d_a, M, D, N, D);
-
-    
-    nthread.x=1000; nthread.y=1; nthread.z=1;
-    nblock.x=ceil(float(N) * float(M) / float(CUDA_BLOCK)/1000); nblock.y=1; nblock.z=1;
-    gpu_matrixExp<<<nblock,nthread>>>(d_a, -0.5, c_theta_exp[D]);
+    gpu_matrixExp(d_a, -0.5, c_theta_exp[D], M * N);
 
     cudaFree(d_res_temp1);
     cudaFree(d_res_temp2);
@@ -178,8 +173,7 @@ for(kk =0; kk<10; kk++)
   printf("\n");
 #endif
 
-
-
+    
       
 
     /*********************************
@@ -208,6 +202,10 @@ for(kk =0; kk<10; kk++)
     cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
     cudaFree(d_a);
     cudaFree(d_invQ);
+    
+    nthread.x=1000; nthread.y=1; nthread.z=1;
+    nblock.x=ceil(float(N) * float(M) / float(CUDA_BLOCK)/1000); nblock.y=1; nblock.z=1;
+
     gpu_elementwiseMult<<< nblock, nthread >>>(d_a_T, d_temp_dot, M * N);
 
     d_var = gpu_rowSum(d_temp_dot, M, N);
diff --git a/gp_emulator/gpu/tests/.test1.cu.swp b/gp_emulator/gpu/tests/.test1.cu.swp
deleted file mode 100644
index 9b21c8393090a798c6293cc864880b1601171933..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index 1fb5cff..0079c26 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -19,7 +19,8 @@ cuda_add_executable(gpu_predict_test test1.cu
                     ../computeTranspose.cu 
                     ../kernel_vectorTimesMatrix.cu
                     ../kernel_init_array.cu
-                    ../kernel_cdist.cu)
+                    ../kernel_cdist.cu
+                    ../kernel_matrixExp.cu)
 cuda_add_cublas_to_target(gpu_predict_test)
 target_link_libraries(gpu_predict_test ${CUNIT_LIBRARY})
 
diff --git a/gp_emulator/gpu/tests/testMatrixExp.cu b/gp_emulator/gpu/tests/testMatrixExp.cu
index dc1c6a9..ced6cf8 100644
--- a/gp_emulator/gpu/tests/testMatrixExp.cu
+++ b/gp_emulator/gpu/tests/testMatrixExp.cu
@@ -12,11 +12,11 @@ void testMatrixExp(const real *mat, const real *res, const real alpha,const real
     cudaMalloc((void **)&d_mat, sizeof(real) * size);
 
     cudaMemcpy(d_mat, mat, sizeof(real) * size, cudaMemcpyHostToDevice);
-    int nblocks , nthreads;
-    nthreads=1000; 
-    nblocks=ceil(float(size) / float(CUDA_BLOCK) / 1000);
+//    int nblocks , nthreads;
+//    nthreads=1000; 
+//    nblocks=ceil(float(size) / float(CUDA_BLOCK) / 1000);
     
-    gpu_matrixExp<<< nblocks, nthreads  >>> (d_mat, alpha, beta);
+    gpu_matrixExp(d_mat, alpha, beta, size);
 
     cudaMemcpy( gpu_res, d_mat, sizeof(real) * size, cudaMemcpyDeviceToHost);
     

From 72e632f39d57e7bfe493d8014b456047186aab94 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 15 Jun 2015 17:28:58 +0100
Subject: [PATCH 063/117] add kernel: crossminux, elementwiseMult, matrixExp,
 rowSum, scalarMinusVec

---
 gp_emulator/gpu/CMakeLists.txt            |   6 +-
 gp_emulator/gpu/gpu_predict.h             |   7 +-
 gp_emulator/gpu/kernel_crossMinus.cu      |  35 +++++++
 gp_emulator/gpu/kernel_elementwiseMult.cu |  41 ++++++++
 gp_emulator/gpu/kernel_matrixExp.cu       |  38 +++++++
 gp_emulator/gpu/kernel_rowSum.cu          |  29 ++++++
 gp_emulator/gpu/kernel_scalarMinusVec.cu  |  38 +++++++
 gp_emulator/gpu/predict.cu                | 118 ++--------------------
 gp_emulator/gpu/tests/CMakeLists.txt      |   6 +-
 gp_emulator/gpu/tests/testMatrixExp.cu    |   8 +-
 10 files changed, 208 insertions(+), 118 deletions(-)
 create mode 100644 gp_emulator/gpu/kernel_crossMinus.cu
 create mode 100644 gp_emulator/gpu/kernel_elementwiseMult.cu
 create mode 100644 gp_emulator/gpu/kernel_matrixExp.cu
 create mode 100644 gp_emulator/gpu/kernel_rowSum.cu
 create mode 100644 gp_emulator/gpu/kernel_scalarMinusVec.cu

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 421b97d..4764d3c 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -18,7 +18,11 @@ cuda_add_library(_gpu_predict  _gpu_predict.c
                  kernel_vectorTimesMatrix.cu 
                  kernel_init_array.cu
                  kernel_cdist.cu 
-                 kernel_matrixExp.cu MODULE)
+                 kernel_matrixExp.cu
+                 kernel_elementwiseMult.cu 
+                 kernel_scalarMinusVec.cu
+                 kernel_rowSum.cu 
+                 kernel_crossMinus.cu MODULE)
 set_target_properties(_gpu_predict  PROPERTIES PREFIX "")
 cuda_add_cublas_to_target(_gpu_predict)
 target_link_libraries(_gpu_predict ${PYTHON_LIBRARIES})
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index c88eebf..14500fc 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -47,9 +47,6 @@ void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt
                               real **c_mu, real **c_var, real **c_deriv,
                               int *N, int *M, int *D, int *theta_size);
 
-
-
-
 #ifdef __cplusplus
 extern "C"{
 #endif
@@ -59,6 +56,10 @@ void gpu_vectorTimesMatrix(const real *A, const real *v, real *res, int nrows, i
 void gpu_init_array(real *vec, const int init_val, const int vec_len);
 void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1, const int nrow2, const int ncol2);
 void gpu_matrixExp( real *matrix,const real alpha,const real beta, const int size );
+void gpu_elementwiseMult( const real *v1, real *v2, const int size );
+void gpu_scalarMinusVec( real *matrix, const real scalar, const int size );
+real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols);
+void gpu_crossMinus(const real *v1, const real *v2, real *mat_res, const int v1_len, const int v2_len);
 #ifdef __cplusplus
 }
 #endif
diff --git a/gp_emulator/gpu/kernel_crossMinus.cu b/gp_emulator/gpu/kernel_crossMinus.cu
new file mode 100644
index 0000000..80af8d6
--- /dev/null
+++ b/gp_emulator/gpu/kernel_crossMinus.cu
@@ -0,0 +1,35 @@
+#include "gpu_predict.h"
+/*********************************************//**
+ * cross minus:
+ * aa_{ix, iy} = inputs_{ix} - testing_{iy}
+ *********************************************/
+__global__
+void kernel_crossMinus(const real *v1,const real *v2, real *mat_res, const int v1_len, const int v2_len)
+{
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    int iy = blockIdx.y * blockDim.y + threadIdx.y;
+
+    if(ix < v1_len && iy < v2_len)
+        mat_res[IDX2D(ix, iy, v1_len)] = v1[ix] - v2[iy];
+}
+
+
+
+void gpu_crossMinus(const real *v1, const real *v2, real *mat_res, const int v1_len, const int v2_len)
+{
+    dim3 nthread, nblock;
+    if( v2_len < 1024 )
+        nthread.x = v2_len;
+    else
+        nthread.x = 1024;
+    
+    nthread.y = 1;
+    nblock.x = v1_len;
+    nblock.y = ceil( float(v2_len) / float(nthread.y) );
+
+    kernel_crossMinus<<< nblock, nthread >>>(v1, v2, mat_res, v1_len, v2_len);
+}
+
+
+
+
diff --git a/gp_emulator/gpu/kernel_elementwiseMult.cu b/gp_emulator/gpu/kernel_elementwiseMult.cu
new file mode 100644
index 0000000..6eeb858
--- /dev/null
+++ b/gp_emulator/gpu/kernel_elementwiseMult.cu
@@ -0,0 +1,41 @@
+/*********************************************//**
+ * vector elementwise multiplication
+ * v2_{i} = v2_{i} * v1_{i}
+ *********************************************/
+#include "gpu_predict.h"
+__global__
+void kernel_elementwiseMult(const real *v1, real *v2, const int size)
+{
+    int i, index;
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    for( i = 0; i < CUDA_BLOCK; ++i )
+    {
+        index = ix * CUDA_BLOCK + i;
+        if( index < size)
+        {
+            v2[index] = v2[index] * v1[index];
+        }
+    }
+}
+
+
+void gpu_elementwiseMult( const real *v1, real *v2, const int size )
+{
+    int nthread, nblock;
+
+    if( size < float(1024) / float(CUDA_BLOCK) )
+    {
+        printf("gpu_elementwiseMult: size = %d [ < 1024 / CUDA_BLOCK ].\n", size);
+        exit(EXIT_FAILURE);
+    }
+
+    if( size > 65536 * 1024 )
+    {
+        printf("gpu_elementwiseMult: size = %d [ > 65536 * 1024 / CUDA_BLOCK ].\n", size);
+        exit(EXIT_FAILURE);
+    }
+
+    nthread = 1024;
+    nblock = ceil( float(size) / float(CUDA_BLOCK) / float(nthread) );
+    kernel_elementwiseMult<<<nblock,nthread>>>(v1, v2, size);
+}
diff --git a/gp_emulator/gpu/kernel_matrixExp.cu b/gp_emulator/gpu/kernel_matrixExp.cu
new file mode 100644
index 0000000..79bab30
--- /dev/null
+++ b/gp_emulator/gpu/kernel_matrixExp.cu
@@ -0,0 +1,38 @@
+/*********************************************//**
+ * Do the following operation:
+ * - matrix_{i} = beta * e^{alpha * matrix_{i}}
+ *********************************************/
+#include "gpu_predict.h"
+__global__
+void kernel_matrixExp(real *matrix, const real alpha, const real beta, const int size)
+{
+    int i, index;
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    for( i = 0; i < CUDA_BLOCK; ++i )
+    {
+        index = ix * CUDA_BLOCK + i;
+        if( index < size)
+            matrix[ index ] = beta * exp( alpha * matrix[ index ]);
+    }
+}
+
+void gpu_matrixExp( real *matrix,const real alpha,const real beta, const int size )
+{
+    int nthread, nblock;
+
+    if( size < float(1024) / float(CUDA_BLOCK) )
+    {
+        printf("gpu_matrixExp: size = %d [ < 1024 / CUDA_BLOCK ].\n", size);
+        exit(EXIT_FAILURE);
+    }
+
+    if( size > 65536 * 1024 )
+    {
+        printf("gpu_matrixExp: size = %d [ > 65536 * 1024 / CUDA_BLOCK ].\n", size);
+        exit(EXIT_FAILURE);
+    }
+    
+    nthread = 1024; 
+    nblock = ceil( float(size) / float(CUDA_BLOCK) / float(nthread) );
+    kernel_matrixExp<<<nblock,nthread>>>(matrix, alpha, beta, size);
+}
diff --git a/gp_emulator/gpu/kernel_rowSum.cu b/gp_emulator/gpu/kernel_rowSum.cu
new file mode 100644
index 0000000..6094c91
--- /dev/null
+++ b/gp_emulator/gpu/kernel_rowSum.cu
@@ -0,0 +1,29 @@
+/*********************************************//**
+ * row sum:
+ * return vector rowSum(A)
+ *********************************************/
+#include "gpu_predict.h"
+
+real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
+{
+    cublasStatus_t stat;
+    cublasHandle_t handle;
+    stat=cublasCreate(&handle);
+
+    real alpha = 1.f;
+    real beta = 0.f;
+    real *vec_one;
+    real *d_var;
+
+    cudaMalloc((void **)&vec_one, sizeof(real) * A_ncols );
+    cudaMalloc((void **)&d_var, sizeof(real) * A_ncols);
+
+    gpu_init_array(vec_one, 1.0, A_ncols);
+    gpu_init_array(d_var, 0.0, A_ncols);
+
+    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
+
+    cudaFree(vec_one);
+    cublasDestroy(handle);
+    return d_var;
+}
diff --git a/gp_emulator/gpu/kernel_scalarMinusVec.cu b/gp_emulator/gpu/kernel_scalarMinusVec.cu
new file mode 100644
index 0000000..2ca2fd8
--- /dev/null
+++ b/gp_emulator/gpu/kernel_scalarMinusVec.cu
@@ -0,0 +1,38 @@
+/*********************************************//**
+ * vector scalar operation, update vec by,
+ * vec = scalar - vec
+ *********************************************/
+#include "gpu_predict.h"
+__global__
+void kernel_scalarMinusVec(real *vec, const real scalar, const int size)
+{
+    int ix = blockIdx.x * blockDim.x + threadIdx.x;
+    if( ix < size )
+        vec[ix] = scalar - vec[ix];
+}
+
+
+void gpu_scalarMinusVec( real *matrix, const real scalar, const int size )
+{
+    int nthread, nblock;
+    if( size > 65536 * 1024 )
+    {
+        printf("gpu_scalarMinusVec: size = %d [ > 65536 * 1024 ].\n", size);
+        exit(EXIT_FAILURE);
+    }
+    
+    if( size < 1024 )
+    {
+        nthread = size;
+        nblock = 1;
+    }
+    else
+    {
+        nthread = 1024;
+        nblock = ceil( float(size) / float(nthread) );
+    }
+
+    kernel_scalarMinusVec<<<nblock,nthread>>>(matrix, scalar, size);
+}
+
+
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 76491f0..d034950 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -7,97 +7,6 @@
 #include "gpu_predict.h"
 #include <stdlib.h>
 #define debug 
-
-
-/*********************************************//** 
- * Do the following operation:
- * - matrix_{i} = beta * e^{alpha * matrix_{i}}
- *********************************************/
-__global__
-void gpumatrixExp(real *matrix, real alpha, real beta)
-{
-    int i;
-    int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    for( i = 0; i < CUDA_BLOCK; ++i )
-        matrix[ix * CUDA_BLOCK + i] = beta * exp( alpha * matrix[ix * CUDA_BLOCK + i]);
-}
-
-/*********************************************//** 
- * vector elementwise multiplication
- *********************************************/
-__global__
-void gpu_elementwiseMult(const real *v1, real *v2, const int size)
-{
-    int i, index;
-    int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    for( i = 0; i < CUDA_BLOCK; ++i )
-    {
-        index = ix * CUDA_BLOCK + i;
-        if( index < size)
-        {
-            v2[index] = v2[index] * v1[index];
-        }
-    }
-
-
-}
-
-/*********************************************//** 
- * vector scalar operation, update vec by,
- * vec = scalar - vec
- *********************************************/
-__global__
-void gpu_scalarMinusVec(real *vec, const real scalar)
-{
-    int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    vec[ix] = scalar - vec[ix];
-
-}
-
-/*********************************************//**
- * row sum:
- * return vector rowSum(A)
- *********************************************/
-real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
-{
-    cublasStatus_t stat;
-    cublasHandle_t handle;
-    stat=cublasCreate(&handle);
-    
-    real alpha = 1.f;
-    real beta = 0.f;
-    real *vec_one;
-    real *d_var;
-    
-    cudaMalloc((void **)&vec_one, sizeof(real) * A_ncols );
-    cudaMalloc((void **)&d_var, sizeof(real) * A_ncols);
-   
-    gpu_init_array(vec_one, 1.0, A_ncols);
-    gpu_init_array(d_var, 0.0, A_ncols);
-
-    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
-    
-    cudaFree(vec_one);
-    cublasDestroy(handle);
-    return d_var;
-}
-
-/*********************************************//**
- * getAa
- * aa_{ix, iy} = inputs_{ix} - testing_{iy} 
- *********************************************/
-__global__
-void gpu_getAa(const real *inputs,const real *testing, real *aa, const int aa_nrows, const int aa_ncols, const int aa_ld)
-{
-    int ix = blockIdx.x * blockDim.x + threadIdx.x;
-    int iy = blockIdx.y * blockDim.y + threadIdx.y;
-
-    aa[IDX2D(ix, iy, aa_ld)] = inputs[ix] - testing[iy];
-}
-
-
-
-
 /*********************************************//**
  * predict function:
  * This is the corresponding CUDA function of
@@ -200,17 +109,13 @@ for(kk =0; kk<10; kk++)
 
     cublasCheckErrors(CUBLAS_GEMM(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, d_temp_dot, M));
     cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
+    
     cudaFree(d_a);
     cudaFree(d_invQ);
     
-    nthread.x=1000; nthread.y=1; nthread.z=1;
-    nblock.x=ceil(float(N) * float(M) / float(CUDA_BLOCK)/1000); nblock.y=1; nblock.z=1;
-
-    gpu_elementwiseMult<<< nblock, nthread >>>(d_a_T, d_temp_dot, M * N);
-
+    gpu_elementwiseMult(d_a_T, d_temp_dot, M * N);
     d_var = gpu_rowSum(d_temp_dot, M, N);
-    
-    gpu_scalarMinusVec<<<N/1000, 1000 >>>(d_var, c_theta_exp[D]);
+    gpu_scalarMinusVec(d_var, c_theta_exp[D], N );
     cudaMemcpy(c_var, d_var, sizeof(real) * N, cudaMemcpyDeviceToHost);
 
     cudaFree(d_var);
@@ -223,6 +128,10 @@ for(kk =0; kk<10; kk++)
     ********************************/
     real *d_deriv;
     cudaMalloc((void **)&d_deriv, sizeof(real) * N );
+    
+    nthread.x=1000; nthread.y=1; nthread.z=1;
+    nblock.x=ceil(float(N) * float(M) / float(CUDA_BLOCK)/1000); nblock.y=1; nblock.z=1;
+
 
     dim3 nblocks_getaa(M, N/1000);
     dim3 nthreads_getaa(1,1000);
@@ -234,23 +143,14 @@ for(kk =0; kk<10; kk++)
     ptr_deriv = c_deriv;
 
      for( i = 0; i < D; ++i){
-        gpu_getAa <<< nblocks_getaa, nthreads_getaa >>>(ptr_inputs, ptr_testing, d_aa, M, N, M);
+        gpu_crossMinus(ptr_inputs, ptr_testing, d_aa, M, N );
         ptr_inputs = ptr_inputs + M;
         ptr_testing = ptr_testing + N;
         alpha = c_theta_exp[i];
-        gpu_elementwiseMult <<< nblock, nthread >>>(d_a_T, d_aa, M * N);
+        gpu_elementwiseMult(d_a_T, d_aa, M * N);
         cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
-#define debug
-#undef debug
-#ifdef debug
-        for( j = 0; j < 10 ; ++j )
-            printf("%.4f|", ptr_deriv[j]);
-        printf("\n");
-#endif
-        ptr_deriv = ptr_deriv + N;
-
      }
 
      cudaFree(d_mu);
diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index 0079c26..3a63a48 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -20,7 +20,11 @@ cuda_add_executable(gpu_predict_test test1.cu
                     ../kernel_vectorTimesMatrix.cu
                     ../kernel_init_array.cu
                     ../kernel_cdist.cu
-                    ../kernel_matrixExp.cu)
+                    ../kernel_matrixExp.cu
+                    ../kernel_elementwiseMult.cu
+                    ../kernel_scalarMinusVec.cu
+                    ../kernel_rowSum.cu
+                    ../kernel_crossMinus.cu)
 cuda_add_cublas_to_target(gpu_predict_test)
 target_link_libraries(gpu_predict_test ${CUNIT_LIBRARY})
 
diff --git a/gp_emulator/gpu/tests/testMatrixExp.cu b/gp_emulator/gpu/tests/testMatrixExp.cu
index ced6cf8..247a8e4 100644
--- a/gp_emulator/gpu/tests/testMatrixExp.cu
+++ b/gp_emulator/gpu/tests/testMatrixExp.cu
@@ -8,15 +8,15 @@ void testMatrixExp(const real *mat, const real *res, const real alpha,const real
     real *d_mat;
     real *gpu_res;
 
-    gpu_res = (real *)malloc(sizeof(real) * size);
-    cudaMalloc((void **)&d_mat, sizeof(real) * size);
+    gpu_res = (real *)malloc( sizeof(real) * size );
+    cudaMalloc( (void **)&d_mat, sizeof(real) * size );
 
-    cudaMemcpy(d_mat, mat, sizeof(real) * size, cudaMemcpyHostToDevice);
+    cudaMemcpy( d_mat, mat, sizeof(real) * size, cudaMemcpyHostToDevice );
 //    int nblocks , nthreads;
 //    nthreads=1000; 
 //    nblocks=ceil(float(size) / float(CUDA_BLOCK) / 1000);
     
-    gpu_matrixExp(d_mat, alpha, beta, size);
+    gpu_matrixExp( d_mat, alpha, beta, size );
 
     cudaMemcpy( gpu_res, d_mat, sizeof(real) * size, cudaMemcpyDeviceToHost);
     

From f2b20ac1a0f602cbf4b2e5dd2f81f0a4eedd1c3e Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 15 Jun 2015 17:32:19 +0100
Subject: [PATCH 064/117] modify python tests

---
 tests/benchmark.py   | 14 ++++----
 tests/unit_tests.py  | 31 ++++++----------
 tests/unit_tests2.py | 84 +++++++++++++++++++++++++++++---------------
 3 files changed, 74 insertions(+), 55 deletions(-)

diff --git a/tests/benchmark.py b/tests/benchmark.py
index fc6c0ef..47dda47 100755
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -19,7 +19,7 @@ def set_testing_val (self, P, N, M):
     print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
     print '-----------------------------'
 
-    for i in xrange(np.int(1e3), np.int(1e7), np.int(1e3)):
+    for i in xrange(np.int(1e4), np.int(1e7), np.int(1e4)):
         N=i
         M=250
         P=10
@@ -38,7 +38,7 @@ def set_testing_val (self, P, N, M):
     	
         #GPU predict
         start = time.time()
-    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float32', threashold = 1e3)
+    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float64', threashold = 2e5)
     	end =time.time()
     	gputime = end - start
         print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (N, cputime, gputime, cputime/gputime),
@@ -47,14 +47,14 @@ def set_testing_val (self, P, N, M):
     
         # checking results
         try: 
-            e_mu  = max(abs(mu_c - mu_g)) / np.max(abs(mu_c))
+            e_mu  = max(abs(mu_c - mu_g) ) / np.max(abs(mu_c))
             e_var = max(abs(var_c - var_g)) / np.max(abs(var_c))
             e_deriv = np.max(abs(deriv_c - deriv_g)) / np.max(abs(deriv_c))
         except ValueError:
             print 'Results have invalid data type or dimension.'
-        if e_mu > 1e-5 or e_var > 1e-5 or e_deriv > 1e-5:
-            print 'Failed: ', 'e_mu=', e_mu, 'e_var=', e_var, 'e_deriv=',e_deriv
+        #if e_mu > 1e-5 or e_var > 1e-5 or e_deriv > 1e-5:
+        print '', 'e_mu=', e_mu, 'e_var=', e_var, 'e_deriv=',e_deriv
 #            break
-        else:
-            print 'Pass'
+        #else:
+        #    print 'Pass'
 
diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index f509fe2..14c0a3e 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -11,13 +11,6 @@ def __init__ (self, P, N, M):
         self.invQ = np.random.random((M,M))
         self.invQt = np.random.random ((M))
 
-
-        self.theta= np.fromfile("theta")
-        self.inputs=np.fromfile("inputs").reshape((M, D))
-        self.invQ = np.fromfile("invQ").reshape((M, M))
-        self.invQt = np.fromfile("invQt")
-        
-
     def get_testing_val (self, testing, do_unc = True):
         
         ( nn, D ) = testing.shape
@@ -38,8 +31,6 @@ def get_testing_val (self, testing, do_unc = True):
         cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
 
         mu = np.dot( cdist_test_var_4.T, self.invQt)
-
-
         var_test_1 = np.dot(self.invQ, cdist_test_var_4)
         
         
@@ -91,15 +82,15 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-
-    N=1000
-    M=250
-    P=10
-    testing=np.random.random((N,P))
-    testing=np.fromfile("X").reshape((N,P))
-    gp=GP(P,N,M)
-    
-    gp.get_testing_val(testing)
-    command = "./gpu_predict_test  %d %d %d" % (M, N, P)
-    os.system(command)
+    for i in xrange(1000, 10000, 1000):
+        N=i
+        M=250
+        P=10
+        
+        testing=np.random.random((N,P))
+        gp=GP(P,N,M)
+        
+        gp.get_testing_val(testing)
+        command = "./gpu_predict_test  %d %d %d" % (M, N, P)
+        os.system(command)
    
diff --git a/tests/unit_tests2.py b/tests/unit_tests2.py
index 6a64ec0..1555ccd 100644
--- a/tests/unit_tests2.py
+++ b/tests/unit_tests2.py
@@ -19,10 +19,22 @@
 
 
 
-def get_testing_val (self, testing, do_unc = True):  
+def get_testing_val (self, testing, prec, do_unc = True):  
+    """Produce data for testing. It is a duplication of GaussianProcess.predict(), 
+    for writting testing data. 
+    """
     ( nn, D ) = testing.shape
     assert D == self.D
     expX=np.exp(self.theta)
+
+    if prec == "float32":
+        testing = np.float32(testing)
+        self.inputs = np.float32(self.inputs)
+        self.invQ = np.float32(self.invQ)
+        self.invQt = np.float32(self.invQt)
+        expX = np.float32(expX)
+
+
     
     t_expX = expX
     t_expXsqrt = np.sqrt(expX[:(self.D)])
@@ -30,27 +42,39 @@ def get_testing_val (self, testing, do_unc = True):
     t_testing = testing
     t_invQ = self.invQ
     
-    cdist_test_var_1= t_expXsqrt * t_inputs #np.sqrt(expX[:(self.D)])*self.inputs
+    cdist_test_var_1 = np.sqrt(expX[:(self.D)])*self.inputs
     cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
     cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
+    # avoid cdist function converting automatically the result to float64
+    #if prec is 'float32':
+    #    cdist_test_var_3 = np.float32(cdist_test_var_3)
+
     cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
-    
     mu = np.dot( cdist_test_var_4.T, self.invQt)
+    
     var_test_1 = np.dot(self.invQ, cdist_test_var_4)     
     
-    t_expX.tofile("./data/expX.bin")
-    t_expXsqrt.tofile("./data/expXsqrt.bin")
-    t_inputs.tofile("./data/inputs.bin")
-    t_testing.tofile("./data/testing.bin")
-    cdist_test_var_1.tofile("./data/cdist_test_var1.bin")
-    cdist_test_var_2.tofile("./data/cdist_test_var2.bin")
-    cdist_test_var_3.tofile("./data/cdist_a.bin")
-    cdist_test_var_4.tofile("./data/cdist_expa.bin")    
+    if prec is 'float32':
+        assert cdist_test_var_1.dtype is np.dtype('float32')
+        assert cdist_test_var_2.dtype is np.dtype('float32')
+        assert cdist_test_var_3.dtype is np.dtype('float32')
+        assert cdist_test_var_4.dtype is np.dtype('float32')
         
-    t_invQ.tofile("./data/invQ.bin")
-    mu.tofile("./data/mu.bin")
-    var_test_1.tofile("./data/var_test1.bin")
-    self.invQt.tofile("./data/invQt.bin")
+        assert var_test_1.dtype is np.dtype('float32')
+
+    np.float64(t_expX).tofile("./data/expX.bin")
+    np.float64(t_expXsqrt).tofile("./data/expXsqrt.bin")
+    np.float64(t_inputs).tofile("./data/inputs.bin")
+    np.float64(t_testing).tofile("./data/testing.bin")
+    np.float64(cdist_test_var_1).tofile("./data/cdist_test_var1.bin")
+    np.float64(cdist_test_var_2).tofile("./data/cdist_test_var2.bin")
+    np.float64(cdist_test_var_3).tofile("./data/cdist_a.bin")
+    np.float64(cdist_test_var_4).tofile("./data/cdist_expa.bin")    
+        
+    np.float64(t_invQ).tofile("./data/invQ.bin")
+    np.float64(mu).tofile("./data/mu.bin")
+    np.float64(var_test_1).tofile("./data/var_test1.bin")
+    np.float64(self.invQt).tofile("./data/invQt.bin")
 
 
     ( nn, D ) = testing.shape
@@ -74,8 +98,8 @@ def get_testing_val (self, testing, do_unc = True):
         deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
         #print deriv[0:10,d]
 
-    var.tofile("./data/var.bin")
-    deriv.tofile("./data/deriv.bin")
+    np.float64(var).tofile("./data/var.bin")
+    np.float64(deriv).tofile("./data/deriv.bin")
 
     if do_unc:
         return mu, var, deriv
@@ -92,6 +116,7 @@ def perband_emulators ( emulators, band_pass ):
         for i in xrange( n_bands ) ]
     x_train_pband = np.array ( x_train_pband )
     emus = []
+    # add get_testing_val 
     GaussianProcess.get_testing_val = MethodType(get_testing_val, None, GaussianProcess)
 
     for i in xrange( n_bands ):
@@ -103,7 +128,7 @@ def perband_emulators ( emulators, band_pass ):
     return emus
 
 
-
+precision = "float64"
 new_output = d[:,10:][:,452:486].mean(axis=1)
 band_pass = np.zeros( (1,2101), dtype=np.bool)
 band_pass[:, 442:476] = 1
@@ -112,22 +137,25 @@ def perband_emulators ( emulators, band_pass ):
 
 print isinstance ( gp_single[0], GaussianProcess)
 X = d[:, :10]
-gpur = gp_single[0].predict ( X ,is_gpu=True)
+gpur = gp_single[0].predict ( X ,is_gpu=True, prec = precision)
 cpur = gp_single[0].predict ( X, is_gpu=False)
+print max(abs(gpur[0]-cpur[0]))
 
-
-
-gp_single[0].get_testing_val(X)
+gp_single[0].get_testing_val(X, prec = 'float64')
 command = "./gpu_predict_test  %d %d %d" % (250, 1000, 10)
 os.system(command)
 
-#print X
-print 'predict finish'
-#plt.plot ( new_output, r,'o')
-#plt.plot([0, 0.6], [0, 0.6], 'k--')
+for i in xrange(3):
+    val_absolute = np.mean(np.abs(cpur[i]))
+    error_absolute = np.abs( cpur[i] - gpur[i] )
+    error_relative = error_absolute / np.abs( cpur[i] )
+    print '\n--------------'
+    print 'output', i, precision 
+    print 'absolute_val\t%.2g'%val_absolute
+    print 'error_absolute\t', '%.2g [average]\t%.2g [max]'% (np.mean(error_absolute), np.max(error_absolute))
+    print 'error_relative\t', '%.2g [average]\t%.2g [max]'% (np.mean(error_relative), np.max(error_relative))
+
 
-#plt.show()
-#%timeit r = gp_single[0].predict ( X )[0]
 
 
 

From b35f1a90d3f9055bbebc2afa52d97bdb5b3de3fd Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Mon, 15 Jun 2015 17:54:36 +0100
Subject: [PATCH 065/117] correct type error of pow

---
 gp_emulator/gpu/tests/testInitArray.cu | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/gp_emulator/gpu/tests/testInitArray.cu b/gp_emulator/gpu/tests/testInitArray.cu
index d512286..5ebf743 100644
--- a/gp_emulator/gpu/tests/testInitArray.cu
+++ b/gp_emulator/gpu/tests/testInitArray.cu
@@ -11,8 +11,8 @@ void testInitArray(void)
  
     for( test = 0; test < 6; ++test )
     {
-        val = pow( 10, test-5);
-        len = pow( 10, test );
+        val = pow( 10.0, test-5);
+        len = pow( 10.0, test );
         gpu_init_array(vec_g, val, len);
         cudaMemcpy(vec_g, vec_c, sizeof(real) * len, cudaMemcpyDeviceToHost);
         

From 3b4f6518117831a9001f5ccf5a48071ae703648e Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 16 Jun 2015 10:32:01 +0100
Subject: [PATCH 066/117] remove matplotlib

---
 tests/unit_tests2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/unit_tests2.py b/tests/unit_tests2.py
index 1555ccd..e3b2d5f 100644
--- a/tests/unit_tests2.py
+++ b/tests/unit_tests2.py
@@ -1,5 +1,5 @@
 import numpy as np
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
 from gp_emulator import MultivariateEmulator, GaussianProcess
 from types import MethodType
 import scipy.spatial.distance as dist

From 1dc55fdf55b0514616fe06254b22153eeae6954d Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 16 Jun 2015 10:50:04 +0100
Subject: [PATCH 067/117] temproary remove matplotlib in multivariate

---
 gp_emulator/multivariate_gp.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gp_emulator/multivariate_gp.py b/gp_emulator/multivariate_gp.py
index d568743..379741c 100644
--- a/gp_emulator/multivariate_gp.py
+++ b/gp_emulator/multivariate_gp.py
@@ -30,7 +30,7 @@
 import shutil
 
 import numpy as np
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
 
 from GaussianProcess import GaussianProcess
 

From d95f3d44c9a977fa1bc3b74979bc421463823f6b Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 16 Jun 2015 13:49:34 +0100
Subject: [PATCH 068/117] fix the bug of deriv

---
 gp_emulator/gpu/kernel_crossMinus.cu |  6 +++---
 gp_emulator/gpu/predict.cu           | 22 +++-------------------
 2 files changed, 6 insertions(+), 22 deletions(-)

diff --git a/gp_emulator/gpu/kernel_crossMinus.cu b/gp_emulator/gpu/kernel_crossMinus.cu
index 80af8d6..8340bbe 100644
--- a/gp_emulator/gpu/kernel_crossMinus.cu
+++ b/gp_emulator/gpu/kernel_crossMinus.cu
@@ -19,11 +19,11 @@ void gpu_crossMinus(const real *v1, const real *v2, real *mat_res, const int v1_
 {
     dim3 nthread, nblock;
     if( v2_len < 1024 )
-        nthread.x = v2_len;
+        nthread.y = v2_len;
     else
-        nthread.x = 1024;
+        nthread.y = 1024;
     
-    nthread.y = 1;
+    nthread.x = 1;
     nblock.x = v1_len;
     nblock.y = ceil( float(v2_len) / float(nthread.y) );
 
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index d034950..d6cd85b 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -6,7 +6,7 @@
  *********************************************/
 #include "gpu_predict.h"
 #include <stdlib.h>
-#define debug 
+#define debug
 /*********************************************//**
  * predict function:
  * This is the corresponding CUDA function of
@@ -70,20 +70,6 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
 
     cudaFree(d_res_temp1);
     cudaFree(d_res_temp2);
-int kk;
-#undef debug
-#ifdef debug
-real *h_a;
-h_a=(real *)malloc(sizeof(real) * M * N);
-cudaMemcpy(h_a, d_a, sizeof(real) * M * N, cudaMemcpyDeviceToHost);
-
-for(kk =0; kk<10; kk++)
-  printf("%f|", h_a[kk]);
-  printf("\n");
-#endif
-
-    
-      
 
     /*********************************
      * compute mu:
@@ -129,10 +115,7 @@ for(kk =0; kk<10; kk++)
     real *d_deriv;
     cudaMalloc((void **)&d_deriv, sizeof(real) * N );
     
-    nthread.x=1000; nthread.y=1; nthread.z=1;
-    nblock.x=ceil(float(N) * float(M) / float(CUDA_BLOCK)/1000); nblock.y=1; nblock.z=1;
-
-
+   
     dim3 nblocks_getaa(M, N/1000);
     dim3 nthreads_getaa(1,1000);
     real *d_aa;
@@ -151,6 +134,7 @@ for(kk =0; kk<10; kk++)
         cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
+        ptr_deriv = ptr_deriv + N;
      }
 
      cudaFree(d_mu);

From 0d47196f0ab36c1ed8b15901693833ce9b25ac11 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Tue, 16 Jun 2015 16:31:25 +0100
Subject: [PATCH 069/117] tidy up; remove error message of predict

---
 gp_emulator/gpu/gpu_predict.h            | 2 +-
 gp_emulator/gpu/predict.cu               | 2 +-
 gp_emulator/gpu/tests/gpu_predict_test.h | 2 +-
 gp_emulator/gpu/tests/testCdist.cu       | 2 +-
 gp_emulator/gpu/tests/testInitArray.cu   | 4 ++--
 5 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 14500fc..e1c998f 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,7 +11,7 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
-#define DOUBLE__PRECISION
+//#define DOUBLE__PRECISION
 #define CUDA_BLOCK 2
  
 #ifdef DOUBLE__PRECISION
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index d6cd85b..21ff3c2 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -79,7 +79,7 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     cudaMalloc((void **)&d_mu, sizeof(real) * N);
     real alpha = 1.f;
     real beta = 0.f;
-    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1));
+    /*cublasCheckErrors(*/CUBLAS_GEMV(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1);//);
     cudaMemcpy(c_mu, d_mu, sizeof(real) * N, cudaMemcpyDeviceToHost);
        
     
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index d6f0dad..c75ba69 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -15,7 +15,7 @@ static real *var_test1;
 static real *mu, *var, *deriv;
 
 static  int M, N, D;
-#define epsilon 1e-3
+#define epsilon 1e-5
 
 real *readTestData(char *file_name, int M, int N, int D, int size);
 
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index eb564b1..a07ee01 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -4,7 +4,7 @@
 
 void testCdist( const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols )
 {
-    int i, j, k, error;
+    int i, error;
     real *in1_T, *in2_T, *gpu_res;
     real *d_in1, *d_in2, *d_res; 
 
diff --git a/gp_emulator/gpu/tests/testInitArray.cu b/gp_emulator/gpu/tests/testInitArray.cu
index 5ebf743..b1a229c 100644
--- a/gp_emulator/gpu/tests/testInitArray.cu
+++ b/gp_emulator/gpu/tests/testInitArray.cu
@@ -6,8 +6,8 @@ void testInitArray(void)
     real val;
     int len;
     int i, test, error;
-    vec_c = (real *)malloc( sizeof(real) * 1e7 );
-    cudaMalloc( (void **)&vec_g, sizeof(real) * 1e7 );
+    vec_c = (real *)malloc( int(sizeof(real) * 1e7) );
+    cudaMalloc( (void **)&vec_g, int(sizeof(real) * 1e7) );
  
     for( test = 0; test < 6; ++test )
     {

From 8f860556355203a8642c8dfcd2e81899600af72a Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 16 Jun 2015 17:35:07 +0100
Subject: [PATCH 070/117] tidyup

---
 gp_emulator/gpu/predict.cu              | 6 +++---
 gp_emulator/gpu/tests/testCublasgemm.cu | 8 +++-----
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 21ff3c2..263bada 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -16,11 +16,11 @@ extern "C"{
 void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
         real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
 {
-    int i,j;
-    cublasStatus_t stat;
+    int i;
+//    cublasStatus_t stat;
     cublasHandle_t handle;
     
-    stat=cublasCreate(&handle);
+    cublasCreate(&handle);
 
     real *c_theta_exp_sqrt;
     c_theta_exp_sqrt = (real *)malloc( sizeof(real) * theta_size );
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index 5fd0246..6afb0d1 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -2,13 +2,11 @@
 
 void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols)
 {
-    cublasStatus_t stat;
     cublasHandle_t handle;
-    stat=cublasCreate(&handle);
+    cublasCreate(&handle);
     
 
     int i;
-    real e;
 
     real *d_mat1, *d_mat2, *d_res;
     real *c_gpu_res;
@@ -43,7 +41,7 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
     cudaMemcpy( d_mat2, c_mat2, sizeof(real) * mat1_nrows * mat2_ncols, cudaMemcpyHostToDevice);
 
 
-/*    real alpha = 1.f;
+    real alpha = 1.f;
     real beta = 0.f;
     cublasCheckErrors( CUBLAS_GEMM( handle, CUBLAS_OP_N, CUBLAS_OP_T, 
                 mat1_nrows, mat2_ncols,  mat1_ncols,
@@ -68,7 +66,7 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
     if( error != 0 )
         printf("  ERROR: cublasgemm [%d/%d]   ", error, mat1_nrows*mat2_ncols);
     CU_ASSERT( error == 0);
-*/
+
 
 
 

From fca0d0bb2a6b100ff48ce77e3930689a5e92dfd0 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Tue, 16 Jun 2015 17:35:35 +0100
Subject: [PATCH 071/117] tidyup

---
 gp_emulator/gpu/tests/testCublasgemm.cu | 16 ----------------
 1 file changed, 16 deletions(-)

diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index 6afb0d1..1e7ef26 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -18,24 +18,9 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
 
 
     real *c_mat1_T;
-/*    c_mat1_T = (real *)malloc(sizeof(real)* mat1_ncols * mat1_nrows);
-    c_mat2_T = (real *)malloc(sizeof(real)* mat2_ncols * mat2_nrows);
-
-
-
-    for( i = 0; i < mat1_ncols * mat1_nrows; i++ )
-        c_mat1_T[i] = c_mat1[i];
-
-    for( i = 0; i < mat2_ncols * mat2_nrows; i++ )
-        c_mat2_T[i] = c_mat2[i];
-
-  */  
     
     
    c_mat1_T = computeTranspose(c_mat1, mat1_nrows, mat1_ncols); 
-//   computeTranspose(c_mat2_T, mat2_ncols, mat2_nrows);
-    
-
 
     cudaMemcpy( d_mat1, c_mat1_T, sizeof(real) * mat1_nrows * mat1_ncols, cudaMemcpyHostToDevice);
     cudaMemcpy( d_mat2, c_mat2, sizeof(real) * mat1_nrows * mat2_ncols, cudaMemcpyHostToDevice);
@@ -71,7 +56,6 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
 
 
     free(c_mat1_T);
-    //free(c_mat2_T);
     cudaFree(d_mat1);
     cudaFree(d_mat2);
     cudaFree(d_res);

From 16711db2b0588aee0b724128acd297e7baece208 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 00:04:08 +0100
Subject: [PATCH 072/117] tidyup

---
 gp_emulator/gpu/predict.cu                    |   8 +-
 .../gpu/tests/testGetPredictDataFromPython.cu |  35 ------
 gp_emulator/gpu/tests/test_py2c.c             | 105 ------------------
 .../gpu/tests/{test1.cu => unit_test.cu}      |   0
 4 files changed, 2 insertions(+), 146 deletions(-)
 delete mode 100644 gp_emulator/gpu/tests/testGetPredictDataFromPython.cu
 delete mode 100644 gp_emulator/gpu/tests/test_py2c.c
 rename gp_emulator/gpu/tests/{test1.cu => unit_test.cu} (100%)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 263bada..9ddecd5 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -9,8 +9,8 @@
 #define debug
 /*********************************************//**
  * predict function:
- * This is the corresponding CUDA function of
- * GaussianPredict:predict in python code.
+ * CUDA version of the corresponding python code
+ * GaussianPredict::predict
  *********************************************/
 extern "C"{
 void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
@@ -56,7 +56,6 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
      * a = cidist(sqrt(expX_{,D}) * inputs * sqrt(expX_{,D} * testing)))
      * Notice: a is equivalent to a^T in python due to column major fashion
      ********************************/ 
-    dim3 nthread, nblock;
     real *d_res_temp1, *d_res_temp2, *d_a;
     cudaMalloc((void **)&d_res_temp1, sizeof(real) * M * D);
     cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
@@ -114,10 +113,7 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     ********************************/
     real *d_deriv;
     cudaMalloc((void **)&d_deriv, sizeof(real) * N );
-    
    
-    dim3 nblocks_getaa(M, N/1000);
-    dim3 nthreads_getaa(1,1000);
     real *d_aa;
     cudaMalloc((void **)&d_aa, sizeof(real) * M * N);
     real *ptr_inputs, *ptr_testing, *ptr_deriv;
diff --git a/gp_emulator/gpu/tests/testGetPredictDataFromPython.cu b/gp_emulator/gpu/tests/testGetPredictDataFromPython.cu
deleted file mode 100644
index 9505ced..0000000
--- a/gp_emulator/gpu/tests/testGetPredictDataFromPython.cu
+++ /dev/null
@@ -1,35 +0,0 @@
-#include "gpu_predict_test.h"
-
-/* ==== Set up the methods table ====================== */
-static PyMethodDef gpuMethods[] = {
-    {"predict_wrap", predict_wrap, METH_VARARGS},
-    {NULL, NULL}     /* Sentinel - marks the end of this structure */
-};
-
-/* ==== Initialize the C_test functions ====================== */
-//Module name must be _C_arraytest in compile and linked 
-void init_gpu_predict()  {
-	(void) Py_InitModule("_gpu_predict_test", gpuMethods);
-	import_array();  // Must be present for NumPy.  Called first after above line.
-}
-
-
-
-PyObject * testGetPredictDataFromPython(PyObject *self, PyObject *args)
-{
-    real *c_theta_exp, *c_invQt, *c_invQ;
-    real *c_testing, *c_inputs;
-    real *c_mu, c_var, c_deriv;
-    int N, M, D, theta_size;
-
-    getPredictDataFromPython(args, &c_theta_exp, &c_invQt, &c_invQ,
-                             &c_testing, &c_inputs,
-                             &c_mu, &c_var, &c_deriv,
-                             &N, &M, &D, &theta_size);
-
-
-
-
-}
-
-
diff --git a/gp_emulator/gpu/tests/test_py2c.c b/gp_emulator/gpu/tests/test_py2c.c
deleted file mode 100644
index 2a7271c..0000000
--- a/gp_emulator/gpu/tests/test_py2c.c
+++ /dev/null
@@ -1,105 +0,0 @@
-
-#include "gpu_predict.h"
-
-/* ==== Set up the methods table ====================== */
-static PyMethodDef gpuMethods[] = {
-    {"predict_wrap", predict_wrap, METH_VARARGS},
-	{NULL, NULL}     /* Sentinel - marks the end of this structure */
-};
-
-/* ==== Initialize the C_test functions ====================== */
-//Module name must be _C_arraytest in compile and linked 
-void init_gpu_predict()  {
-	(void) Py_InitModule("_gpu_predict", gpuMethods);
-	import_array();  // Must be present for NumPy.  Called first after above line.
-}
-
-
-
-
-
-/* ==== Create 1D Carray from PyArray ======================
-    Assumes PyArray is contiguous in memory.             */
-real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  {
-    int i,n;
-    //not_realvector(arrayin);    
-    n=arrayin->dimensions[0];
-    return  (real *) arrayin->data;  /* pointer to arrayin data as double */
-}
-
-
-
-
-PyObject *predict_wrap ( PyObject *self, PyObject *args )
-{
-    int N,M,D,theta_size;
-    PyArrayObject *py_theta_exp, *py_inputs, *py_invQt, *py_invQ, *py_testing;
-    PyArrayObject *py_mu, *py_var, *py_deriv;
-    real *c_theta_exp;
-    real *c_inputs, *c_invQt, *c_invQ, *c_testing;
-    real *c_mu, *c_var, *c_deriv;
-
-
-    PyArg_ParseTuple ( args, "O!O!O!O!O!O!O!O!iiii",
-        &PyArray_Type, &py_theta_exp,
-        &PyArray_Type, &py_inputs,
-        &PyArray_Type, &py_invQt,
-        &PyArray_Type, &py_invQ,
-        &PyArray_Type, &py_testing,
-        &PyArray_Type, &py_mu,
-        &PyArray_Type, &py_var,
-        &PyArray_Type, &py_deriv,
-        &N, &M, &D,&theta_size);
-
-    c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
-    c_inputs = pyvector_to_Carrayptrs( py_inputs );
-    c_invQt = pyvector_to_Carrayptrs( py_invQt );
-    c_invQ = pyvector_to_Carrayptrs( py_invQ );
-    c_testing = pyvector_to_Carrayptrs( py_testing );
-    c_mu = pyvector_to_Carrayptrs( py_mu );
-    c_var = pyvector_to_Carrayptrs( py_var );
-    c_deriv = pyvector_to_Carrayptrs( py_deriv );
-
-    //transpose 2D array to column major to cope with cublas
-    real *c_invQ_T, *c_inputs_T, *c_testing_T;
-    c_invQ_T = computeTranspose( c_invQ, M, M );
-    c_inputs_T = computeTranspose( c_inputs, D, M );
-    c_testing_T = computeTranspose( c_testing, D, N );
-    
-    predict( c_theta_exp, c_inputs_T, c_invQt, c_invQ_T, c_testing_T, 
-            c_mu, c_var, c_deriv,
-            N, M, D, theta_size );
-    int ii;
-    for(ii = 0;ii<10;ii++)
-        printf("%f|",c_testing[ii]);
-
-    free(c_invQ_T);
-    free(c_inputs_T);
-    free(c_testing_T);
-    
-    return Py_BuildValue("");
-
-    
-}
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/gp_emulator/gpu/tests/test1.cu b/gp_emulator/gpu/tests/unit_test.cu
similarity index 100%
rename from gp_emulator/gpu/tests/test1.cu
rename to gp_emulator/gpu/tests/unit_test.cu

From b7c09ab1ec077efdf4daf75db97267d795e5dde6 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 00:04:45 +0100
Subject: [PATCH 073/117] compare_result

---
 gp_emulator/gpu/tests/CMakeLists.txt     |  3 +-
 gp_emulator/gpu/tests/compare_result.cu  | 26 ++++++++++
 gp_emulator/gpu/tests/gpu_predict_test.h | 14 ++++--
 gp_emulator/gpu/tests/testCdist.cu       | 25 ++++------
 gp_emulator/gpu/tests/testCublasgemm.cu  | 17 +------
 gp_emulator/gpu/tests/testMatrixExp.cu   | 18 +------
 gp_emulator/gpu/tests/testPredict.cu     | 61 +++---------------------
 gp_emulator/gpu/tests/testVecTimesMat.cu | 16 +------
 8 files changed, 56 insertions(+), 124 deletions(-)
 create mode 100644 gp_emulator/gpu/tests/compare_result.cu

diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index 3a63a48..12722ea 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -7,8 +7,9 @@ find_path(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
 
 include_directories(${CUNIT_INCLUDE_DIR})
 
-cuda_add_executable(gpu_predict_test test1.cu 
+cuda_add_executable(gpu_predict_test unit_test.cu 
                     readTestData.cu     
+                    compare_result.cu
                     testCdist.cu        
                     testCublasgemm.cu   
                     testMatrixExp.cu    
diff --git a/gp_emulator/gpu/tests/compare_result.cu b/gp_emulator/gpu/tests/compare_result.cu
new file mode 100644
index 0000000..aeb8a1d
--- /dev/null
+++ b/gp_emulator/gpu/tests/compare_result.cu
@@ -0,0 +1,26 @@
+#include "gpu_predict_test.h"
+//#define max(a,b) (((a)>(b))?(a):(b))
+
+void compare_result( const real *test_val, const real *origin_val, const int len, const real epsilon_average, const real epsilon_max, char var_name[])
+{
+    int i;
+    real e = 0.0 ;
+    real error = 0.0;
+    real error_max = 0.0;
+
+    for( i = 0; i < len; ++i )
+    {
+        e = abs( test_val[i] - origin_val[i] ) / abs( origin_val[i] );
+        error += e;
+        error_max = max( error_max, e );
+    } 
+    error = error / len;
+
+    if(error > epsilon_average || error_max > epsilon_max)
+        printf("        [%s] Average error = %.1e, Max error = %.1e\n",var_name, error, error_max);
+    CU_ASSERT(error < epsilon_average);
+    CU_ASSERT(error_max < epsilon_max);
+
+}
+
+
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index c75ba69..415ffa1 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -4,9 +4,6 @@
 #include "CUnit/Basic.h"
 #include <cuda.h>
 
-
-
-
 static real *expX, *expXsqrt, *inputs, *testing, *invQ, *invQt;
 static real *cdist_a, *cdist_expa;
 static real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
@@ -15,9 +12,17 @@ static real *var_test1;
 static real *mu, *var, *deriv;
 
 static  int M, N, D;
-#define epsilon 1e-5
+#ifdef DOUBLE__PRECISION
+    #define EPSILON_AVG 1e-6
+    #define EPSILON_MAX 1e-2
+#else
+    #define EPSILON_AVG 1e-3
+    #define EPSILON_MAX 1
+#endif
+
 
 real *readTestData(char *file_name, int M, int N, int D, int size);
+void compare_result( const real *test_val, const real *origin_val, const int len, const real epsilon_average, const real epsilon_max, char var_name[]);
 
 
 void testInitArray(void);
@@ -27,3 +32,4 @@ void testCdist(const real *in1,const real *in2, const real *res, const int in1_n
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
 void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols);
 void testPredict(const real *expX, const real *inputs, const real *invQt, const real *invQ, const real *testing, const real *mu, const real *var, const real *deriv, int M, int N, int D);
+
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index a07ee01..9e0de07 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -30,22 +30,13 @@ void testCdist( const real *in1,const real *in2, const real *res, const int in1_
     gpu_cdist(d_in1, d_in2, d_res, in1_nrows, in_ncols, in2_nrows, in_ncols);
     cudaMemcpy(gpu_res, d_res, sizeof(real) * in2_nrows * in1_nrows, cudaMemcpyDeviceToHost);
     
-    error = 0;
-    for( i = 0; i < in1_nrows * in2_nrows; i++ )
-    {
-         if( abs( res[i] - gpu_res[i] ) / res[i] > epsilon )
-             error++;
-    }
-    if( error != 0)
-        printf( "  cdist: [%d/%d]\n", error, in1_nrows*in2_nrows );
- 
-   CU_ASSERT( error == 0 );
-
-   free(in1_T);
-   free(in2_T);
-   free(gpu_res);
+    compare_result(gpu_res, res, in1_nrows * in2_nrows, EPSILON_AVG, EPSILON_MAX, "RESULT");
+    
+    free(in1_T);
+    free(in2_T);
+    free(gpu_res);
    
-   cudaFree(d_in1);
-   cudaFree(d_in2);
-   cudaFree(d_res);
+    cudaFree(d_in1);
+    cudaFree(d_in2);
+    cudaFree(d_res);
 }
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index 1e7ef26..fe37fb7 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -37,24 +37,9 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, c
                 d_res, mat1_ncols ));
 
     cudaMemcpy( c_gpu_res, d_res, sizeof(real) * mat1_nrows *  mat2_ncols, cudaMemcpyDeviceToHost);
-
- 
-
     c_gpu_res = computeTranspose(c_gpu_res, mat1_nrows, mat2_ncols); 
-    int error = 0;
-    for(i = 0; i < mat1_nrows * mat2_ncols; i++)
-    {
-        if( abs( c_gpu_res[i] - c_res[i] ) / c_res[i] > epsilon )
-            error++;
-    }
+    compare_result( c_gpu_res, c_res, mat1_nrows * mat2_ncols, EPSILON_AVG, EPSILON_MAX, "RESULTS");
     
-    if( error != 0 )
-        printf("  ERROR: cublasgemm [%d/%d]   ", error, mat1_nrows*mat2_ncols);
-    CU_ASSERT( error == 0);
-
-
-
-
     free(c_mat1_T);
     cudaFree(d_mat1);
     cudaFree(d_mat2);
diff --git a/gp_emulator/gpu/tests/testMatrixExp.cu b/gp_emulator/gpu/tests/testMatrixExp.cu
index 247a8e4..8019b79 100644
--- a/gp_emulator/gpu/tests/testMatrixExp.cu
+++ b/gp_emulator/gpu/tests/testMatrixExp.cu
@@ -3,8 +3,6 @@
 
 void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size)
 {
-    int i;
-    int error = 0;
     real *d_mat;
     real *gpu_res;
 
@@ -12,23 +10,9 @@ void testMatrixExp(const real *mat, const real *res, const real alpha,const real
     cudaMalloc( (void **)&d_mat, sizeof(real) * size );
 
     cudaMemcpy( d_mat, mat, sizeof(real) * size, cudaMemcpyHostToDevice );
-//    int nblocks , nthreads;
-//    nthreads=1000; 
-//    nblocks=ceil(float(size) / float(CUDA_BLOCK) / 1000);
-    
     gpu_matrixExp( d_mat, alpha, beta, size );
-
     cudaMemcpy( gpu_res, d_mat, sizeof(real) * size, cudaMemcpyDeviceToHost);
-    
-    for( i = 0; i < size; i++ )
-    {
-        if(abs( gpu_res[i] - res[i] ) / res[i] > epsilon)
-            error++;
-    }
-    
-    if( error != 0)
-        printf("MatrixExp error [%d/%d]", error,size);
-    CU_ASSERT( error == 0 );
+    compare_result( gpu_res, res, size, EPSILON_AVG, EPSILON_MAX, "RESULT");
     
     cudaFree(d_mat);
     free(gpu_res);
diff --git a/gp_emulator/gpu/tests/testPredict.cu b/gp_emulator/gpu/tests/testPredict.cu
index 0d83d20..07c9f68 100644
--- a/gp_emulator/gpu/tests/testPredict.cu
+++ b/gp_emulator/gpu/tests/testPredict.cu
@@ -5,8 +5,6 @@ void testPredict(const real *expX, const real *inputs, const real *invQt, const
         const real *mu, const real *var, const real *deriv, int M, int N, int D)
 {
 
-    int i;
-
     clock_t begin1, begin2, end1, end2;
 
     real *gpu_mu, *gpu_var, *gpu_deriv;
@@ -37,59 +35,14 @@ void testPredict(const real *expX, const real *inputs, const real *invQt, const
     end1 = clock();
 
 
-    printf("\n");
-    printf("      -total time including matrix transposing = %f (sec)\n", (double)(end1-begin1)/CLOCKS_PER_SEC);
-    printf("      -pure predict function = %f (sec)\n", (double)(end2-begin2)/CLOCKS_PER_SEC);
+//    printf("\n");
+//    printf("      -total time including matrix transposing = %f (sec)\n", (double)(end1-begin1)/CLOCKS_PER_SEC);
+//    printf("      -pure predict function = %f (sec)\n", (double)(end2-begin2)/CLOCKS_PER_SEC);
+    
+    compare_result( gpu_mu, mu, N, EPSILON_AVG, EPSILON_MAX, "mu");
+    compare_result( gpu_var, var, N, EPSILON_AVG, EPSILON_MAX,  "var");
+    compare_result( gpu_deriv, deriv, N * D, EPSILON_AVG, EPSILON_MAX, "deriv");
     
-    int error_num;
-    real error;
-    error_num = 0;
-    error = 0.0;
-    for( i = 0; i < N * D; ++i )
-    {
-        if(abs( gpu_deriv[i] - deriv[i] ) / abs( deriv[i] ) > epsilon )
-        {
-            error_num++;
-            error += abs( gpu_deriv[i] - deriv[i] ) / abs( deriv[i] );
-        }
-
-    }
-    CU_ASSERT(error == 0);
-    if( error != 0 )
-        printf( "  error_deriv =  %f [%d/%d]   ", error/error_num, error_num,  N * D );
-
-    error_num = 0;
-    error = 0.0;
-    for( i = 0; i < N; ++i )
-    {
-        if( abs( gpu_mu[i] - mu[i] ) / abs( mu[i] )> epsilon )
-        {
-            error_num++;
-            error += abs( gpu_mu[i] - mu[i] ) / abs( mu[i] );
-        }
-    }
-    CU_ASSERT(error == 0);
-    if( error != 0 )
-        printf( "    error_mu = %f [%d/%d]   ", error/error_num, error_num, N );
-
-    error_num = 0;
-    error = 0.0;
-    for( i = 0; i < N; ++i)
-    {
-        if( abs( gpu_var[i] - var[i] ) / abs( var[i] ) > epsilon )
-        {
-            error_num++;
-            error += abs( gpu_var[i] - var[i] ) / abs( var[i] );
-        }
-    }
-    CU_ASSERT( error == 0 );
-    if( error != 0 )
-        printf( "  error_var: %f [%d/%d]   ",error/error_num, error_num, N );
-
-
-   
-
-        
     free(invQ_T);
     free(inputs_T);
     free(testing_T);
diff --git a/gp_emulator/gpu/tests/testVecTimesMat.cu b/gp_emulator/gpu/tests/testVecTimesMat.cu
index aa234f2..7bd4bee 100644
--- a/gp_emulator/gpu/tests/testVecTimesMat.cu
+++ b/gp_emulator/gpu/tests/testVecTimesMat.cu
@@ -5,12 +5,9 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
 {
     real *d_vec, *d_mat, *d_res;
     real *c_res_gpu;
-
     real *c_mat_T;
 
-
     CU_ASSERT (vec_len == mat_ncols);
-    int i;
     cudaMalloc((void **)&d_vec, sizeof(real) * vec_len );
     cudaMalloc((void **)&d_mat, sizeof(real) * mat_nrows * mat_ncols );
     cudaMalloc((void **)&d_res, sizeof(real) * mat_nrows * mat_ncols );
@@ -18,11 +15,8 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
     c_mat_T = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols);
     c_res_gpu = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols );
 
-/*    for( i = 0; i < mat_nrows * mat_ncols; i++ )
-        c_mat_T[i] = c_mat[i];*/
     c_mat_T = computeTranspose(c_mat, mat_ncols, mat_nrows);
 
-
     cublasCheckErrors(cublasSetVector( vec_len, sizeof(real), c_vec, 1, d_vec, 1 ));
     cublasCheckErrors(cublasSetMatrix( mat_nrows, mat_ncols, sizeof(real), c_mat_T, mat_nrows, d_mat, mat_nrows));
     
@@ -30,15 +24,7 @@ void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,con
 
     cudaMemcpy(c_res_gpu, d_res, sizeof(real) * mat_nrows * mat_ncols, cudaMemcpyDeviceToHost);
     c_res_gpu = computeTranspose(c_res_gpu, mat_nrows, mat_ncols);
-    
-    int error = 0;
-    for( i = 0; i < mat_nrows * mat_ncols; i++)
-    {
-        if( abs(c_res[i] - c_res_gpu[i]) / c_res[i] > epsilon )
-            error++;
-    }
-    
-    CU_ASSERT( error == 0);
+    compare_result( c_res_gpu, c_res, mat_nrows * mat_ncols, EPSILON_AVG, EPSILON_MAX, "RESULT");
 
     free(c_res_gpu);
     free(c_mat_T);

From 7b31a9317a562bff52e05d5e9dc6a15262dd1bce Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 00:31:35 +0100
Subject: [PATCH 074/117] tidyup

---
 build/.gitignore       |   4 +
 data/.gitignore        |   4 +
 tests/benchmark.py     |  13 ++--
 tests/data/.gitignore  |   4 +
 tests/test_emulator.py |  67 +++++++++++++++++
 tests/unit_tests.py    |   2 +-
 tests/unit_tests2.py   | 161 -----------------------------------------
 7 files changed, 87 insertions(+), 168 deletions(-)
 create mode 100644 build/.gitignore
 create mode 100644 data/.gitignore
 create mode 100644 tests/data/.gitignore
 create mode 100644 tests/test_emulator.py
 delete mode 100644 tests/unit_tests2.py

diff --git a/build/.gitignore b/build/.gitignore
new file mode 100644
index 0000000..5e7d273
--- /dev/null
+++ b/build/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/data/.gitignore b/data/.gitignore
new file mode 100644
index 0000000..5e7d273
--- /dev/null
+++ b/data/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/tests/benchmark.py b/tests/benchmark.py
index 47dda47..b66c44d 100755
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -38,7 +38,7 @@ def set_testing_val (self, P, N, M):
     	
         #GPU predict
         start = time.time()
-    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float64', threashold = 2e5)
+    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float32', threashold = 2e5)
     	end =time.time()
     	gputime = end - start
         print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (N, cputime, gputime, cputime/gputime),
@@ -52,9 +52,10 @@ def set_testing_val (self, P, N, M):
             e_deriv = np.max(abs(deriv_c - deriv_g)) / np.max(abs(deriv_c))
         except ValueError:
             print 'Results have invalid data type or dimension.'
-        #if e_mu > 1e-5 or e_var > 1e-5 or e_deriv > 1e-5:
-        print '', 'e_mu=', e_mu, 'e_var=', e_var, 'e_deriv=',e_deriv
-#            break
-        #else:
-        #    print 'Pass'
+        if e_mu > 1e-5 or e_var > 1e-5 or e_deriv > 1e-5:
+            print 'FAILED\t',
+        else:
+            print 'Pass\t',
+        print 'e_mu=%.2g\te_var=%.2g\te_deriv=%.2g\t'%(e_mu, e_var, e_deriv)
+
 
diff --git a/tests/data/.gitignore b/tests/data/.gitignore
new file mode 100644
index 0000000..5e7d273
--- /dev/null
+++ b/tests/data/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/tests/test_emulator.py b/tests/test_emulator.py
new file mode 100644
index 0000000..16a7a49
--- /dev/null
+++ b/tests/test_emulator.py
@@ -0,0 +1,67 @@
+import numpy as np
+#import matplotlib.pyplot as plt
+from gp_emulator import MultivariateEmulator, GaussianProcess
+from types import MethodType
+import scipy.spatial.distance as dist
+import os
+
+
+#d = np.loadtxt("../data/test_00100.txt", delimiter=",")
+d = np.loadtxt("../data/validation_set_100000.txt",delimiter=",")
+gp = MultivariateEmulator(dump="../data/prosail_vza30_sza0_saa0_vaa0_n250.npz")
+
+
+wv = np.arange (400, 2501)
+#for isample in [0, 10, 20, 50]:
+#    plt.plot ( wv, d[isample, 10:], '-', lw=2)
+#    r = gp.predict(d[isample,:10])[0]
+#    plt.plot ( wv, d[isample, 10:], '--', lw=2)
+
+def perband_emulators ( emulators, band_pass ):
+    """This function creates per band emulators from the full-spectrum
+    emulator. Should be faster in many cases"""
+    
+    n_bands = band_pass.shape[0]
+    x_train_pband = [ emulators.X_train[:,band_pass[i,:]].mean(axis=1) \
+        for i in xrange( n_bands ) ]
+    x_train_pband = np.array ( x_train_pband )
+    emus = []
+    # add get_testing_val 
+    #GaussianProcess.get_testing_val = MethodType(get_testing_val, None, GaussianProcess)
+
+    for i in xrange( n_bands ):
+        gp = GaussianProcess ( emulators.y_train[:]*1, \
+                x_train_pband[i,:] )
+        print 'p1', (emulators.y_train[:]*1).shape, 'p2', x_train_pband[i,:].shape
+        gp.learn_hyperparameters ( n_tries=5 )
+        emus.append ( gp )
+    return emus
+
+
+precision = "float32"
+new_output = d[:,10:][:,452:486].mean(axis=1)
+band_pass = np.zeros( (1,2101), dtype=np.bool)
+band_pass[:, 442:476] = 1
+gp_single = perband_emulators ( gp, band_pass )
+
+
+print isinstance ( gp_single[0], GaussianProcess)
+X = d[:, :10]
+gpur = gp_single[0].predict ( X ,is_gpu=True, prec = precision)
+cpur = gp_single[0].predict ( X, is_gpu=False)
+
+
+for i in xrange(3):
+    val_absolute = np.mean(np.abs(cpur[i]))
+    error_absolute = np.abs( cpur[i] - gpur[i] )
+    error_relative = error_absolute / np.abs( cpur[i] )
+    print '\n--------------'
+    print 'output', i, precision 
+    print 'absolute_val\t%.2g'%val_absolute
+    print 'error_absolute\t', '%.2g [average]\t%.2g [max]'% (np.mean(error_absolute), np.max(error_absolute))
+    print 'error_relative\t', '%.2g [average]\t%.2g [max]'% (np.mean(error_relative), np.max(error_relative))
+
+
+
+
+
diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index 14c0a3e..7818f1b 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -82,7 +82,7 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    for i in xrange(1000, 10000, 1000):
+    for i in xrange(10000, 1000000, 10000):
         N=i
         M=250
         P=10
diff --git a/tests/unit_tests2.py b/tests/unit_tests2.py
deleted file mode 100644
index e3b2d5f..0000000
--- a/tests/unit_tests2.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import numpy as np
-#import matplotlib.pyplot as plt
-from gp_emulator import MultivariateEmulator, GaussianProcess
-from types import MethodType
-import scipy.spatial.distance as dist
-import os
-
-
-#d = np.loadtxt("../data/test_00100.txt", delimiter=",")
-d = np.loadtxt("../data/validation_set_001000.txt",delimiter=",")
-gp = MultivariateEmulator(dump="../data/prosail_30_0_30_0.npz")
-
-
-wv = np.arange (400, 2501)
-#for isample in [0, 10, 20, 50]:
-#    plt.plot ( wv, d[isample, 10:], '-', lw=2)
-#    r = gp.predict(d[isample,:10])[0]
-#    plt.plot ( wv, d[isample, 10:], '--', lw=2)
-
-
-
-def get_testing_val (self, testing, prec, do_unc = True):  
-    """Produce data for testing. It is a duplication of GaussianProcess.predict(), 
-    for writting testing data. 
-    """
-    ( nn, D ) = testing.shape
-    assert D == self.D
-    expX=np.exp(self.theta)
-
-    if prec == "float32":
-        testing = np.float32(testing)
-        self.inputs = np.float32(self.inputs)
-        self.invQ = np.float32(self.invQ)
-        self.invQt = np.float32(self.invQt)
-        expX = np.float32(expX)
-
-
-    
-    t_expX = expX
-    t_expXsqrt = np.sqrt(expX[:(self.D)])
-    t_inputs = self.inputs
-    t_testing = testing
-    t_invQ = self.invQ
-    
-    cdist_test_var_1 = np.sqrt(expX[:(self.D)])*self.inputs
-    cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
-    cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
-    # avoid cdist function converting automatically the result to float64
-    #if prec is 'float32':
-    #    cdist_test_var_3 = np.float32(cdist_test_var_3)
-
-    cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
-    mu = np.dot( cdist_test_var_4.T, self.invQt)
-    
-    var_test_1 = np.dot(self.invQ, cdist_test_var_4)     
-    
-    if prec is 'float32':
-        assert cdist_test_var_1.dtype is np.dtype('float32')
-        assert cdist_test_var_2.dtype is np.dtype('float32')
-        assert cdist_test_var_3.dtype is np.dtype('float32')
-        assert cdist_test_var_4.dtype is np.dtype('float32')
-        
-        assert var_test_1.dtype is np.dtype('float32')
-
-    np.float64(t_expX).tofile("./data/expX.bin")
-    np.float64(t_expXsqrt).tofile("./data/expXsqrt.bin")
-    np.float64(t_inputs).tofile("./data/inputs.bin")
-    np.float64(t_testing).tofile("./data/testing.bin")
-    np.float64(cdist_test_var_1).tofile("./data/cdist_test_var1.bin")
-    np.float64(cdist_test_var_2).tofile("./data/cdist_test_var2.bin")
-    np.float64(cdist_test_var_3).tofile("./data/cdist_a.bin")
-    np.float64(cdist_test_var_4).tofile("./data/cdist_expa.bin")    
-        
-    np.float64(t_invQ).tofile("./data/invQ.bin")
-    np.float64(mu).tofile("./data/mu.bin")
-    np.float64(var_test_1).tofile("./data/var_test1.bin")
-    np.float64(self.invQt).tofile("./data/invQt.bin")
-
-
-    ( nn, D ) = testing.shape
-    assert D == self.D
-    
-    expX = np.exp ( self.theta )          
-    
-    a = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
-    a = expX[self.D]*np.exp(-0.5*a)
-    b = expX[self.D]
-    
-    mu = np.dot( a.T, self.invQt)
-    if do_unc:
-        var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
-    # Derivative and partial derivatives of the function
-    deriv = np.zeros ( ( nn, self.D ) )
-    
-    for d in xrange ( self.D ):
-        aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]   
-        c = a*aa.T
-        deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
-        #print deriv[0:10,d]
-
-    np.float64(var).tofile("./data/var.bin")
-    np.float64(deriv).tofile("./data/deriv.bin")
-
-    if do_unc:
-        return mu, var, deriv
-    else:
-        return mu, deriv
-
-
-def perband_emulators ( emulators, band_pass ):
-    """This function creates per band emulators from the full-spectrum
-    emulator. Should be faster in many cases"""
-    
-    n_bands = band_pass.shape[0]
-    x_train_pband = [ emulators.X_train[:,band_pass[i,:]].mean(axis=1) \
-        for i in xrange( n_bands ) ]
-    x_train_pband = np.array ( x_train_pband )
-    emus = []
-    # add get_testing_val 
-    GaussianProcess.get_testing_val = MethodType(get_testing_val, None, GaussianProcess)
-
-    for i in xrange( n_bands ):
-        gp = GaussianProcess ( emulators.y_train[:]*1, \
-                x_train_pband[i,:] )
-        print 'p1', (emulators.y_train[:]*1).shape, 'p2', x_train_pband[i,:].shape
-        gp.learn_hyperparameters ( n_tries=5 )
-        emus.append ( gp )
-    return emus
-
-
-precision = "float64"
-new_output = d[:,10:][:,452:486].mean(axis=1)
-band_pass = np.zeros( (1,2101), dtype=np.bool)
-band_pass[:, 442:476] = 1
-gp_single = perband_emulators ( gp, band_pass )
-
-
-print isinstance ( gp_single[0], GaussianProcess)
-X = d[:, :10]
-gpur = gp_single[0].predict ( X ,is_gpu=True, prec = precision)
-cpur = gp_single[0].predict ( X, is_gpu=False)
-print max(abs(gpur[0]-cpur[0]))
-
-gp_single[0].get_testing_val(X, prec = 'float64')
-command = "./gpu_predict_test  %d %d %d" % (250, 1000, 10)
-os.system(command)
-
-for i in xrange(3):
-    val_absolute = np.mean(np.abs(cpur[i]))
-    error_absolute = np.abs( cpur[i] - gpur[i] )
-    error_relative = error_absolute / np.abs( cpur[i] )
-    print '\n--------------'
-    print 'output', i, precision 
-    print 'absolute_val\t%.2g'%val_absolute
-    print 'error_absolute\t', '%.2g [average]\t%.2g [max]'% (np.mean(error_absolute), np.max(error_absolute))
-    print 'error_relative\t', '%.2g [average]\t%.2g [max]'% (np.mean(error_relative), np.max(error_relative))
-
-
-
-
-

From 119b69481591c9479cf58ab8225339dd0562cce1 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 01:08:57 +0100
Subject: [PATCH 075/117] tidyup

---
 README.rst                    | 44 ++++++++++++++++++++++++++++++++---
 gp_emulator/gpu/gpu_predict.h |  2 +-
 tests/benchmark.py            | 11 +++++----
 3 files changed, 48 insertions(+), 9 deletions(-)
 mode change 100755 => 100644 tests/benchmark.py

diff --git a/README.rst b/README.rst
index d640933..948be8a 100644
--- a/README.rst
+++ b/README.rst
@@ -8,8 +8,46 @@ GP emulators
 
 This repository contains an implementation of GPs for emulation in Python. Although many different implementations exist, this particular one deals with fast GP predictions for large number of input vectors, where the training data sets are typically modest (e.g. less than 300 samples). Access to the emulation's partial derivatives and Hessian matrix is calculated, and training is also taken care of.
 
-You can install with 
+Requirements:
+---------
+* python ( 2.7 or later )
+* numpy
+* scipy
+* GPU predict module:
+    * cmake 
+    * CUDA 5.0 or later
+    * CUnit
 
-        python setup.py install
+Install without GPU:
+---------
+"""bash
+cd gp_emulator
+python setup.py install
+"""
 
-The only requirements are (if memory serves) numpy and scipy.
+Install with GPU predict:
+---------
+1. install python modules
+"""bash
+python setup.py install
+"""
+2. compile CUDA modules
+"""bash
+cd build
+cmake ..
+make 
+"""
+3. export the python path
+
+Tests (only with predict):
+----------
+1. Unit testing (tests/unit_tests.py):
+   * random inputs generated by python pre
+   * operate unit testings of GPU functions. 
+   * compare GPU and python outputs
+2. benchmark (tests/benchmark.py)
+   * obtain speedup of GPU predict  
+   * random inputs
+3. testing emulator (tests/testing_emulator)
+   * run emulator with and without GPU
+   
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index e1c998f..14500fc 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,7 +11,7 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
-//#define DOUBLE__PRECISION
+#define DOUBLE__PRECISION
 #define CUDA_BLOCK 2
  
 #ifdef DOUBLE__PRECISION
diff --git a/tests/benchmark.py b/tests/benchmark.py
old mode 100755
new mode 100644
index b66c44d..63d91ef
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -1,4 +1,5 @@
 #!/usr/local/bin/python
+
 import numpy as np
 import scipy.spatial.distance as dist
 import _gpu_predict
@@ -19,10 +20,10 @@ def set_testing_val (self, P, N, M):
     print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
     print '-----------------------------'
 
-    for i in xrange(np.int(1e4), np.int(1e7), np.int(1e4)):
-        N=i
-        M=250
-        P=10
+    for size in xrange(np.int(1e4), np.int(1e7), np.int(1e4)):
+        N = size
+        M = 250
+        P = 10
 
         inputs = np.random.random((M,P))
         testing=np.random.random((N,P))
@@ -38,7 +39,7 @@ def set_testing_val (self, P, N, M):
     	
         #GPU predict
         start = time.time()
-    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float32', threashold = 2e5)
+    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float64', threashold = 2e5)
     	end =time.time()
     	gputime = end - start
         print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (N, cputime, gputime, cputime/gputime),

From 03117f12f169d1419085a8723cd277bd9d3114f2 Mon Sep 17 00:00:00 2001
From: Sinan Shi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 01:19:09 +0100
Subject: [PATCH 076/117] Update README.rst

---
 README.rst | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/README.rst b/README.rst
index 948be8a..ac617f4 100644
--- a/README.rst
+++ b/README.rst
@@ -20,34 +20,34 @@ Requirements:
 
 Install without GPU:
 ---------
-"""bash
+```bash
 cd gp_emulator
 python setup.py install
-"""
+```
 
 Install with GPU predict:
 ---------
 1. install python modules
-"""bash
+```bash
 python setup.py install
-"""
+```
 2. compile CUDA modules
-"""bash
+```bash
 cd build
 cmake ..
 make 
-"""
+```
 3. export the python path
 
 Tests (only with predict):
 ----------
-1. Unit testing (tests/unit_tests.py):
-   * random inputs generated by python pre
+1. Unit testing ([unit_tests.py](https://github.com/UCL/gp_emulator/blob/master/tests/unit_tests.py)):
+   * random inputs generated by python predict
    * operate unit testings of GPU functions. 
    * compare GPU and python outputs
-2. benchmark (tests/benchmark.py)
+2. benchmark ([benchmark.py](https://github.com/UCL/gp_emulator/blob/master/tests/benchmark.py))
    * obtain speedup of GPU predict  
    * random inputs
-3. testing emulator (tests/testing_emulator)
+3. testing emulator ([testing_emulator](https://github.com/UCL/gp_emulator/blob/master/tests/test_perband_emulator.py))
    * run emulator with and without GPU
    

From c478507c70954c840bc0a33138f3077fa5b21a1c Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 01:21:14 +0100
Subject: [PATCH 077/117] readme

---
 README.rst => README.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename README.rst => README.md (100%)

diff --git a/README.rst b/README.md
similarity index 100%
rename from README.rst
rename to README.md

From 91504d77fac43d270bf0115b76e6832d09dab9a0 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 01:26:32 +0100
Subject: [PATCH 078/117] format readme

---
 README.md | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index ac617f4..a9a99ff 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ Requirements:
     * CUDA 5.0 or later
     * CUnit
 
-Install without GPU:
+Install without GPU predict:
 ---------
 ```bash
 cd gp_emulator
@@ -28,17 +28,19 @@ python setup.py install
 Install with GPU predict:
 ---------
 1. install python modules
-```bash
+    ```bash
 python setup.py install
 ```
 2. compile CUDA modules
-```bash
+    ```bash
 cd build
 cmake ..
 make 
 ```
-3. export the python path
-
+3. export the python path following the cmake instruction, e.g.
+    ```bash
+    export PYTHONPATH=$PYTHONPATH:/home/sinan/gp-emulator/emulator/build/lib```
+    
 Tests (only with predict):
 ----------
 1. Unit testing ([unit_tests.py](https://github.com/UCL/gp_emulator/blob/master/tests/unit_tests.py)):

From 5f33dce45b46641544c88c1d3819e66a947a6f43 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 11:44:08 +0100
Subject: [PATCH 079/117] remove comments

---
 tests/unit_tests.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index 7818f1b..5a8c9ea 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -25,7 +25,7 @@ def get_testing_val (self, testing, do_unc = True):
         t_testing = testing
         t_invQ = self.invQ
 
-        cdist_test_var_1= t_expXsqrt * t_inputs #np.sqrt(expX[:(self.D)])*self.inputs
+        cdist_test_var_1= t_expXsqrt * t_inputs 
         cdist_test_var_2 = np.sqrt(expX[:(self.D)])*testing
         cdist_test_var_3 = dist.cdist ( np.sqrt(expX[:(self.D)])*self.inputs, np.sqrt(expX[:(self.D)])*testing, 'sqeuclidean')
         cdist_test_var_4 = expX[self.D]*np.exp(-0.5*cdist_test_var_3)
@@ -67,11 +67,8 @@ def get_testing_val (self, testing, do_unc = True):
 
         for d in xrange ( self.D ):
             aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
-            
             c = a*aa.T
             deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
-#            print deriv[0:10,d]
-
         var.tofile("./data/var.bin")
         deriv.tofile("./data/deriv.bin")
 
@@ -82,8 +79,8 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    for i in xrange(10000, 1000000, 10000):
-        N=i
+    for size in xrange(10000, 1000000, 10000):
+        N=size
         M=250
         P=10
         

From ff313dc1034738be2f6cf2e8d0a492caad414789 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 17 Jun 2015 17:39:31 +0100
Subject: [PATCH 080/117] calling cmake via setup.py

---
 README.md => README.rst |  0
 setup.py                | 51 ++++++++++++++++++++++++++++++++++++++++-
 2 files changed, 50 insertions(+), 1 deletion(-)
 rename README.md => README.rst (100%)

diff --git a/README.md b/README.rst
similarity index 100%
rename from README.md
rename to README.rst
diff --git a/setup.py b/setup.py
index 97d52fb..1845ae3 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,55 @@
 #!/usr/bin/env python
-
 from distutils.core import setup
 
+from setuptools import setup
+import distutils.command.build as _build
+import setuptools.command.install as _install
+
+import sys
+import os
+import os.path as op
+import distutils.spawn as ds
+import distutils.dir_util as dd
+
+
+
+def run_cmake():
+    if ds.find_executable('cmake') is None:
+        print "CMake  is required"
+        print "Please install cmake version >= 2.6 and re-run setup"
+        sys.exit(-1)
+
+    build_dir = op.join(op.split(__file__)[0], 'build')
+    dd.mkpath(build_dir)
+    os.chdir(build_dir)
+
+    try:
+        ds.spawn(['cmake','../'])
+    except ds.DistutilsExecError:
+        print "Error while running cmake"
+        sys.exit(-1)
+    try:
+        ds.spawn(['make','-j'])
+    except ds.DistutilsExecError:
+        print "Error while compiling"
+        sys.exit(-1)
+ 
+
+class install(_install.install):
+    def run(self):
+        cwd = os.getcwd()
+        run_cmake()
+        os.chdir(cwd)
+        _install.install.run(self)
+
+class build(_build.build):
+    def run(self):
+        cwd = os.getcwd()
+        run_cmake()
+        os.chdir(cwd)
+        _build.build.run(self)
+
+
 setup(name='gp_emulator',
       version='1.4.3',
       description='A Python GaussianProcess emulator software package',
@@ -9,4 +57,5 @@
       author_email='j.gomez-dans@ucl.ac.uk',
       url='http://bitbucket.org/gomezdansj/gp_emulator',
       packages=['gp_emulator'],
+      cmdclass={'build':build, 'install':install},
      )

From 0f2aa7eda8a74bf5b3c95d84afd11cf0c27d799d Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sat, 20 Jun 2015 14:11:11 +0100
Subject: [PATCH 081/117] setup include .so file

---
 MANIFEST.in | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/MANIFEST.in b/MANIFEST.in
index 4cad124..61f6ad6 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,2 +1,3 @@
 include setup.py README.rst
-recursive-include gp_emulator/GaussianProcess.py __init__.py lhd.py multivariate_gp.py save_emulators.py
+#recursive-include gp_emulator/ GaussianProcess.py __init__.py lhd.py multivariate_gp.py save_emulators.py
+#include build/lib/_gpu_predict.so

From 149181261a854859c93ffd023054fa4635b8e482 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sat, 20 Jun 2015 14:11:32 +0100
Subject: [PATCH 082/117] change prec to precision

---
 gp_emulator/GaussianProcess.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index 473abff..a9a4172 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -4,7 +4,7 @@
 import scipy.spatial.distance as dist
 import random
 import sys
-#import _gpu_predict
+import _gpu_predict
 
 def k_fold_cross_validation(X, K, randomise = False):
     """
@@ -237,7 +237,7 @@ def cpu_predict ( self, testing, do_unc=True ):
         mu = np.dot( a.T, self.invQt)
 
         if do_unc:
-	       var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
+            var = b - np.sum (  a * np.dot(self.invQ,a), axis=0)
         # Derivative and partial derivatives of the function
         deriv = np.zeros ( ( nn, self.D ) )
 
@@ -251,9 +251,9 @@ def cpu_predict ( self, testing, do_unc=True ):
 	    return mu, deriv
         
         
-    def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True):
+    def gpu_predict ( self, testing, precision, threshold):
         import _gpu_predict
-        ( nn, D ) = testing.shape
+        nn, D  = testing.shape
         assert D == self.D
         expX = np.exp ( self.theta )
         
@@ -261,7 +261,7 @@ def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True)
         M=self.inputs.shape[0]
         theta_size=self.theta.size
 
-        nblocks = np.int(np.ceil( np.float32( nn ) / threashold ))
+        nblocks = np.int(np.ceil( np.float32( nn ) / threshold ))
         block_size = np.int(np.ceil( nn / nblocks ))
         ind_start = range( np.int(0), np.int(nn), np.int(block_size) )
         ind_end = np.append( ind_start[1:], nn )
@@ -274,7 +274,7 @@ def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True)
             ind_start[ nblocks - 1 ] = ind_end[ nblocks - 2 ]
         
         assert np.all(ind_end - ind_start > 500 )
-        assert np.all(ind_end - ind_start < threashold * 1.5 )
+        assert np.all(ind_end - ind_start < threshold * 1.5 )
             
 
 
@@ -283,7 +283,7 @@ def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True)
         invQt = self.invQt
         invQ =  self.invQ.reshape(self.invQ.shape[0] * self.invQ.shape[1])
 
-        if prec == "float32":
+        if precision == "float32":
             inputs = np.float32( inputs )
             invQt = np.float32( invQt )
             invQ = np.float32( invQ )
@@ -302,7 +302,7 @@ def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True)
            var_block = np.zeros( N_this_block )
            deriv_block = np.zeros( N_this_block * D )
 
-           if prec == "float32":
+           if precision == "float32":
                testing_block = np.float32(testing_block)
                mu_block = np.float32(mu_block)
                var_block = np.float32(var_block)
@@ -321,10 +321,10 @@ def gpu_predict ( self, testing, prec, threashold):# self, testing, do_unc=True)
 
 
 
-    def predict(self, testing, do_unc = True, is_gpu = False, prec = 'double', threashold = 2e5):
+    def predict(self, testing, do_unc = True, is_gpu = False, precison = 'double', threshold = 2e5):
         ( nn, D ) = testing.shape
         if is_gpu == True:
-            return self.gpu_predict(testing, prec, threashold = threashold)
+            return self.gpu_predict(testing, precison, threshold = threshold)
         else:
             return self.cpu_predict(testing, do_unc)
 

From 8ebc3d65a9004bf1dac35b11dd10b8487167b063 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sun, 21 Jun 2015 08:39:28 +0100
Subject: [PATCH 083/117] change variable name

---
 gp_emulator/GaussianProcess.py | 105 +++++++++++++++++----------------
 1 file changed, 53 insertions(+), 52 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index a9a4172..d81f691 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -249,22 +249,12 @@ def cpu_predict ( self, testing, do_unc=True ):
             return mu, var, deriv
         else:
 	    return mu, deriv
-        
-        
-    def gpu_predict ( self, testing, precision, threshold):
-        import _gpu_predict
-        nn, D  = testing.shape
-        assert D == self.D
-        expX = np.exp ( self.theta )
-        
-        N=testing.shape[0]
-        M=self.inputs.shape[0]
-        theta_size=self.theta.size
 
-        nblocks = np.int(np.ceil( np.float32( nn ) / threshold ))
-        block_size = np.int(np.ceil( nn / nblocks ))
-        ind_start = range( np.int(0), np.int(nn), np.int(block_size) )
-        ind_end = np.append( ind_start[1:], nn )
+    def get_gpu_block( self, size, threshold ):
+        nblocks = np.int(np.ceil( np.float64( size ) / threshold ))
+        block_size = np.int(np.ceil( size / nblocks ))
+        ind_start = range( np.int(0), np.int(size), np.int(block_size) )
+        ind_end = np.append( ind_start[1:], size )
         nblocks = len(ind_start)
         
         # the last two block should have equal size
@@ -275,56 +265,67 @@ def gpu_predict ( self, testing, precision, threshold):
         
         assert np.all(ind_end - ind_start > 500 )
         assert np.all(ind_end - ind_start < threshold * 1.5 )
-            
-
-
-        #stretch the matrices to vector to feed _predict_wrap()
-        inputs = self.inputs.reshape(self.inputs.shape[0] * self.inputs.shape[1])
-        invQt = self.invQt
-        invQ =  self.invQ.reshape(self.invQ.shape[0] * self.invQ.shape[1])
+        return ind_start, ind_end
 
-        if precision == "float32":
-            inputs = np.float32( inputs )
-            invQt = np.float32( invQt )
-            invQ = np.float32( invQ )
-            expX = np.float32( expX )
 
-        mu = []
-        var = []
-        deriv = np.array([]).reshape( ( 0, D) )
-
-        for block_id in xrange(len(ind_start)):
-           testing_block = testing[ind_start[block_id]:ind_end[block_id],:]   
+        
+        
+    def gpu_predict ( self, testing, precision, threshold):
+        '''
+        Parameters:
+        --------------
+        testing: 
+        precision: can be np.float32 or np.float64
+        threshold: 
+        '''
+        import _gpu_predict
+        n_predict, n_inputs = testing.shape
+        n_train = self.inputs.shape[0]
+        theta_size=self.theta.size
+        
+        assert n_inputs == self.D
+           
+        #_predict_wrap() has to be fed by one dimentional array
+        inputs = precision(self.inputs.reshape(self.inputs.shape[0] * self.inputs.shape[1]))
+        invQt = precision(self.invQt)
+        invQ =  precision(self.invQ.reshape(self.invQ.shape[0] * self.invQ.shape[1]))
+        expX = precision(np.exp(self.theta))
+
+        result = []
+        error = []
+        deriv = np.array([]).reshape((0, n_inputs))
+        ind_start, ind_end = self.get_gpu_block( n_predict, threshold )
+
+        for block_start, block_end in zip(ind_start, ind_end):
+           testing_block = testing[block_start:block_end,:]   
            testing_block = testing_block.reshape( testing_block.shape[0] * testing_block.shape[1] )
 
-           N_this_block = np.int(ind_end[block_id] - ind_start[block_id])
-           mu_block = np.zeros( N_this_block )
-           var_block = np.zeros( N_this_block )
-           deriv_block = np.zeros( N_this_block * D )
-
-           if precision == "float32":
-               testing_block = np.float32(testing_block)
-               mu_block = np.float32(mu_block)
-               var_block = np.float32(var_block)
-               deriv_block = np.float32(deriv_block)
+           n_predict_block = np.int(block_end - block_start)
+           result_block = np.zeros( n_predict_block )
+           error_block = np.zeros( n_predict_block )
+           deriv_block = np.zeros( n_predict_block * n_inputs )
+           
+           testing_block = precision(testing_block)
+           result_block = precision(result_block)
+           error_block = precision(error_block)
+           deriv_block = precision(deriv_block)
  
            _gpu_predict.predict_wrap(
                    expX, inputs, invQt, invQ, testing_block,
-                   mu_block, var_block, deriv_block,
-                   N_this_block, M, D, theta_size)
+                   result_block, error_block, deriv_block,
+                   n_predict_block, n_train, n_inputs, theta_size)
 
-           mu = np.append(mu, mu_block)
-           var = np.append(var, var_block)
-           deriv = np.append( deriv, deriv_block.reshape( D, N_this_block ).T, axis = 0)
+           result = np.append(result, result_block)
+           error = np.append(error, error_block)
+           deriv = np.append(deriv, deriv_block.reshape( n_inputs, n_predict_block ).T, axis = 0)
 
-        return mu, var, deriv
+        return result, error, deriv
 
 
 
-    def predict(self, testing, do_unc = True, is_gpu = False, precison = 'double', threshold = 2e5):
-        ( nn, D ) = testing.shape
+    def predict(self, testing, do_unc = True, is_gpu = False, precision = np.float64, threshold = 2e5):
         if is_gpu == True:
-            return self.gpu_predict(testing, precison, threshold = threshold)
+            return self.gpu_predict(testing, precision, threshold = threshold)
         else:
             return self.cpu_predict(testing, do_unc)
 

From f5586dfc1f8e57ffbec8cee62584ffa8627f1d77 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sun, 21 Jun 2015 08:40:55 +0100
Subject: [PATCH 084/117] remove __main__

---
 gp_emulator/GaussianProcess.py | 60 ----------------------------------
 1 file changed, 60 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index d81f691..f0433b5 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -353,63 +353,3 @@ def hessian ( self, testing ):
                 cc = a*(aa.T)
                 hess[:, d,d2] = np.dot(cc.T, self.invQt)
         return hess
-
-
-
-        
-
-
-if __name__ == "__main__":
-    import matplotlib.pyplot as plt
-    np.set_printoptions(precision=2, suppress=True)
-    #input_obs = np.array ( [[-4, -3, -1, 0, 2]]).T
-    #target1 = np.array ([-2, 0, 1., 2., -1])
-    #data = np.loadtxt ("mvreg.dat", delimiter="," )
-    #target1 = data[ :,0]
-    #target2 = data[ :,1]
-    #target3 = data[ :,2]
-    #input_obs = data[ :, 3: ]
-    wheat = np.loadtxt("../data/argentine_wheat.dat")[:, 1:]
-    yields = wheat[:,0]
-    mu = yields.mean()
-    sigma = yields.std()
-    yields = (yields - mu ) / sigma
-    wheat [:, 0] = yields
-    rmse = []
-    print wheat.shape
-    for ( train,validate) in k_fold_cross_validation ( wheat, 5, randomise=True):
-        train = np.array ( train )
-        validate = np.array ( validate )
-        yields_t = train [ :, 0]
-        inputs_t = train [ :, 1:]
-        yields_v = validate [ :,  0]
-        inputs_v = validate [ :, 1:]
-        #print inputs_t.shape, yields_t.shape, inputs_v.shape
-        gp = GaussianProcess ( inputs_t, yields_t )
-        theta_min= gp.learn_hyperparameters (n_tries=2)
-        pred_mu, pred_var, par_dev = gp.predict ( inputs_v )
-        #gp.gpu_predict ( inputs_v )
-        #print "TEST"
-        #print inputs_v
-        #print pred_mu, pred_var, par_dev
-        #r = ( yields_v - pred_mu )**2#/pred_var
-        #rmse.append ( [ np.sqrt(r.mean()), theta_min[1] ])
-        
-        
-    #gp.theta[2] = 0.
-    ######theta = gp.theta
-    ######print theta
-    ######gp.predict ( input_obs )
-    
-    ######x = np.linspace(-5,5,100)
-    
-    ######(mu, var ) = gp.predict ( x[:, np.newaxis])
-    ######plt.plot ( x, mu, '-r' )
-    ######plt.fill_between ( x, mu+np.sqrt(var), mu-np.sqrt(var), color='0.8')
-    #######plt.errorbar ( x, mu, yerr=np.sqrt(var)*0.5 )
-    ######plt.plot ( input_obs, target1, 'gs' )
-    ######plt.title("theta: %s" % theta )
-    ######plt.show()
-
-
-

From 7f394690c4811e2568e68ff08e192392b07cf6ff Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sun, 21 Jun 2015 09:23:35 +0100
Subject: [PATCH 085/117] modify get_gpu_block, add comments

---
 gp_emulator/GaussianProcess.py | 35 ++++++++++++++++++++++------------
 1 file changed, 23 insertions(+), 12 deletions(-)

diff --git a/gp_emulator/GaussianProcess.py b/gp_emulator/GaussianProcess.py
index f0433b5..47129a2 100644
--- a/gp_emulator/GaussianProcess.py
+++ b/gp_emulator/GaussianProcess.py
@@ -250,33 +250,33 @@ def cpu_predict ( self, testing, do_unc=True ):
         else:
 	    return mu, deriv
 
-    def get_gpu_block( self, size, threshold ):
-        nblocks = np.int(np.ceil( np.float64( size ) / threshold ))
-        block_size = np.int(np.ceil( size / nblocks ))
+    def get_gpu_block( self, size, block_size ):
+        '''
+        Distribute a size long vector block_size, and return the start index
+        and end index of each block. To ensure size of the last block 
+        is not too small, the last two blocks will have a equal size.  
+        '''
         ind_start = range( np.int(0), np.int(size), np.int(block_size) )
         ind_end = np.append( ind_start[1:], size )
         nblocks = len(ind_start)
         
-        # the last two block should have equal size
+        # compute the size of last two blocks.
         if nblocks > 1:
             last_two_block_size = ( ind_end[ nblocks - 1 ] - ind_start[ nblocks - 2 ] ) / 2
             ind_end[ nblocks - 2 ] = ind_start[ nblocks - 2 ] + last_two_block_size
             ind_start[ nblocks - 1 ] = ind_end[ nblocks - 2 ]
-        
-        assert np.all(ind_end - ind_start > 500 )
-        assert np.all(ind_end - ind_start < threshold * 1.5 )
+       
+        assert np.all(ind_end - ind_start <= block_size )
         return ind_start, ind_end
 
-
-        
         
     def gpu_predict ( self, testing, precision, threshold):
         '''
         Parameters:
         --------------
-        testing: 
-        precision: can be np.float32 or np.float64
-        threshold: 
+        testing: 2D array n_predict * n_inputs
+        precision: np.float32 / np.float64
+        threshold: see predict() threshold.
         '''
         import _gpu_predict
         n_predict, n_inputs = testing.shape
@@ -317,6 +317,7 @@ def gpu_predict ( self, testing, precision, threshold):
 
            result = np.append(result, result_block)
            error = np.append(error, error_block)
+           #deriv produced by gpu is transposed, so here we need transpose them back.
            deriv = np.append(deriv, deriv_block.reshape( n_inputs, n_predict_block ).T, axis = 0)
 
         return result, error, deriv
@@ -324,6 +325,16 @@ def gpu_predict ( self, testing, precision, threshold):
 
 
     def predict(self, testing, do_unc = True, is_gpu = False, precision = np.float64, threshold = 2e5):
+        '''
+        Parameters:
+        --------------
+        testing: 2D array n_predict * n_inputs
+        precision: np.float32 / np.float64
+        do_unc: tag to switch on or off calculation of the uncertainty. It can only affect cpu_predict()
+        threshold: is the maximum number of n_predict that gpu_predict() can deal with.
+                If the n_predict is larger than the threshold, data will be truncated,
+                and gpu_predict will be excuted for multiple time.
+        '''
         if is_gpu == True:
             return self.gpu_predict(testing, precision, threshold = threshold)
         else:

From 7b5baca6c645347a5db6c5d84435a0f6112fe1cf Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sun, 21 Jun 2015 22:36:26 +0100
Subject: [PATCH 086/117] change variable name

---
 gp_emulator/gpu/_gpu_predict.c | 89 ++++++++++++++++++----------------
 1 file changed, 47 insertions(+), 42 deletions(-)

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
index 94c914d..7ecd0fd 100644
--- a/gp_emulator/gpu/_gpu_predict.c
+++ b/gp_emulator/gpu/_gpu_predict.c
@@ -1,7 +1,15 @@
+/**********************************//**
+ * source code of share library _gpu_predict.so
+ * importing inputs from python
+ * calling GPU predict function by predict_wrap()
+ * python utility functions are based on the following template:
+ *  http://wiki.scipy.org/Cookbook/C_Extensions/NumPy_arrays
+ **********************************/
+
 #include "gpu_predict.h"
 #include<errno.h>
 
-/*********************************
+/*********************************//**
  * Set up the methods table
  *********************************/
 static PyMethodDef gpuMethods[] = 
@@ -10,7 +18,7 @@ static PyMethodDef gpuMethods[] =
     {NULL, NULL}     /* Sentinel - marks the end of this structure */
 };
 
-/**********************************
+/**********************************//**
  * Initialize the C_test functions 
  * Module name must be _C_arraytest in compile and linked 
  **********************************/
@@ -21,7 +29,7 @@ void init_gpu_predict()
 }
 
 
-/**********************************
+/**********************************//**
  * Check the dimension and data type 
  * of input python arrays.
  **********************************/
@@ -48,7 +56,7 @@ void checkRealVector(PyArrayObject *vec)
 
 
 
-/**********************************
+/**********************************//**
  * Convert data (vector) from PyArrayObject to real
  **********************************/
 real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  
@@ -59,43 +67,43 @@ real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)
 
 
 
-/**********************************
+/**********************************//**
  * getPredictDataFromPython
- * Assign multiple input python array to C array. 
- * Both of them are in 1D. 
+ * Assign multiple input python arrays, which are all in 1D
+ * to C array.
  **********************************/
 void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt, real **c_invQ, 
-                              real **c_testing, real **c_inputs,
-                              real **c_mu, real **c_var, real **c_deriv,
-                              int *N, int *M, int *D, int *theta_size)
+                              real **c_predict, real **c_train,
+                              real **c_result, real **c_error, real **c_deriv,
+                              int *Npredict, int *Ntrain, int *Ninputs, int *theta_size)
 {
-    PyArrayObject *py_theta_exp, *py_inputs, *py_invQt, *py_invQ, *py_testing;
-    PyArrayObject *py_mu, *py_var, *py_deriv;
+    PyArrayObject *py_theta_exp, *py_train, *py_invQt, *py_invQ, *py_predict;
+    PyArrayObject *py_result, *py_error, *py_deriv;
 
     PyArg_ParseTuple ( args, "O!O!O!O!O!O!O!O!iiii",
         &PyArray_Type, &py_theta_exp,
-        &PyArray_Type, &py_inputs,
+        &PyArray_Type, &py_train,
         &PyArray_Type, &py_invQt,
         &PyArray_Type, &py_invQ,
-        &PyArray_Type, &py_testing,
-        &PyArray_Type, &py_mu,
-        &PyArray_Type, &py_var,
+        &PyArray_Type, &py_predict,
+        &PyArray_Type, &py_result,
+        &PyArray_Type, &py_error,
         &PyArray_Type, &py_deriv,
-        N, M, D, theta_size);
+        Npredict, Ntrain, Ninputs, theta_size);
 
     *c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
-    *c_inputs = pyvector_to_Carrayptrs( py_inputs );
+    *c_train = pyvector_to_Carrayptrs( py_train );
     *c_invQt = pyvector_to_Carrayptrs( py_invQt );
     *c_invQ = pyvector_to_Carrayptrs( py_invQ );
-    *c_testing = pyvector_to_Carrayptrs( py_testing );
-    *c_mu = pyvector_to_Carrayptrs( py_mu );
-    *c_var = pyvector_to_Carrayptrs( py_var );
+    *c_predict = pyvector_to_Carrayptrs( py_predict );
+    *c_result = pyvector_to_Carrayptrs( py_result );
+    *c_error = pyvector_to_Carrayptrs( py_error );
     *c_deriv = pyvector_to_Carrayptrs( py_deriv );
 }
 
 
 
-/**********************************
+/**********************************//**
  * predict_wrap
  * 1) call getPredictDataFromPython;
  * 2) transpose 2D arrays (data arranged in 1D) to column major;
@@ -103,35 +111,32 @@ void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt
  **********************************/
 PyObject *predict_wrap ( PyObject *self, PyObject *args )
 {
-    int N,M,D,theta_size;
+    int Npredict, Ntrain, Ninputs, theta_size;
     real *c_theta_exp;
-    real *c_inputs, *c_invQt, *c_invQ, *c_testing;
-    real *c_mu, *c_var, *c_deriv;
+    real *c_train, *c_invQt, *c_invQ, *c_predict;
+    real *c_result, *c_error, *c_deriv;
    
     getPredictDataFromPython(args, &c_theta_exp, &c_invQt, &c_invQ,
-                             &c_testing, &c_inputs,
-                             &c_mu, &c_var, &c_deriv,
-                             &N, &M, &D, &theta_size);
-
+                             &c_predict, &c_train,
+                             &c_result, &c_error, &c_deriv,
+                             &Npredict, &Ntrain, &Ninputs,  &theta_size);
 
     //transpose 2D array to column major to cope with cublas
-    real *c_invQ_T, *c_inputs_T, *c_testing_T;
-    c_invQ_T = computeTranspose( c_invQ, M, M );
-    c_inputs_T = computeTranspose( c_inputs, D, M );
-    c_testing_T = computeTranspose( c_testing, D, N);
-
-    predict( c_theta_exp, c_inputs_T, c_invQt, c_invQ_T, c_testing_T, 
-            c_mu, c_var, c_deriv,
-            N, M, D, theta_size );
+    real *c_invQ_T, *c_train_T, *c_predict_T;
+    c_invQ_T = computeTranspose( c_invQ, Ntrain, Ntrain );
+    c_train_T = computeTranspose( c_train, Ninputs, Ntrain );
+    c_predict_T = computeTranspose( c_predict, Ninputs, Npredict);
 
+    predict( c_theta_exp, c_train_T, c_invQt, c_invQ_T, c_predict_T, 
+            c_result, c_error, c_deriv,
+            Npredict, Ntrain, Ninputs, theta_size );
 
     free(c_invQ_T);
-    free(c_inputs_T);
-    free(c_testing_T);
+    free(c_train_T);
+    free(c_predict_T);
    
-    return Py_BuildValue("");
-
-    
+    Py_INCREF(Py_None);
+    return Py_None;
 }
 
 

From 1a3367961a0c4c991e55b08ee225a954dd3e7a95 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sun, 21 Jun 2015 22:57:58 +0100
Subject: [PATCH 087/117] better variable names predict.cu

---
 gp_emulator/gpu/predict.cu | 106 ++++++++++++++++++-------------------
 1 file changed, 53 insertions(+), 53 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 9ddecd5..2a1f3af 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -2,7 +2,7 @@
  * CUDA implementation of derived from GaussianProcess
  * in python code. 
  * 
- * Sinan Shi (UCL) 
+ * Sinan Shi 
  *********************************************/
 #include "gpu_predict.h"
 #include <stdlib.h>
@@ -13,13 +13,13 @@
  * GaussianPredict::predict
  *********************************************/
 extern "C"{
-void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,const real *c_invQ, const real *c_testing,  
-        real *c_mu, real *c_var, real *c_deriv,const int N,const int M, const int  D, const int theta_size)
+void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
+        const real *c_invQ, const real *c_predict,  
+        real *c_result, real *c_error, real *c_deriv,
+        const int Npredict,const int Ntrain, const int  Ninputs, const int theta_size)
 {
     int i;
-//    cublasStatus_t stat;
     cublasHandle_t handle;
-    
     cublasCreate(&handle);
 
     real *c_theta_exp_sqrt;
@@ -31,41 +31,41 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
 
 
     //define device vector and matrices
-    real *d_inputs, *d_theta_exp, *d_theta_exp_sqrt, *d_invQt, *d_invQ, *d_testing;
+    real *d_train, *d_theta_exp, *d_theta_exp_sqrt, *d_invQt, *d_invQ, *d_predict;
 
     //allocate and copy vector on device 
     cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
     cudaMalloc( (void **)&d_theta_exp_sqrt, sizeof(real) * theta_size );
-    cudaMalloc( (void **)&d_invQt, sizeof(real) * M);
+    cudaMalloc( (void **)&d_invQt, sizeof(real) * Ntrain);
     cublasCheckErrors(cublasSetVector( theta_size, sizeof(real), c_theta_exp_sqrt, 1, d_theta_exp_sqrt, 1 ));
     cublasCheckErrors(cublasSetVector( theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
-    cublasCheckErrors(cublasSetVector( M, sizeof(real), c_invQt, 1, d_invQt, 1));
+    cublasCheckErrors(cublasSetVector( Ntrain, sizeof(real), c_invQt, 1, d_invQt, 1));
 
     //allocate and copy matrix on device
-    cudaMalloc( (void **)&d_inputs, sizeof(real) * M * D );
-    cudaMalloc( (void **)&d_invQ, sizeof(real) * M * M );
-    cudaMalloc( (void **)&d_testing, sizeof(real) * N * D );
-    cublasCheckErrors(cublasSetMatrix( M, D, sizeof(real), c_inputs, M, d_inputs, M ));
-    cublasCheckErrors(cublasSetMatrix( M, M, sizeof(real), c_invQ, M, d_invQ, M ));
-    cublasCheckErrors(cublasSetMatrix( N, D, sizeof(real), c_testing, N, d_testing, N));
+    cudaMalloc( (void **)&d_train, sizeof(real) * Ntrain * Ninputs );
+    cudaMalloc( (void **)&d_invQ, sizeof(real) * Ntrain * Ntrain );
+    cudaMalloc( (void **)&d_predict, sizeof(real) * Npredict * Ninputs );
+    cublasCheckErrors(cublasSetMatrix( Ntrain, Ninputs, sizeof(real), c_train, Ntrain, d_train, Ntrain ));
+    cublasCheckErrors(cublasSetMatrix( Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
+    cublasCheckErrors(cublasSetMatrix( Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
     
 
         
     /*********************************
      * Euclidian distance calculation
-     * a = cidist(sqrt(expX_{,D}) * inputs * sqrt(expX_{,D} * testing)))
-     * Notice: a is equivalent to a^T in python due to column major fashion
+     * a = cidist(sqrt(expX_{,Ninputs}) * inputs * sqrt(expX_{,Ninputs} * testing)))
+     * Npredictotice: a is equivalent to a^T in python due to column major fashion
      ********************************/ 
     real *d_res_temp1, *d_res_temp2, *d_a;
-    cudaMalloc((void **)&d_res_temp1, sizeof(real) * M * D);
-    cudaMalloc((void **)&d_res_temp2, sizeof(real) * N * D);
-    cudaMalloc((void **)&d_a, sizeof(real) * M * N);
+    cudaMalloc((void **)&d_res_temp1, sizeof(real) * Ntrain * Ninputs);
+    cudaMalloc((void **)&d_res_temp2, sizeof(real) * Npredict * Ninputs);
+    cudaMalloc((void **)&d_a, sizeof(real) * Ntrain * Npredict);
 
-    gpu_vectorTimesMatrix(d_inputs, d_theta_exp_sqrt, d_res_temp1, M, D);
-    gpu_vectorTimesMatrix(d_testing, d_theta_exp_sqrt, d_res_temp2, N, D);
-    gpu_init_array( d_a, 0.0, N * M );
-    gpu_cdist(d_res_temp1, d_res_temp2, d_a, M, D, N, D);
-    gpu_matrixExp(d_a, -0.5, c_theta_exp[D], M * N);
+    gpu_vectorTimesMatrix(d_train, d_theta_exp_sqrt, d_res_temp1, Ntrain, Ninputs);
+    gpu_vectorTimesMatrix(d_predict, d_theta_exp_sqrt, d_res_temp2, Npredict, Ninputs);
+    gpu_init_array( d_a, 0.0, Npredict * Ntrain );
+    gpu_cdist(d_res_temp1, d_res_temp2, d_a, Ntrain, Ninputs, Npredict, Ninputs);
+    gpu_matrixExp(d_a, -0.5, c_theta_exp[Ninputs], Ntrain * Npredict);
 
     cudaFree(d_res_temp1);
     cudaFree(d_res_temp2);
@@ -74,12 +74,12 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
      * compute mu:
      * mu = a * invQt (dot product)
      ********************************/
-    real *d_mu;
-    cudaMalloc((void **)&d_mu, sizeof(real) * N);
+    real *d_result;
+    cudaMalloc((void **)&d_result, sizeof(real) * Npredict);
     real alpha = 1.f;
     real beta = 0.f;
-    /*cublasCheckErrors(*/CUBLAS_GEMV(handle, CUBLAS_OP_N, N, M, &alpha, d_a, N, d_invQt, 1, &beta, d_mu, 1);//);
-    cudaMemcpy(c_mu, d_mu, sizeof(real) * N, cudaMemcpyDeviceToHost);
+    /*cublasCheckErrors(*/CUBLAS_GEMV(handle, CUBLAS_OP_N, Npredict, Ntrain, &alpha, d_a, Npredict, d_invQt, 1, &beta, d_result, 1);//);
+    cudaMemcpy(c_result, d_result, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
        
     
    /*********************************
@@ -87,23 +87,23 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     * var = b - rowsum(a * dot(invQ, a_T))
     ********************************/
     real *d_temp_dot, *d_a_T;
-    real *d_var;
+    real *d_error;
 
-    cudaMalloc((void **)&d_temp_dot, sizeof(real) * M * N);
-    cudaMalloc((void **)&d_a_T, sizeof(real) * M * N);
+    cudaMalloc((void **)&d_temp_dot, sizeof(real) * Ntrain * Npredict);
+    cudaMalloc((void **)&d_a_T, sizeof(real) * Ntrain * Npredict);
 
-    cublasCheckErrors(CUBLAS_GEMM(handle, CUBLAS_OP_N, CUBLAS_OP_T, M, N, M, &alpha, d_invQ, M, d_a, N, &beta, d_temp_dot, M));
-    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, M, N, &alpha, d_a, N, &beta, d_a, M, d_a_T, M));
+    cublasCheckErrors(CUBLAS_GEMM(handle, CUBLAS_OP_N, CUBLAS_OP_T, Ntrain, Npredict, Ntrain, &alpha, d_invQ, Ntrain, d_a, Npredict, &beta, d_temp_dot, Ntrain));
+    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, Ntrain, Npredict, &alpha, d_a, Npredict, &beta, d_a, Ntrain, d_a_T, Ntrain));
     
     cudaFree(d_a);
     cudaFree(d_invQ);
     
-    gpu_elementwiseMult(d_a_T, d_temp_dot, M * N);
-    d_var = gpu_rowSum(d_temp_dot, M, N);
-    gpu_scalarMinusVec(d_var, c_theta_exp[D], N );
-    cudaMemcpy(c_var, d_var, sizeof(real) * N, cudaMemcpyDeviceToHost);
+    gpu_elementwiseMult(d_a_T, d_temp_dot, Ntrain * Npredict);
+    d_error = gpu_rowSum(d_temp_dot, Ntrain, Npredict);
+    gpu_scalarMinusVec(d_error, c_theta_exp[Ninputs], Npredict );
+    cudaMemcpy(c_error, d_error, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
 
-    cudaFree(d_var);
+    cudaFree(d_error);
     cudaFree(d_temp_dot);
 
 
@@ -112,35 +112,35 @@ void predict(const real *c_theta_exp, const real *c_inputs,const real *c_invQt,c
     * 
     ********************************/
     real *d_deriv;
-    cudaMalloc((void **)&d_deriv, sizeof(real) * N );
+    cudaMalloc((void **)&d_deriv, sizeof(real) * Npredict );
    
     real *d_aa;
-    cudaMalloc((void **)&d_aa, sizeof(real) * M * N);
+    cudaMalloc((void **)&d_aa, sizeof(real) * Ntrain * Npredict);
     real *ptr_inputs, *ptr_testing, *ptr_deriv;
-    ptr_inputs = d_inputs;
-    ptr_testing = d_testing;
+    ptr_inputs = d_train;
+    ptr_testing = d_predict;
     ptr_deriv = c_deriv;
 
-     for( i = 0; i < D; ++i){
-        gpu_crossMinus(ptr_inputs, ptr_testing, d_aa, M, N );
-        ptr_inputs = ptr_inputs + M;
-        ptr_testing = ptr_testing + N;
+     for( i = 0; i < Ninputs; ++i){
+        gpu_crossMinus(ptr_inputs, ptr_testing, d_aa, Ntrain, Npredict );
+        ptr_inputs = ptr_inputs + Ntrain;
+        ptr_testing = ptr_testing + Npredict;
         alpha = c_theta_exp[i];
-        gpu_elementwiseMult(d_a_T, d_aa, M * N);
-        cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, M, N, &alpha, d_aa, M, d_invQt, 1, &beta, d_deriv,1));
+        gpu_elementwiseMult(d_a_T, d_aa, Ntrain * Npredict);
+        cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, Ntrain, Npredict, &alpha, d_aa, Ntrain, d_invQt, 1, &beta, d_deriv,1));
 
-        cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * N, cudaMemcpyDeviceToHost);
-        ptr_deriv = ptr_deriv + N;
+        cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
+        ptr_deriv = ptr_deriv + Npredict;
      }
 
-     cudaFree(d_mu);
+     cudaFree(d_result);
      cudaFree(d_invQt);
      cudaFree(d_a_T);
      cudaFree(d_aa);
-     cudaFree(d_inputs);
+     cudaFree(d_train);
      cudaFree(d_deriv);
      cudaFree(d_theta_exp);
-     cudaFree(d_testing);
+     cudaFree(d_predict);
    
 }
 }

From 248af9760d55745a17f9a77aa1e6899eafc7d526 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Sun, 21 Jun 2015 23:20:40 +0100
Subject: [PATCH 088/117] breakdown distance calculation

---
 gp_emulator/gpu/predict.cu | 76 +++++++++++++++++++++++---------------
 1 file changed, 47 insertions(+), 29 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 2a1f3af..e55d7bf 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -7,6 +7,37 @@
 #include "gpu_predict.h"
 #include <stdlib.h>
 #define debug
+
+/*********************************
+ * Euclidian distance calculation
+ * 1) dist_matrix = cidist(sqrt(expX_{,Ninputs}) * train * sqrt(expX_{,Ninputs} * predict)))
+ * 2) dist_matrix = -0.5 * exp(expX_{Ninputs})
+ * Notice: distance is equivalent to distance^T
+ ********************************/ 
+real *compute_distance(const real *d_predict, const real *d_train, 
+                      const real *c_theta_exp,const real *d_theta_exp, 
+                      const real *d_theta_exp_sqrt,
+                      const int Npredict, const int Ntrain, const int Ninputs)
+{
+    real *d_res_temp1, *d_res_temp2, *d_dist_matrix;
+    cudaMalloc((void **)&d_res_temp1, sizeof(real) * Ntrain * Ninputs);
+    cudaMalloc((void **)&d_res_temp2, sizeof(real) * Npredict * Ninputs);
+    cudaMalloc((void **)&d_dist_matrix, sizeof(real) * Ntrain * Npredict);
+
+    gpu_vectorTimesMatrix(d_train, d_theta_exp_sqrt, d_res_temp1, Ntrain, Ninputs);
+    gpu_vectorTimesMatrix(d_predict, d_theta_exp_sqrt, d_res_temp2, Npredict, Ninputs);
+    gpu_init_array( d_dist_matrix, 0.0, Npredict * Ntrain );
+    gpu_cdist(d_res_temp1, d_res_temp2, d_dist_matrix, Ntrain, Ninputs, Npredict, Ninputs);
+    gpu_matrixExp(d_dist_matrix, -0.5, c_theta_exp[Ninputs], Ntrain * Npredict);
+
+    cudaFree(d_res_temp1);
+    cudaFree(d_res_temp2);
+    return(d_dist_matrix);
+}
+
+
+
+
 /*********************************************//**
  * predict function:
  * CUDA version of the corresponding python code
@@ -48,28 +79,14 @@ void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
     cublasCheckErrors(cublasSetMatrix( Ntrain, Ninputs, sizeof(real), c_train, Ntrain, d_train, Ntrain ));
     cublasCheckErrors(cublasSetMatrix( Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
     cublasCheckErrors(cublasSetMatrix( Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
-    
-
-        
-    /*********************************
-     * Euclidian distance calculation
-     * a = cidist(sqrt(expX_{,Ninputs}) * inputs * sqrt(expX_{,Ninputs} * testing)))
-     * Npredictotice: a is equivalent to a^T in python due to column major fashion
-     ********************************/ 
-    real *d_res_temp1, *d_res_temp2, *d_a;
-    cudaMalloc((void **)&d_res_temp1, sizeof(real) * Ntrain * Ninputs);
-    cudaMalloc((void **)&d_res_temp2, sizeof(real) * Npredict * Ninputs);
-    cudaMalloc((void **)&d_a, sizeof(real) * Ntrain * Npredict);
-
-    gpu_vectorTimesMatrix(d_train, d_theta_exp_sqrt, d_res_temp1, Ntrain, Ninputs);
-    gpu_vectorTimesMatrix(d_predict, d_theta_exp_sqrt, d_res_temp2, Npredict, Ninputs);
-    gpu_init_array( d_a, 0.0, Npredict * Ntrain );
-    gpu_cdist(d_res_temp1, d_res_temp2, d_a, Ntrain, Ninputs, Npredict, Ninputs);
-    gpu_matrixExp(d_a, -0.5, c_theta_exp[Ninputs], Ntrain * Npredict);
 
-    cudaFree(d_res_temp1);
-    cudaFree(d_res_temp2);
+    real * d_dist_matrix;
+    d_dist_matrix = compute_distance(d_predict, d_train, c_theta_exp, d_theta_exp, d_theta_exp_sqrt,
+                                    Npredict, Ntrain, Ninputs);
+   
 
+        
+    
     /*********************************
      * compute mu:
      * mu = a * invQt (dot product)
@@ -78,7 +95,8 @@ void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
     cudaMalloc((void **)&d_result, sizeof(real) * Npredict);
     real alpha = 1.f;
     real beta = 0.f;
-    /*cublasCheckErrors(*/CUBLAS_GEMV(handle, CUBLAS_OP_N, Npredict, Ntrain, &alpha, d_a, Npredict, d_invQt, 1, &beta, d_result, 1);//);
+    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_N, Npredict, Ntrain, &alpha, d_dist_matrix, 
+                Npredict, d_invQt, 1, &beta, d_result, 1));
     cudaMemcpy(c_result, d_result, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
        
     
@@ -86,19 +104,19 @@ void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
     * compute var:
     * var = b - rowsum(a * dot(invQ, a_T))
     ********************************/
-    real *d_temp_dot, *d_a_T;
+    real *d_temp_dot, *d_dist_matrix_T;
     real *d_error;
 
     cudaMalloc((void **)&d_temp_dot, sizeof(real) * Ntrain * Npredict);
-    cudaMalloc((void **)&d_a_T, sizeof(real) * Ntrain * Npredict);
+    cudaMalloc((void **)&d_dist_matrix_T, sizeof(real) * Ntrain * Npredict);
 
-    cublasCheckErrors(CUBLAS_GEMM(handle, CUBLAS_OP_N, CUBLAS_OP_T, Ntrain, Npredict, Ntrain, &alpha, d_invQ, Ntrain, d_a, Npredict, &beta, d_temp_dot, Ntrain));
-    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, Ntrain, Npredict, &alpha, d_a, Npredict, &beta, d_a, Ntrain, d_a_T, Ntrain));
+    cublasCheckErrors(CUBLAS_GEMM(handle, CUBLAS_OP_N, CUBLAS_OP_T, Ntrain, Npredict, Ntrain, &alpha, d_invQ, Ntrain, d_dist_matrix, Npredict, &beta, d_temp_dot, Ntrain));
+    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, Ntrain, Npredict, &alpha, d_dist_matrix, Npredict, &beta, d_dist_matrix, Ntrain, d_dist_matrix_T, Ntrain));
     
-    cudaFree(d_a);
+    cudaFree(d_dist_matrix);
     cudaFree(d_invQ);
     
-    gpu_elementwiseMult(d_a_T, d_temp_dot, Ntrain * Npredict);
+    gpu_elementwiseMult(d_dist_matrix_T, d_temp_dot, Ntrain * Npredict);
     d_error = gpu_rowSum(d_temp_dot, Ntrain, Npredict);
     gpu_scalarMinusVec(d_error, c_theta_exp[Ninputs], Npredict );
     cudaMemcpy(c_error, d_error, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
@@ -126,7 +144,7 @@ void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
         ptr_inputs = ptr_inputs + Ntrain;
         ptr_testing = ptr_testing + Npredict;
         alpha = c_theta_exp[i];
-        gpu_elementwiseMult(d_a_T, d_aa, Ntrain * Npredict);
+        gpu_elementwiseMult(d_dist_matrix_T, d_aa, Ntrain * Npredict);
         cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, Ntrain, Npredict, &alpha, d_aa, Ntrain, d_invQt, 1, &beta, d_deriv,1));
 
         cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
@@ -135,7 +153,7 @@ void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
 
      cudaFree(d_result);
      cudaFree(d_invQt);
-     cudaFree(d_a_T);
+     cudaFree(d_dist_matrix_T);
      cudaFree(d_aa);
      cudaFree(d_train);
      cudaFree(d_deriv);

From 3bf91cd305e66b6511f92c9f27820e5eb573a1fc Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 22 Jun 2015 00:47:57 +0100
Subject: [PATCH 089/117] break down predict.cu blocks into functions

---
 gp_emulator/gpu/predict.cu | 238 ++++++++++++++++++++++---------------
 1 file changed, 143 insertions(+), 95 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index e55d7bf..a10b514 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -8,36 +8,140 @@
 #include <stdlib.h>
 #define debug
 
-/*********************************
+/*********************************//*
  * Euclidian distance calculation
- * 1) dist_matrix = cidist(sqrt(expX_{,Ninputs}) * train * sqrt(expX_{,Ninputs} * predict)))
- * 2) dist_matrix = -0.5 * exp(expX_{Ninputs})
- * Notice: distance is equivalent to distance^T
+ * 1) res_mv1 = theta_exp_sqrt_{,Ninputs} * train
+ * 2) res_mv2 = theta_exp_sqrt_{,Ninputs} * predict
+ * 3) dist_matrix = cidist(res_mv1, res_mv2)
+ * 4) dist_matrix = -0.5 * exp(expX_{Ninputs})
+ * Notice: distance is equivalent to distance^T (a^T) in python
  ********************************/ 
 real *compute_distance(const real *d_predict, const real *d_train, 
                       const real *c_theta_exp,const real *d_theta_exp, 
                       const real *d_theta_exp_sqrt,
                       const int Npredict, const int Ntrain, const int Ninputs)
 {
-    real *d_res_temp1, *d_res_temp2, *d_dist_matrix;
-    cudaMalloc((void **)&d_res_temp1, sizeof(real) * Ntrain * Ninputs);
-    cudaMalloc((void **)&d_res_temp2, sizeof(real) * Npredict * Ninputs);
+    real *d_res_mv1, *d_res_mv2, *d_dist_matrix;
+    cudaMalloc((void **)&d_res_mv1, sizeof(real) * Ntrain * Ninputs);
+    cudaMalloc((void **)&d_res_mv2, sizeof(real) * Npredict * Ninputs);
     cudaMalloc((void **)&d_dist_matrix, sizeof(real) * Ntrain * Npredict);
 
-    gpu_vectorTimesMatrix(d_train, d_theta_exp_sqrt, d_res_temp1, Ntrain, Ninputs);
-    gpu_vectorTimesMatrix(d_predict, d_theta_exp_sqrt, d_res_temp2, Npredict, Ninputs);
+    gpu_vectorTimesMatrix(d_train, d_theta_exp_sqrt, d_res_mv1, Ntrain, Ninputs);
+    gpu_vectorTimesMatrix(d_predict, d_theta_exp_sqrt, d_res_mv2, Npredict, Ninputs);
     gpu_init_array( d_dist_matrix, 0.0, Npredict * Ntrain );
-    gpu_cdist(d_res_temp1, d_res_temp2, d_dist_matrix, Ntrain, Ninputs, Npredict, Ninputs);
+    gpu_cdist(d_res_mv1, d_res_mv2, d_dist_matrix, Ntrain, Ninputs, Npredict, Ninputs);
     gpu_matrixExp(d_dist_matrix, -0.5, c_theta_exp[Ninputs], Ntrain * Npredict);
 
-    cudaFree(d_res_temp1);
-    cudaFree(d_res_temp2);
+    cudaFree(d_res_mv1);
+    cudaFree(d_res_mv2);
     return(d_dist_matrix);
 }
 
 
+/*********************************//*
+ * compute result:
+ * c_result = dist_matrix * invQt (dot product)
+ ********************************/
+void compute_result(cublasHandle_t handle, real *c_result, const real *d_dist_matrix, const real *d_invQt,
+                const int Npredict, const int Ntrain, const int Ninputs)
+{
+    real *d_result;
+    cudaMalloc((void **)&d_result, sizeof(real) * Npredict);
+    real alpha = 1.f;
+    real beta = 0.f;
+    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_N, Npredict, Ntrain, &alpha, d_dist_matrix, 
+                Npredict, d_invQt, 1, &beta, d_result, 1));
+    cudaMemcpy(c_result, d_result, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
+    cudaFree(d_result);
+}
 
+real *gpu_transpose(cublasHandle_t handle, real *d_matrix, const int nrow, const int ncol)
+{
+    real *d_matrix_T;
+    real alpha = 1.f;
+    real beta = 0.f;
+    cudaMalloc((void **)&d_matrix_T, sizeof(real) * nrow * ncol );
+    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, 
+                nrow, ncol, &alpha, d_matrix, ncol, &beta, 
+                d_matrix, nrow, d_matrix_T, nrow));
+    return( d_matrix_T );
+}
+  
+/*********************************
+ * compute error:
+ * c_error = b - rowsum(a * dot(invQ, d_dist_matrix_T))
+ * arguments d_invQ, d_dist_matrix have been freed.
+ ********************************/
+void compute_error(cublasHandle_t handle, real *c_error,
+        real *d_dist_matrix,const  real *d_dist_matrix_T, 
+        real *d_invQ, real *c_theta_exp,
+        const int Npredict, const int Ntrain, const int Ninputs)
+{
+    real alpha = 1.f;
+    real beta = 0.f;
+    real *d_res_dot;
+    real *d_error;
+    
+    cudaMalloc((void **)&d_res_dot, sizeof(real) * Ntrain * Npredict);
+    cublasCheckErrors(CUBLAS_GEMM(
+                handle, CUBLAS_OP_N, CUBLAS_OP_T, 
+                Ntrain, Npredict, Ntrain, 
+                &alpha, d_invQ, Ntrain, 
+                d_dist_matrix, Npredict, 
+                &beta, d_res_dot, Ntrain));    // dot(invQ, d_dist_matrix_T)
+    gpu_elementwiseMult(d_dist_matrix_T, d_res_dot, Ntrain * Npredict);
+    d_error = gpu_rowSum(d_res_dot, Ntrain, Npredict);
+    gpu_scalarMinusVec(d_error, c_theta_exp[Ninputs], Npredict );
+ 
+    cudaMemcpy(c_error, d_error, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
+
+    cudaFree(d_dist_matrix);
+    cudaFree(d_invQ);
+    cudaFree(d_error);
+    cudaFree(d_res_dot);
+}
+
+/*********************************//*
+ * compute deriv:
+ *  
+ ********************************/
+void compute_deriv( cublasHandle_t handle, real *c_deriv, const real *c_theta_exp, 
+        real *d_predict, real *d_train,
+        const real *d_invQt, const real *d_dist_matrix_T,
+        const int Npredict, const int Ntrain, const int Ninputs)
+{
+    int i;
+    real alpha;
+    real beta = 0.f;
+ 
+    real *d_deriv, *d_aa;
+    cudaMalloc((void **)&d_deriv, sizeof(real) * Npredict );
+    cudaMalloc((void **)&d_aa, sizeof(real) * Ntrain * Npredict);
+    real *ptr_train, *ptr_predict, *ptr_deriv;
+    ptr_train = d_train;
+    ptr_predict = d_predict;
+    ptr_deriv = c_deriv;
+
+     for( i = 0; i < Ninputs; ++i){
+        gpu_crossMinus(ptr_train, ptr_predict, d_aa, Ntrain, Npredict );
+        ptr_train = ptr_train + Ntrain;
+        ptr_predict = ptr_predict + Npredict;
+        alpha = c_theta_exp[i];
+        gpu_elementwiseMult(d_dist_matrix_T, d_aa, Ntrain * Npredict);
+        cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, Ntrain, Npredict, &alpha, d_aa, Ntrain, d_invQt, 1, &beta, d_deriv,1));
+
+        cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
+        ptr_deriv = ptr_deriv + Npredict;
+     }
 
+     cudaFree(d_deriv);
+     cudaFree(d_aa);
+}
+
+
+
+
+ 
 /*********************************************//**
  * predict function:
  * CUDA version of the corresponding python code
@@ -68,97 +172,41 @@ void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
     cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
     cudaMalloc( (void **)&d_theta_exp_sqrt, sizeof(real) * theta_size );
     cudaMalloc( (void **)&d_invQt, sizeof(real) * Ntrain);
-    cublasCheckErrors(cublasSetVector( theta_size, sizeof(real), c_theta_exp_sqrt, 1, d_theta_exp_sqrt, 1 ));
-    cublasCheckErrors(cublasSetVector( theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
-    cublasCheckErrors(cublasSetVector( Ntrain, sizeof(real), c_invQt, 1, d_invQt, 1));
+    cublasCheckErrors(cublasSetVector( 
+                theta_size, sizeof(real), c_theta_exp_sqrt, 1, d_theta_exp_sqrt, 1 ));
+    cublasCheckErrors(cublasSetVector( 
+                theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
+    cublasCheckErrors(cublasSetVector( 
+                Ntrain, sizeof(real), c_invQt, 1, d_invQt, 1));
 
     //allocate and copy matrix on device
     cudaMalloc( (void **)&d_train, sizeof(real) * Ntrain * Ninputs );
     cudaMalloc( (void **)&d_invQ, sizeof(real) * Ntrain * Ntrain );
     cudaMalloc( (void **)&d_predict, sizeof(real) * Npredict * Ninputs );
-    cublasCheckErrors(cublasSetMatrix( Ntrain, Ninputs, sizeof(real), c_train, Ntrain, d_train, Ntrain ));
-    cublasCheckErrors(cublasSetMatrix( Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
-    cublasCheckErrors(cublasSetMatrix( Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
-
-    real * d_dist_matrix;
-    d_dist_matrix = compute_distance(d_predict, d_train, c_theta_exp, d_theta_exp, d_theta_exp_sqrt,
-                                    Npredict, Ntrain, Ninputs);
-   
-
-        
+    cublasCheckErrors(cublasSetMatrix( 
+                Ntrain, Ninputs, sizeof(real), c_train, Ntrain, d_train, Ntrain ));
+    cublasCheckErrors(cublasSetMatrix( 
+                Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
+    cublasCheckErrors(cublasSetMatrix( 
+                Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
+
+    real *d_dist_matrix, *d_dist_matrix_T;
     
-    /*********************************
-     * compute mu:
-     * mu = a * invQt (dot product)
-     ********************************/
-    real *d_result;
-    cudaMalloc((void **)&d_result, sizeof(real) * Npredict);
-    real alpha = 1.f;
-    real beta = 0.f;
-    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_N, Npredict, Ntrain, &alpha, d_dist_matrix, 
-                Npredict, d_invQt, 1, &beta, d_result, 1));
-    cudaMemcpy(c_result, d_result, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
-       
+    d_dist_matrix = compute_distance(d_predict, d_train, c_theta_exp, d_theta_exp, 
+            d_theta_exp_sqrt, Npredict, Ntrain, Ninputs);
+ 
+    compute_result(handle, c_result, d_dist_matrix, d_invQt, Npredict, Ntrain, Ninputs);
     
-   /*********************************
-    * compute var:
-    * var = b - rowsum(a * dot(invQ, a_T))
-    ********************************/
-    real *d_temp_dot, *d_dist_matrix_T;
-    real *d_error;
-
-    cudaMalloc((void **)&d_temp_dot, sizeof(real) * Ntrain * Npredict);
-    cudaMalloc((void **)&d_dist_matrix_T, sizeof(real) * Ntrain * Npredict);
-
-    cublasCheckErrors(CUBLAS_GEMM(handle, CUBLAS_OP_N, CUBLAS_OP_T, Ntrain, Npredict, Ntrain, &alpha, d_invQ, Ntrain, d_dist_matrix, Npredict, &beta, d_temp_dot, Ntrain));
-    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, Ntrain, Npredict, &alpha, d_dist_matrix, Npredict, &beta, d_dist_matrix, Ntrain, d_dist_matrix_T, Ntrain));
+    d_dist_matrix_T = gpu_transpose(handle, d_dist_matrix, Ntrain, Npredict);
     
-    cudaFree(d_dist_matrix);
-    cudaFree(d_invQ);
+    compute_deriv( handle, c_deriv, c_theta_exp, 
+            d_predict, d_train, d_invQt, d_dist_matrix_T,
+            Npredict, Ntrain, Ninputs);
     
-    gpu_elementwiseMult(d_dist_matrix_T, d_temp_dot, Ntrain * Npredict);
-    d_error = gpu_rowSum(d_temp_dot, Ntrain, Npredict);
-    gpu_scalarMinusVec(d_error, c_theta_exp[Ninputs], Npredict );
-    cudaMemcpy(c_error, d_error, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
-
-    cudaFree(d_error);
-    cudaFree(d_temp_dot);
-
-
-   /*********************************
-    * compute deriv:
-    * 
-    ********************************/
-    real *d_deriv;
-    cudaMalloc((void **)&d_deriv, sizeof(real) * Npredict );
-   
-    real *d_aa;
-    cudaMalloc((void **)&d_aa, sizeof(real) * Ntrain * Npredict);
-    real *ptr_inputs, *ptr_testing, *ptr_deriv;
-    ptr_inputs = d_train;
-    ptr_testing = d_predict;
-    ptr_deriv = c_deriv;
-
-     for( i = 0; i < Ninputs; ++i){
-        gpu_crossMinus(ptr_inputs, ptr_testing, d_aa, Ntrain, Npredict );
-        ptr_inputs = ptr_inputs + Ntrain;
-        ptr_testing = ptr_testing + Npredict;
-        alpha = c_theta_exp[i];
-        gpu_elementwiseMult(d_dist_matrix_T, d_aa, Ntrain * Npredict);
-        cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, Ntrain, Npredict, &alpha, d_aa, Ntrain, d_invQt, 1, &beta, d_deriv,1));
-
-        cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
-        ptr_deriv = ptr_deriv + Npredict;
-     }
-
-     cudaFree(d_result);
-     cudaFree(d_invQt);
-     cudaFree(d_dist_matrix_T);
-     cudaFree(d_aa);
-     cudaFree(d_train);
-     cudaFree(d_deriv);
-     cudaFree(d_theta_exp);
-     cudaFree(d_predict);
-   
+    cudaFree(d_invQt);
+    cudaFree(d_dist_matrix_T);
+    cudaFree(d_train);
+    cudaFree(d_theta_exp);
+    cudaFree(d_predict);
 }
 }

From daf90f95d524716832b84e94b96880927cc68d9b Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 22 Jun 2015 00:57:43 +0100
Subject: [PATCH 090/117] correcting the error that compute_error is not called
 in last version

---
 gp_emulator/gpu/predict.cu | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index a10b514..dafe9a2 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -74,7 +74,7 @@ real *gpu_transpose(cublasHandle_t handle, real *d_matrix, const int nrow, const
  ********************************/
 void compute_error(cublasHandle_t handle, real *c_error,
         real *d_dist_matrix,const  real *d_dist_matrix_T, 
-        real *d_invQ, real *c_theta_exp,
+        real *d_invQ, const real *c_theta_exp,
         const int Npredict, const int Ntrain, const int Ninputs)
 {
     real alpha = 1.f;
@@ -192,13 +192,17 @@ void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
 
     real *d_dist_matrix, *d_dist_matrix_T;
     
-    d_dist_matrix = compute_distance(d_predict, d_train, c_theta_exp, d_theta_exp, 
-            d_theta_exp_sqrt, Npredict, Ntrain, Ninputs);
- 
-    compute_result(handle, c_result, d_dist_matrix, d_invQt, Npredict, Ntrain, Ninputs);
-    
+    d_dist_matrix = compute_distance(d_predict, d_train, 
+            c_theta_exp, d_theta_exp, 
+            d_theta_exp_sqrt, 
+            Npredict, Ntrain, Ninputs);
+    compute_result(handle, c_result, 
+            d_dist_matrix, d_invQt, 
+            Npredict, Ntrain, Ninputs);
     d_dist_matrix_T = gpu_transpose(handle, d_dist_matrix, Ntrain, Npredict);
-    
+    compute_error(handle, c_error, d_dist_matrix, 
+            d_dist_matrix_T, d_invQ, c_theta_exp,
+            Npredict, Ntrain, Ninputs); 
     compute_deriv( handle, c_deriv, c_theta_exp, 
             d_predict, d_train, d_invQt, d_dist_matrix_T,
             Npredict, Ntrain, Ninputs);

From eb4f09b5c9528bf0a0f4ce6361e509dc69ca1d34 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 22 Jun 2015 15:43:45 +0100
Subject: [PATCH 091/117] tidy up, change variable names, change macro to
 inline functions

---
 gp_emulator/gpu/gpu_predict.h             | 13 +++++++-----
 gp_emulator/gpu/kernel_cdist.cu           | 13 +++++++-----
 gp_emulator/gpu/kernel_crossMinus.cu      |  4 ++--
 gp_emulator/gpu/kernel_elementwiseMult.cu | 25 ++++++++++++-----------
 4 files changed, 31 insertions(+), 24 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 14500fc..f044771 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -13,16 +13,15 @@
 #include <cublas_v2.h>
 #define DOUBLE__PRECISION
 #define CUDA_BLOCK 2
- 
+#define MIN_NPREDICT 1000 //ensure there will be enough amount of threads to launch. 
+#define MAX_NUM_THREAD 1024 //sm_20
+#define MAX_NUM_BLOCK 65536
 #ifdef DOUBLE__PRECISION
   #define real double
 #else
   #define real float
 #endif 
 
-#define IDX2D(i,j,ld) (((j)*(ld))+(i))//column major
-
-
 #ifdef DOUBLE__PRECISION
   #define CUBLAS_GEMV cublasDgemv
   #define CUBLAS_GEMM cublasDgemm
@@ -33,7 +32,11 @@
   #define CUBLAS_GEAM cublasSgeam
 #endif
 
-
+__forceinline__ __host__ __device__
+int IDX2D(int row, int col, int lead_dim)
+{
+    return(((col)*(lead_dim))+(row));
+}
 
 PyArrayObject *pyvector(PyObject *objin);
 real*pyvector_to_Carrayptrs(PyArrayObject *arrayin);
diff --git a/gp_emulator/gpu/kernel_cdist.cu b/gp_emulator/gpu/kernel_cdist.cu
index 2a08867..becef22 100644
--- a/gp_emulator/gpu/kernel_cdist.cu
+++ b/gp_emulator/gpu/kernel_cdist.cu
@@ -7,6 +7,9 @@
  *********************************************/
 #include "gpu_predict.h"
 #define min(a, b) (((a) < (b)) ? (a) : (b))
+#define CDIST_NTHREAD_X 256
+#define CDIST_NTHREAD_Y 4
+#define CDIST_NTHREAD_Z 1
 
 __global__
 void kernel_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int nrow2, const int ncol)
@@ -22,16 +25,16 @@ void kernel_cdist(const real *input1, const real *input2, real *output, const in
 
 void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1, const int nrow2, const int ncol2)
 {
-    if( nrow2 < 1000 )
+    if( nrow2 < MIN_NPREDICT )
     {
-        printf("gpu_cdist: nrow2 < 1000");
+        printf("gpu_cdist: nrow2(Npredict) < %d\n", MIN_NPREDICT);
         exit(EXIT_FAILURE);
     }
 
     dim3 nthread, nblock;
-    nthread.x = 256;
-    nthread.y = 4;
-    nthread.z = 1;
+    nthread.x = CDIST_NTHREAD_X;
+    nthread.y = CDIST_NTHREAD_Y;
+    nthread.z = CDIST_NTHREAD_Z;
 
     nblock.x = ceil( float(nrow2) / float(nthread.x) );
     nblock.y = ceil( float(nrow1) / float(nthread.y) );
diff --git a/gp_emulator/gpu/kernel_crossMinus.cu b/gp_emulator/gpu/kernel_crossMinus.cu
index 8340bbe..ff8c4a3 100644
--- a/gp_emulator/gpu/kernel_crossMinus.cu
+++ b/gp_emulator/gpu/kernel_crossMinus.cu
@@ -18,10 +18,10 @@ void kernel_crossMinus(const real *v1,const real *v2, real *mat_res, const int v
 void gpu_crossMinus(const real *v1, const real *v2, real *mat_res, const int v1_len, const int v2_len)
 {
     dim3 nthread, nblock;
-    if( v2_len < 1024 )
+    if( v2_len < MAX_NUM_THREAD )
         nthread.y = v2_len;
     else
-        nthread.y = 1024;
+        nthread.y = MAX_NUM_THREAD;
     
     nthread.x = 1;
     nblock.x = v1_len;
diff --git a/gp_emulator/gpu/kernel_elementwiseMult.cu b/gp_emulator/gpu/kernel_elementwiseMult.cu
index 6eeb858..1c397ac 100644
--- a/gp_emulator/gpu/kernel_elementwiseMult.cu
+++ b/gp_emulator/gpu/kernel_elementwiseMult.cu
@@ -1,10 +1,10 @@
 /*********************************************//**
  * vector elementwise multiplication
- * v2_{i} = v2_{i} * v1_{i}
+ * vector2_{i} = vector2_{i} * vector1_{i}
  *********************************************/
 #include "gpu_predict.h"
 __global__
-void kernel_elementwiseMult(const real *v1, real *v2, const int size)
+void kernel_elementwiseMult(const real *vector1, real *vector2, const int size)
 {
     int i, index;
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
@@ -13,29 +13,30 @@ void kernel_elementwiseMult(const real *v1, real *v2, const int size)
         index = ix * CUDA_BLOCK + i;
         if( index < size)
         {
-            v2[index] = v2[index] * v1[index];
+            vector2[index] = vector2[index] * vector1[index];
         }
     }
 }
 
 
-void gpu_elementwiseMult( const real *v1, real *v2, const int size )
+void gpu_elementwiseMult( const real *vector1, real *vector2, const int size )
 {
     int nthread, nblock;
 
-    if( size < float(1024) / float(CUDA_BLOCK) )
+    if( size < float(MAX_NUM_THREAD) / float(CUDA_BLOCK) )
     {
-        printf("gpu_elementwiseMult: size = %d [ < 1024 / CUDA_BLOCK ].\n", size);
+        printf("gpu_elementwiseMult: size = %d [ < MAX_NUM_THREAD / CUDA_BLOCK ].\n", size);
         exit(EXIT_FAILURE);
     }
 
-    if( size > 65536 * 1024 )
+    nthread = MAX_NUM_THREAD;
+    nblock = ceil( float(size) / float(CUDA_BLOCK) / float(nthread) );
+    
+    if( nblock > MAX_NUM_BLOCK )
     {
-        printf("gpu_elementwiseMult: size = %d [ > 65536 * 1024 / CUDA_BLOCK ].\n", size);
+        printf("gpu_elementwiseMult: nblock outside the range of [1, MAX_NUM_BLOCK]\n");
         exit(EXIT_FAILURE);
     }
-
-    nthread = 1024;
-    nblock = ceil( float(size) / float(CUDA_BLOCK) / float(nthread) );
-    kernel_elementwiseMult<<<nblock,nthread>>>(v1, v2, size);
+    
+    kernel_elementwiseMult<<<nblock,nthread>>>(vector1, vector2, size);
 }

From 775dfb799f998f8bbc29451997c90090d5040454 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 22 Jun 2015 15:46:35 +0100
Subject: [PATCH 092/117] char float64 to function np.float64

---
 tests/benchmark.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/benchmark.py b/tests/benchmark.py
index 63d91ef..20f5231 100644
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -20,7 +20,7 @@ def set_testing_val (self, P, N, M):
     print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
     print '-----------------------------'
 
-    for size in xrange(np.int(1e4), np.int(1e7), np.int(1e4)):
+    for size in xrange(np.int(1e3), np.int(1e5), np.int(1e4)):
         N = size
         M = 250
         P = 10
@@ -39,7 +39,7 @@ def set_testing_val (self, P, N, M):
     	
         #GPU predict
         start = time.time()
-    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, prec = 'float64', threashold = 2e5)
+    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, precision = np.float64, threshold = 1e4)
     	end =time.time()
     	gputime = end - start
         print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (N, cputime, gputime, cputime/gputime),

From 05858b4c433fef99c9f0bdfd81fa1c30ef6b77ab Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 24 Jun 2015 09:58:39 +0100
Subject: [PATCH 093/117] c++ implementation of predict function

---
 gp_emulator/gpu/CMakeLists.txt   |   5 +-
 gp_emulator/gpu/_gpu_predict.cpp | 189 +++++++++++++++++++++++++++++++
 gp_emulator/gpu/predictcpp.cu    | 184 ++++++++++++++++++++++++++++++
 3 files changed, 376 insertions(+), 2 deletions(-)
 create mode 100644 gp_emulator/gpu/_gpu_predict.cpp
 create mode 100644 gp_emulator/gpu/predictcpp.cu

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 4764d3c..06383a0 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -12,8 +12,9 @@ include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
-cuda_add_library(_gpu_predict  _gpu_predict.c 
-                 predict.cu  
+cuda_add_library(_gpu_predict  _gpu_predict.cpp 
+                 predict.cu 
+                 predictcpp.cu
                  computeTranspose.cu 
                  kernel_vectorTimesMatrix.cu 
                  kernel_init_array.cu
diff --git a/gp_emulator/gpu/_gpu_predict.cpp b/gp_emulator/gpu/_gpu_predict.cpp
new file mode 100644
index 0000000..a826372
--- /dev/null
+++ b/gp_emulator/gpu/_gpu_predict.cpp
@@ -0,0 +1,189 @@
+/**********************************//**
+ * source code of share library _gpu_predict.so
+ * importing inputs from python
+ * calling GPU predict function by predict_wrap()
+ * python utility functions are based on the following template:
+ *  http://wiki.scipy.org/Cookbook/C_Extensions/NumPy_arrays
+ **********************************/
+
+#include "gpu_predict.h"
+#include<errno.h>
+
+/*********************************//**
+ * Set up the methods table
+ *********************************/
+static PyMethodDef gpuMethods[] = 
+{
+    {"predict_wrap", predict_wrap, METH_VARARGS},
+    {NULL, NULL}     /* Sentinel - marks the end of this structure */
+};
+
+/**********************************//**
+ * Initialize the C_test functions 
+ * Module name must be _C_arraytest in compile and linked 
+ **********************************/
+
+extern "C"{
+void init_gpu_predict()  
+{
+    (void) Py_InitModule("_gpu_predict", gpuMethods);
+    import_array();  // Must be present for NumPy.  Called first after above line.
+}
+}
+
+
+
+/**********************************//**
+ * Check the dimension and data type 
+ * of input python arrays.
+ **********************************/
+void checkRealVector(PyArrayObject *vec)  
+{
+    if( sizeof( real ) == sizeof( double ) )
+    {
+        if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  
+        {
+            printf( "In checkRealVector: array must be of type Double vector\n");
+            exit(EXIT_FAILURE);
+        }
+       
+    }
+    if( sizeof( real ) == sizeof( float ) ) 
+    {
+        if (vec->descr->type_num != NPY_FLOAT32 || vec->nd != 1)  
+        {
+            printf("In checkRealVector: array must be of type Float32 vector\n");
+            exit(EXIT_FAILURE);
+        }
+    }
+}
+
+
+
+/**********************************//**
+ * Convert data (vector) from PyArrayObject to real
+ **********************************/
+real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  
+{
+    checkRealVector(arrayin);
+    return  (real *) arrayin->data;  /* pointer to arrayin data as real */
+}
+
+
+
+/**********************************//**
+ * getPredictDataFromPython
+ * Assign multiple input python arrays, which are all in 1D
+ * to C array.
+ **********************************/
+void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt, real **c_invQ, 
+                              real **c_predict, real **c_train,
+                              real **c_result, real **c_error, real **c_deriv,
+                              int *Npredict, int *Ntrain, int *Ninputs, int *theta_size)
+{
+    PyArrayObject *py_theta_exp, *py_train, *py_invQt, *py_invQ, *py_predict;
+    PyArrayObject *py_result, *py_error, *py_deriv;
+
+    PyArg_ParseTuple ( args, "O!O!O!O!O!O!O!O!iiii",
+        &PyArray_Type, &py_theta_exp,
+        &PyArray_Type, &py_train,
+        &PyArray_Type, &py_invQt,
+        &PyArray_Type, &py_invQ,
+        &PyArray_Type, &py_predict,
+        &PyArray_Type, &py_result,
+        &PyArray_Type, &py_error,
+        &PyArray_Type, &py_deriv,
+        Npredict, Ntrain, Ninputs, theta_size);
+
+    *c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
+    *c_train = pyvector_to_Carrayptrs( py_train );
+    *c_invQt = pyvector_to_Carrayptrs( py_invQt );
+    *c_invQ = pyvector_to_Carrayptrs( py_invQ );
+    *c_predict = pyvector_to_Carrayptrs( py_predict );
+    *c_result = pyvector_to_Carrayptrs( py_result );
+    *c_error = pyvector_to_Carrayptrs( py_error );
+    *c_deriv = pyvector_to_Carrayptrs( py_deriv );
+}
+
+
+
+/**********************************//**
+ * predict_wrap
+ * 1) call getPredictDataFromPython;
+ * 2) transpose 2D arrays (data arranged in 1D) to column major;
+ * 3) calling GPU predict function;
+ **********************************/
+PyObject *predict_wrap ( PyObject *self, PyObject *args )
+{
+    int i;
+    int Npredict, Ntrain, Ninputs, theta_size;
+    real *c_theta_exp;
+    real *c_train, *c_invQt, *c_invQ, *c_predict;
+    real *c_result, *c_error, *c_deriv;
+   
+    getPredictDataFromPython(args, &c_theta_exp, &c_invQt, &c_invQ,
+                             &c_predict, &c_train,
+                             &c_result, &c_error, &c_deriv,
+                             &Npredict, &Ntrain, &Ninputs,  &theta_size);
+
+    //transpose 2D array to column major to cope with cublas
+    real *c_invQ_T, *c_train_T, *c_predict_T;
+    c_invQ_T = computeTranspose( c_invQ, Ntrain, Ntrain );
+    c_train_T = computeTranspose( c_train, Ninputs, Ntrain );
+    c_predict_T = computeTranspose( c_predict, Ninputs, Npredict);
+
+    
+    real *c_theta_exp_sqrt;
+    c_theta_exp_sqrt = (real *)malloc( sizeof(real) * theta_size );
+    for( i=0; i < theta_size; i++ )
+    {
+        c_theta_exp_sqrt[i] = sqrt( c_theta_exp[i] );
+    }
+
+
+    // predict( c_theta_exp, c_train_T, c_invQt, c_invQ_T, c_predict_T, 
+    //         c_result, c_error, c_deriv,
+    //         Npredict, Ntrain, Ninputs, theta_size );
+
+    gpuPredict gpu_predict(
+        c_theta_exp, c_theta_exp_sqrt,
+        c_invQt, c_invQ_T, 
+        c_predict_T,c_train_T, 
+        c_result, c_error, c_deriv,
+        Npredict, Ntrain, Ninputs, theta_size );
+    gpu_predict.predict();
+
+
+
+
+
+
+    free(c_invQ_T);
+    free(c_train_T);
+    free(c_predict_T);
+   
+    Py_INCREF(Py_None);
+    return Py_None;
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/gp_emulator/gpu/predictcpp.cu b/gp_emulator/gpu/predictcpp.cu
new file mode 100644
index 0000000..172babe
--- /dev/null
+++ b/gp_emulator/gpu/predictcpp.cu
@@ -0,0 +1,184 @@
+
+/*********************************************//** 
+ * CUDA implementation of derived from GaussianProcess
+ * in python code. 
+ * 
+ * Sinan Shi 
+ *********************************************/
+#include "gpu_predict.h"
+#include <stdlib.h>
+#define debug
+
+
+void gpuPredict::init_gpu(void)
+{
+    cublasCreate(&handle);
+    cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
+    cudaMalloc( (void **)&d_theta_exp_sqrt, sizeof(real) * theta_size );
+    cudaMalloc( (void **)&d_invQt, sizeof(real) * Ntrain);
+    cublasCheckErrors(cublasSetVector( 
+                theta_size, sizeof(real), c_theta_exp_sqrt, 1, d_theta_exp_sqrt, 1 ));
+    cublasCheckErrors(cublasSetVector( 
+                theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
+    cublasCheckErrors(cublasSetVector( 
+                Ntrain, sizeof(real), c_invQt, 1, d_invQt, 1));
+
+    //allocate and copy matrix on device
+    cudaMalloc( (void **)&d_train, sizeof(real) * Ntrain * Ninputs );
+    cudaMalloc( (void **)&d_invQ, sizeof(real) * Ntrain * Ntrain );
+    cudaMalloc( (void **)&d_predict, sizeof(real) * Npredict * Ninputs );
+    cublasCheckErrors(cublasSetMatrix( 
+                Ntrain, Ninputs, sizeof(real), c_train, Ntrain, d_train, Ntrain ));
+    cublasCheckErrors(cublasSetMatrix( 
+                Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
+    cublasCheckErrors(cublasSetMatrix( 
+                Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
+
+}
+
+/*********************************//*
+ * Euclidian distance calculation
+ * 1) res_mv1 = theta_exp_sqrt_{,Ninputs} * train
+ * 2) res_mv2 = theta_exp_sqrt_{,Ninputs} * predict
+ * 3) dist_matrix = cidist(res_mv1, res_mv2)
+ * 4) dist_matrix = -0.5 * exp(expX_{Ninputs})
+ * Notice: distance is equivalent to distance^T (a^T) in python
+ ********************************/ 
+void gpuPredict::compute_distance(void)
+{
+    real *d_res_mv1, *d_res_mv2;
+    cudaMalloc((void **)&d_res_mv1, sizeof(real) * Ntrain * Ninputs);
+    cudaMalloc((void **)&d_res_mv2, sizeof(real) * Npredict * Ninputs);
+    cudaMalloc((void **)&d_dist_matrix, sizeof(real) * Ntrain * Npredict);
+
+    gpu_vectorTimesMatrix(d_train, d_theta_exp_sqrt, d_res_mv1, Ntrain, Ninputs);
+    gpu_vectorTimesMatrix(d_predict, d_theta_exp_sqrt, d_res_mv2, Npredict, Ninputs);
+    gpu_init_array( d_dist_matrix, 0.0, Npredict * Ntrain );
+    gpu_cdist(d_res_mv1, d_res_mv2, d_dist_matrix, Ntrain, Ninputs, Npredict, Ninputs);
+    gpu_matrixExp(d_dist_matrix, -0.5, c_theta_exp[Ninputs], Ntrain * Npredict);
+
+    cudaFree(d_res_mv1);
+    cudaFree(d_res_mv2);
+}
+
+/*********************************//*
+ * compute result:
+ * c_result = dist_matrix * invQt (dot product)
+ ********************************/
+void gpuPredict::compute_result(void)
+{
+    real *d_result;
+    cudaMalloc((void **)&d_result, sizeof(real) * Npredict);
+    real alpha = 1.f;
+    real beta = 0.f;
+    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_N, Npredict, Ntrain, &alpha, d_dist_matrix, 
+                Npredict, d_invQt, 1, &beta, d_result, 1));
+    cudaMemcpy(c_result, d_result, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
+    cudaFree(d_result);
+}
+
+
+real * gpuPredict::gpu_transpose(real *d_matrix, const int nrow, const int ncol)
+{
+    real *d_matrix_T;
+    real alpha = 1.f;
+    real beta = 0.f;
+    cudaMalloc((void **)&d_matrix_T, sizeof(real) * nrow * ncol );
+    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, 
+                nrow, ncol, &alpha, d_matrix, ncol, &beta, 
+                d_matrix, nrow, d_matrix_T, nrow));
+    return( d_matrix_T );
+}
+
+/*********************************
+ * compute error:
+ * c_error = b - rowsum(a * dot(invQ, d_dist_matrix_T))
+ * arguments d_invQ, d_dist_matrix have been freed.
+ ********************************/
+void gpuPredict::compute_error()
+{
+    real alpha = 1.f;
+    real beta = 0.f;
+    real *d_res_dot;
+    real *d_error;
+    
+    cudaMalloc((void **)&d_res_dot, sizeof(real) * Ntrain * Npredict);
+    cublasCheckErrors(CUBLAS_GEMM(
+                handle, CUBLAS_OP_N, CUBLAS_OP_T, 
+                Ntrain, Npredict, Ntrain, 
+                &alpha, d_invQ, Ntrain, 
+                d_dist_matrix, Npredict, 
+                &beta, d_res_dot, Ntrain));  // dot(invQ, d_dist_matrix_T)
+    d_dist_matrix_T = gpuPredict::gpu_transpose(d_dist_matrix, Ntrain, Npredict);
+    gpu_elementwiseMult(d_dist_matrix_T, d_res_dot, Ntrain * Npredict);
+    d_error = gpu_rowSum(d_res_dot, Ntrain, Npredict);
+    gpu_scalarMinusVec(d_error, c_theta_exp[Ninputs], Npredict );
+ 
+    cudaMemcpy(c_error, d_error, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
+
+    cudaFree(d_dist_matrix);
+    cudaFree(d_invQ);
+    cudaFree(d_error);
+    cudaFree(d_res_dot);
+}
+
+
+
+/*********************************//*
+ * compute deriv:
+ *  
+ ********************************/
+void gpuPredict::compute_deriv( void )
+{
+    int i;
+    real alpha;
+    real beta = 0.f;
+ 
+    real *d_deriv, *d_aa;
+    cudaMalloc((void **)&d_deriv, sizeof(real) * Npredict );
+    cudaMalloc((void **)&d_aa, sizeof(real) * Ntrain * Npredict);
+    real *ptr_train, *ptr_predict, *ptr_deriv;
+    ptr_train = d_train;
+    ptr_predict = d_predict;
+    ptr_deriv = c_deriv;
+
+     for( i = 0; i < Ninputs; ++i){
+        gpu_crossMinus(ptr_train, ptr_predict, d_aa, Ntrain, Npredict );
+        ptr_train = ptr_train + Ntrain;
+        ptr_predict = ptr_predict + Npredict;
+        alpha = c_theta_exp[i];
+        gpu_elementwiseMult(d_dist_matrix_T, d_aa, Ntrain * Npredict);
+        cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, Ntrain, Npredict, &alpha, d_aa, Ntrain, d_invQt, 1, &beta, d_deriv,1));
+
+        cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
+        ptr_deriv = ptr_deriv + Npredict;
+     }
+
+     cudaFree(d_deriv);
+     cudaFree(d_aa);
+}
+
+
+void gpuPredict::predict( void )
+{
+     gpuPredict::init_gpu();
+     gpuPredict::compute_distance();
+     gpuPredict::compute_result();
+     gpuPredict::compute_error();
+     gpuPredict::compute_deriv();
+    
+    cublasDestroy(handle);
+    cudaFree(d_invQt);
+    cudaFree(d_dist_matrix_T);
+    cudaFree(d_train);
+    cudaFree(d_theta_exp);
+    cudaFree(d_predict);
+    cudaFree(d_theta_exp_sqrt);
+}
+
+
+
+
+
+
+

From 06a69319e58300cbd0a6a991ccabcef4aed20b9c Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 24 Jun 2015 10:09:48 +0100
Subject: [PATCH 094/117] finish c++ implementation header file

---
 gp_emulator/gpu/gpu_predict.h | 63 +++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index f044771..6a62b33 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -69,6 +69,69 @@ void gpu_crossMinus(const real *v1, const real *v2, real *mat_res, const int v1_
 
 
 
+
+class gpuPredict
+{
+  real *c_result, *c_error, *c_deriv;
+  const real *c_theta_exp, *c_theta_exp_sqrt;
+  const real *c_invQt, *c_invQ, *c_predict, *c_train, *c_inputs;
+  const int Npredict, Ntrain, Ninputs;
+  const int theta_size;
+
+  real *d_result, *d_error, *d_deriv;
+  real *d_theta_exp, *d_theta_exp_sqrt;
+  real *d_invQt, *d_invQ, *d_predict, *d_train, *d_inputs;
+  real *d_dist_matrix; real *d_dist_matrix_T;
+  cublasHandle_t handle;
+
+
+
+  public:
+    gpuPredict(real *ctheta_exp, 
+      real *ctheta_exp_sqrt,
+      real *cinvQt,
+      real *cinvQ, 
+      real *cpredict,          
+      real *ctrain,
+      real *cresult, 
+      real *cerror,
+      real *cderiv,
+      int npredict,
+      int ntrain,
+      int ninputs,
+      int thetasize):
+    c_theta_exp(ctheta_exp),
+    c_theta_exp_sqrt(ctheta_exp_sqrt),
+    c_invQt(cinvQt),
+    c_invQ(cinvQ),
+    c_predict(cpredict),
+    c_train(ctrain),
+    c_result(cresult),
+    c_error(cerror),
+    c_deriv(cderiv),
+    Npredict(npredict),
+    Ntrain(ntrain),
+    Ninputs(ninputs),
+    theta_size(thetasize){};
+    
+
+    real * gpu_transpose(real *, const int, const int);
+    void init_gpu(void);
+    void compute_distance(void);
+    void compute_result(void);
+    void compute_error(void);
+    void compute_deriv(void);
+    void predict(void);
+
+
+};
+
+
+
+
+
+
+
 // error check macros
 #define cudaCheckErrors(msg) \
     do { \

From c701bcf025ab92c33c231dd7157100d0bc7b3139 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 24 Jun 2015 10:10:22 +0100
Subject: [PATCH 095/117] tidyup

---
 gp_emulator/gpu/predictcpp.cu | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/gp_emulator/gpu/predictcpp.cu b/gp_emulator/gpu/predictcpp.cu
index 172babe..a015edf 100644
--- a/gp_emulator/gpu/predictcpp.cu
+++ b/gp_emulator/gpu/predictcpp.cu
@@ -166,14 +166,14 @@ void gpuPredict::predict( void )
      gpuPredict::compute_result();
      gpuPredict::compute_error();
      gpuPredict::compute_deriv();
-    
-    cublasDestroy(handle);
-    cudaFree(d_invQt);
-    cudaFree(d_dist_matrix_T);
-    cudaFree(d_train);
-    cudaFree(d_theta_exp);
-    cudaFree(d_predict);
-    cudaFree(d_theta_exp_sqrt);
+     
+     cublasDestroy(handle);
+     cudaFree(d_invQt);
+     cudaFree(d_dist_matrix_T);
+     cudaFree(d_train);
+     cudaFree(d_theta_exp);
+     cudaFree(d_predict);
+     cudaFree(d_theta_exp_sqrt);
 }
 
 

From decbaf0abc34acb987c359f2710be80a43220f6c Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 24 Jun 2015 10:55:50 +0100
Subject: [PATCH 096/117] reduce unit test iterations

---
 tests/unit_tests.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index 5a8c9ea..69a987a 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -79,7 +79,7 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    for size in xrange(10000, 1000000, 10000):
+    for size in xrange(int(1e4), int(2e5),int(5e4)):
         N=size
         M=250
         P=10

From 25bbed1064ab3bbfbf4cc5ae01d6c3f2319a71e4 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 24 Jun 2015 11:02:50 +0100
Subject: [PATCH 097/117] free_gpu()

---
 gp_emulator/gpu/gpu_predict.h |  3 +--
 gp_emulator/gpu/predictcpp.cu | 20 ++++++++++++--------
 2 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 6a62b33..54a11e1 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -122,8 +122,7 @@ class gpuPredict
     void compute_error(void);
     void compute_deriv(void);
     void predict(void);
-
-
+    void free_gpu(void);
 };
 
 
diff --git a/gp_emulator/gpu/predictcpp.cu b/gp_emulator/gpu/predictcpp.cu
index a015edf..ca4defb 100644
--- a/gp_emulator/gpu/predictcpp.cu
+++ b/gp_emulator/gpu/predictcpp.cu
@@ -158,15 +158,8 @@ void gpuPredict::compute_deriv( void )
      cudaFree(d_aa);
 }
 
-
-void gpuPredict::predict( void )
+void gpuPredict::free_gpu(void)
 {
-     gpuPredict::init_gpu();
-     gpuPredict::compute_distance();
-     gpuPredict::compute_result();
-     gpuPredict::compute_error();
-     gpuPredict::compute_deriv();
-     
      cublasDestroy(handle);
      cudaFree(d_invQt);
      cudaFree(d_dist_matrix_T);
@@ -177,6 +170,17 @@ void gpuPredict::predict( void )
 }
 
 
+void gpuPredict::predict( void )
+{
+     gpuPredict::init_gpu();
+     gpuPredict::compute_distance();
+     gpuPredict::compute_result();
+     gpuPredict::compute_error();
+     gpuPredict::compute_deriv();
+     gpuPredict::free_gpu();
+}
+
+
 
 
 

From df574fdba3a7b4711bb26b74ffd77a13536e43ae Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 24 Jun 2015 16:23:33 +0100
Subject: [PATCH 098/117] setup test

---
 CMakeLists.txt                |  3 ++-
 README.rst                    | 27 +++++++--------------------
 gp_emulator/gpu/gpu_predict.h |  2 +-
 setup.py                      |  6 +++++-
 tests/benchmark.py            |  5 +++--
 5 files changed, 18 insertions(+), 25 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cfa7e47..18601e8 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -6,7 +6,8 @@ subdirs(gp_emulator/gpu)
 set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib)
 set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/tests)
 string(ASCII 27 Esc)
+add_definitions(-DDOUBLE__PRECISION)
 
 set(ColourReset "${Esc}[m")
 set(Red  "${Esc}[1;31m")
-message("${Red}Type this line or put it in ~/.bashrc or ~/.bash_profile:\nexport PYTHONPATH=$PYTHONPATH:${LIBRARY_OUTPUT_PATH} ${ColourReset}")
+message("${Red}export PYTHONPATH=$PYTHONPATH:${LIBRARY_OUTPUT_PATH} ${ColourReset}")
diff --git a/README.rst b/README.rst
index a9a99ff..21dd3c5 100644
--- a/README.rst
+++ b/README.rst
@@ -18,28 +18,15 @@ Requirements:
     * CUDA 5.0 or later
     * CUnit
 
-Install without GPU predict:
----------
-```bash
-cd gp_emulator
-python setup.py install
-```
-
 Install with GPU predict:
 ---------
-1. install python modules
-    ```bash
-python setup.py install
-```
-2. compile CUDA modules
-    ```bash
-cd build
-cmake ..
-make 
-```
-3. export the python path following the cmake instruction, e.g.
-    ```bash
-    export PYTHONPATH=$PYTHONPATH:/home/sinan/gp-emulator/emulator/build/lib```
+1. decide precision by modifying  CMakeList.txt 
+   ```add_definition(-DDOUBLE_PRECISION) # double
+   ````
+2. install
+   ```
+   python setup.py install
+   ```
     
 Tests (only with predict):
 ----------
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 54a11e1..15cac77 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,7 +11,7 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
-#define DOUBLE__PRECISION
+//#define DOUBLE__PRECISION
 #define CUDA_BLOCK 2
 #define MIN_NPREDICT 1000 //ensure there will be enough amount of threads to launch. 
 #define MAX_NUM_THREAD 1024 //sm_20
diff --git a/setup.py b/setup.py
index 1845ae3..f273184 100644
--- a/setup.py
+++ b/setup.py
@@ -48,6 +48,10 @@ def run(self):
         run_cmake()
         os.chdir(cwd)
         _build.build.run(self)
+class test(_install.install):
+    def run(self):
+        os.system("python tests/benchmark.py")
+
 
 
 setup(name='gp_emulator',
@@ -57,5 +61,5 @@ def run(self):
       author_email='j.gomez-dans@ucl.ac.uk',
       url='http://bitbucket.org/gomezdansj/gp_emulator',
       packages=['gp_emulator'],
-      cmdclass={'build':build, 'install':install},
+      cmdclass={'build':build, 'install':install, 'test':test},
      )
diff --git a/tests/benchmark.py b/tests/benchmark.py
index 20f5231..beb0853 100644
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -6,6 +6,7 @@
 import time
 from gp_emulator import GaussianProcess 
 from types import MethodType
+import sys
 
 def set_testing_val (self, P, N, M):
     self.D = P
@@ -19,7 +20,7 @@ def set_testing_val (self, P, N, M):
     GaussianProcess.set_testing_val = MethodType(set_testing_val, None, GaussianProcess)
     print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
     print '-----------------------------'
-
+    
     for size in xrange(np.int(1e3), np.int(1e5), np.int(1e4)):
         N = size
         M = 250
@@ -39,7 +40,7 @@ def set_testing_val (self, P, N, M):
     	
         #GPU predict
         start = time.time()
-    	[mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, precision = np.float64, threshold = 1e4)
+        [mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, precision = np.float64, threshold = 1e4)
     	end =time.time()
     	gputime = end - start
         print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (N, cputime, gputime, cputime/gputime),

From d95dd9f3ec6b563d65c8a8d1c4b51148beb41c41 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 11:41:48 +0100
Subject: [PATCH 099/117] refactoring tests

---
 gp_emulator/gpu/CMakeLists.txt           |   2 +-
 gp_emulator/gpu/gpu_predict.h            |   5 -
 gp_emulator/gpu/kernel_cdist.cu          |   2 +-
 gp_emulator/gpu/predictcpp.cu            |   4 +-
 gp_emulator/gpu/tests/CMakeLists.txt     |   3 +-
 gp_emulator/gpu/tests/compare_result.cu  |  21 ++--
 gp_emulator/gpu/tests/gpu_predict_test.h |  48 ++++---
 gp_emulator/gpu/tests/readTestData.cu    |   2 +-
 gp_emulator/gpu/tests/testCdist.cu       |   3 +-
 gp_emulator/gpu/tests/testCublasgemm.cu  |   4 +-
 gp_emulator/gpu/tests/testPredict.cu     |  64 ++++------
 gp_emulator/gpu/tests/unit_test.cu       | 152 +++++++----------------
 12 files changed, 121 insertions(+), 189 deletions(-)

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 06383a0..90f9664 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -13,7 +13,7 @@ include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
 cuda_add_library(_gpu_predict  _gpu_predict.cpp 
-                 predict.cu 
+    #                 predict.cu 
                  predictcpp.cu
                  computeTranspose.cu 
                  kernel_vectorTimesMatrix.cu 
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 15cac77..8a4a259 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -67,9 +67,6 @@ void gpu_crossMinus(const real *v1, const real *v2, real *mat_res, const int v1_
 }
 #endif
 
-
-
-
 class gpuPredict
 {
   real *c_result, *c_error, *c_deriv;
@@ -84,8 +81,6 @@ class gpuPredict
   real *d_dist_matrix; real *d_dist_matrix_T;
   cublasHandle_t handle;
 
-
-
   public:
     gpuPredict(real *ctheta_exp, 
       real *ctheta_exp_sqrt,
diff --git a/gp_emulator/gpu/kernel_cdist.cu b/gp_emulator/gpu/kernel_cdist.cu
index becef22..f98a76b 100644
--- a/gp_emulator/gpu/kernel_cdist.cu
+++ b/gp_emulator/gpu/kernel_cdist.cu
@@ -27,7 +27,7 @@ void gpu_cdist(const real *input1, const real *input2, real *output, const int n
 {
     if( nrow2 < MIN_NPREDICT )
     {
-        printf("gpu_cdist: nrow2(Npredict) < %d\n", MIN_NPREDICT);
+        printf("gpu_cdist: %d [nrow2(Npredict)] < %d\n",nrow2, MIN_NPREDICT);
         exit(EXIT_FAILURE);
     }
 
diff --git a/gp_emulator/gpu/predictcpp.cu b/gp_emulator/gpu/predictcpp.cu
index ca4defb..48a4f52 100644
--- a/gp_emulator/gpu/predictcpp.cu
+++ b/gp_emulator/gpu/predictcpp.cu
@@ -34,6 +34,8 @@ void gpuPredict::init_gpu(void)
     cublasCheckErrors(cublasSetMatrix( 
                 Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
 
+
+
 }
 
 /*********************************//*
@@ -169,7 +171,6 @@ void gpuPredict::free_gpu(void)
      cudaFree(d_theta_exp_sqrt);
 }
 
-
 void gpuPredict::predict( void )
 {
      gpuPredict::init_gpu();
@@ -185,4 +186,3 @@ void gpuPredict::predict( void )
 
 
 
-
diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index 12722ea..357f7cd 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -16,7 +16,8 @@ cuda_add_executable(gpu_predict_test unit_test.cu
                     testPredict.cu      
                     testVecTimesMat.cu
                     testInitArray.cu
-                    ../predict.cu 
+                    #                    ../predict.cu
+                    ../predictcpp.cu
                     ../computeTranspose.cu 
                     ../kernel_vectorTimesMatrix.cu
                     ../kernel_init_array.cu
diff --git a/gp_emulator/gpu/tests/compare_result.cu b/gp_emulator/gpu/tests/compare_result.cu
index aeb8a1d..cc312f6 100644
--- a/gp_emulator/gpu/tests/compare_result.cu
+++ b/gp_emulator/gpu/tests/compare_result.cu
@@ -1,24 +1,25 @@
 #include "gpu_predict_test.h"
 //#define max(a,b) (((a)>(b))?(a):(b))
 
-void compare_result( const real *test_val, const real *origin_val, const int len, const real epsilon_average, const real epsilon_max, char var_name[])
+void compare_result( const real *test_val, const real *origin_val, const int len, 
+        const real epsilon_average, const real epsilon_max, char var_name[])
 {
     int i;
-    real e = 0.0 ;
-    real error = 0.0;
+    real error = 0.0 ;
+    real error_all = 0.0;
     real error_max = 0.0;
 
     for( i = 0; i < len; ++i )
     {
-        e = abs( test_val[i] - origin_val[i] ) / abs( origin_val[i] );
-        error += e;
-        error_max = max( error_max, e );
+        error = abs( test_val[i] - origin_val[i] ) / abs( origin_val[i] );
+        error_all += error;
+        error_max = max( error_max, error );
     } 
-    error = error / len;
+    error_all = error_all / len;
 
-    if(error > epsilon_average || error_max > epsilon_max)
-        printf("        [%s] Average error = %.1e, Max error = %.1e\n",var_name, error, error_max);
-    CU_ASSERT(error < epsilon_average);
+    if(error_all > epsilon_average || error_max > epsilon_max)
+        printf("        [%s] Average error = %.1e, Max error = %.1e\n",var_name, error_all, error_max);
+    CU_ASSERT(error_all < epsilon_average);
     CU_ASSERT(error_max < epsilon_max);
 
 }
diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index 415ffa1..c14cb6c 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -4,14 +4,16 @@
 #include "CUnit/Basic.h"
 #include <cuda.h>
 
-static real *expX, *expXsqrt, *inputs, *testing, *invQ, *invQt;
-static real *cdist_a, *cdist_expa;
-static real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
-static real *var_test1;
+extern real *expX, *expXsqrt, *in_train, *in_predict, *invQ, *invQt;
+extern real *cdist_a, *cdist_expa;
+extern real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
+extern real *error_test1;
 
-static real *mu, *var, *deriv;
+extern real *result_t, *error_t, *deriv_t;
+
+extern int Npredict_t, Ntrain_t, Ninputs_t;
+extern int theta_size_t;
 
-static  int M, N, D;
 #ifdef DOUBLE__PRECISION
     #define EPSILON_AVG 1e-6
     #define EPSILON_MAX 1e-2
@@ -21,15 +23,29 @@ static  int M, N, D;
 #endif
 
 
-real *readTestData(char *file_name, int M, int N, int D, int size);
-void compare_result( const real *test_val, const real *origin_val, const int len, const real epsilon_average, const real epsilon_max, char var_name[]);
-
-
+real *readTestData(char *file_name, int size);
+void compare_result(const real *test_val, const real *origin_val, 
+        const int len, const real epsilon_average, 
+        const real epsilon_max, char var_name[]);
 void testInitArray(void);
 
-void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols);
-void testCdist(const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols);
-void testMatrixExp(const real *mat, const real *res, const real alpha,const real beta,const int size);
-void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols);
-void testPredict(const real *expX, const real *inputs, const real *invQt, const real *invQ, const real *testing, const real *mu, const real *var, const real *deriv, int M, int N, int D);
-
+void testVecTimesMat(const real *c_vec,
+        const real *c_mat, const real *c_res,
+        const int vec_len, const int mat_nrows, 
+        const int mat_ncols);
+void testCdist(const real *in1, const real *in2, 
+        const real *res, const int in1_nrows, 
+        const int in2_nrows, const int in_ncols);
+void testMatrixExp(const real *mat, 
+        const real *res, const real alpha,
+        const real beta,const int size);
+void testCublasgemm(const real *c_mat1, 
+        const real *c_mat2, const real *c_res, 
+        const int mat1_nrows, const int mat1_ncols, 
+        const int mat2_nrows, const int mat2_ncols);
+/*
+void testPredict(real *expX, real *expXsqrt, 
+        real *invQt, real *invQ, real *in_predict, real *in_train, 
+        int Npredict_t, int  Ntrain_t, int Ninputs_t, int theta_size_t, 
+        real *result_t, real *error_t, real *deriv_t);*/
+void testPredict(void);
diff --git a/gp_emulator/gpu/tests/readTestData.cu b/gp_emulator/gpu/tests/readTestData.cu
index 97505bc..6f1938f 100644
--- a/gp_emulator/gpu/tests/readTestData.cu
+++ b/gp_emulator/gpu/tests/readTestData.cu
@@ -2,7 +2,7 @@
 #include <string.h>
 
 
-real *readTestData(char *file_name, int M, int N, int D, int size)
+real *readTestData(char *file_name, const int size)
 {
     int i;
     char path[400];
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index 9e0de07..e450ca4 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -2,7 +2,8 @@
 #define IDX2D(i,j,ld) (((j)*(ld))+(i))
 
 
-void testCdist( const real *in1,const real *in2, const real *res, const int in1_nrows, const int in2_nrows, const int in_ncols )
+void testCdist( const real *in1,const real *in2, const real *res, 
+        const int in1_nrows, const int in2_nrows, const int in_ncols )
 {
     int i, error;
     real *in1_T, *in2_T, *gpu_res;
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index fe37fb7..0329f3c 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -1,6 +1,8 @@
 #include "gpu_predict_test.h"
 
-void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, const int mat2_ncols)
+void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, 
+        const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, 
+        const int mat2_ncols)
 {
     cublasHandle_t handle;
     cublasCreate(&handle);
diff --git a/gp_emulator/gpu/tests/testPredict.cu b/gp_emulator/gpu/tests/testPredict.cu
index 07c9f68..5717adb 100644
--- a/gp_emulator/gpu/tests/testPredict.cu
+++ b/gp_emulator/gpu/tests/testPredict.cu
@@ -1,57 +1,39 @@
 #include "gpu_predict_test.h"
 #include <time.h>
 
-void testPredict(const real *expX, const real *inputs, const real *invQt, const real *invQ, const real *testing, 
-        const real *mu, const real *var, const real *deriv, int M, int N, int D)
+void testPredict(void)
 {
+    real *gpu_result, *gpu_error, *gpu_deriv;
+    gpu_result = (real *)malloc(sizeof(real) * Npredict_t);
+    gpu_error = (real *)malloc(sizeof(real) * Npredict_t);
+    gpu_deriv = (real *)malloc(sizeof(real) * Npredict_t * Ninputs_t);
 
-    clock_t begin1, begin2, end1, end2;
+    real *invQ_T, *train_T, *predict_T;
+    invQ_T = (real *)malloc(sizeof(real) * Ntrain_t * Ntrain_t);
+    train_T = (real *)malloc(sizeof(real) * Npredict_t * Ninputs_t);
+    predict_T = (real *)malloc(sizeof(real) * Npredict_t * Ninputs_t);
 
-    real *gpu_mu, *gpu_var, *gpu_deriv;
-    gpu_mu = (real *)malloc(sizeof(real) * N);
-    gpu_var = (real *)malloc(sizeof(real) * N);
-    gpu_deriv = (real *)malloc(sizeof(real) * N * D);
-
-
-    real *invQ_T, *inputs_T, *testing_T;
-    invQ_T = (real *)malloc(sizeof(real) * M * M);
-    inputs_T = (real *)malloc(sizeof(real) * N * D);
-    testing_T = (real *)malloc(sizeof(real) * N * D);
-   
-    
-
-    begin1 = clock();
-    
-    invQ_T = computeTranspose( invQ, M, M );
-    inputs_T = computeTranspose( inputs, D, M );
-    testing_T = computeTranspose( testing, D, N );
-
-    begin2 = clock();
-    predict( expX, inputs_T, invQt, invQ_T, testing_T, gpu_mu, gpu_var, gpu_deriv, N, M, D, D+2);
-    end2 = clock();
-
-    gpu_deriv = computeTranspose( gpu_deriv, N, D);
     
-    end1 = clock();
+    invQ_T = computeTranspose( invQ, Ntrain_t, Ntrain_t );
+    train_T = computeTranspose( in_train, Ninputs_t, Ntrain_t );
+    predict_T = computeTranspose( in_predict, Ninputs_t, Npredict_t );
 
+    gpuPredict gpu_predict(expX, expXsqrt, invQt, invQ_T, predict_T, train_T,
+            gpu_result, gpu_error, gpu_deriv, Npredict_t, Ntrain_t, Ninputs_t, theta_size_t);
+    gpu_predict.predict();
 
-//    printf("\n");
-//    printf("      -total time including matrix transposing = %f (sec)\n", (double)(end1-begin1)/CLOCKS_PER_SEC);
-//    printf("      -pure predict function = %f (sec)\n", (double)(end2-begin2)/CLOCKS_PER_SEC);
+    gpu_deriv = computeTranspose( gpu_deriv, Npredict_t, Ninputs_t);
     
-    compare_result( gpu_mu, mu, N, EPSILON_AVG, EPSILON_MAX, "mu");
-    compare_result( gpu_var, var, N, EPSILON_AVG, EPSILON_MAX,  "var");
-    compare_result( gpu_deriv, deriv, N * D, EPSILON_AVG, EPSILON_MAX, "deriv");
+    compare_result( gpu_result, result_t, Npredict_t, EPSILON_AVG, EPSILON_MAX, "result");
+    compare_result( gpu_error, error_t, Npredict_t, EPSILON_AVG, EPSILON_MAX,  "error");
+    compare_result( gpu_deriv, deriv_t, Npredict_t * Ninputs_t, EPSILON_AVG, EPSILON_MAX, "deriv");
     
     free(invQ_T);
-    free(inputs_T);
-    free(testing_T);
-    cudaFree(gpu_mu);
-    cudaFree(gpu_var);
+    free(train_T);
+    free(predict_T);
+    cudaFree(gpu_result);
+    cudaFree(gpu_error);
     cudaFree(gpu_deriv);
-
-    
-
 }
 
 
diff --git a/gp_emulator/gpu/tests/unit_test.cu b/gp_emulator/gpu/tests/unit_test.cu
index f0dab41..c2e4173 100644
--- a/gp_emulator/gpu/tests/unit_test.cu
+++ b/gp_emulator/gpu/tests/unit_test.cu
@@ -3,119 +3,85 @@
 #include "gpu_predict_test.h"
 #include "cuda.h"
 
-/*
-static real *expXsqrt, *inputs, *testing;
-static real *cdist_a;
-static real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
- 
-
-static  int M, N, D;
-
-*/
-static int init_suite1(void)
+real *expX, *expXsqrt, *in_train, *in_predict, *invQ, *invQt;
+real *cdist_a, *cdist_expa;
+real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
+real *error_test1;
+real *result_t, *error_t, *deriv_t;
+int Npredict_t, Ntrain_t, Ninputs_t;
+int theta_size_t;
+
+int init_suite1(void)
 {
-    invQ            =  readTestData( "invQ.bin", M, N, D, M * M);
-    invQt           =  readTestData( "invQt.bin", M, N, D, M );
+    invQ            =  readTestData( "invQ.bin", Ntrain_t * Ntrain_t);
+    invQt           =  readTestData( "invQt.bin", Ntrain_t );
     
-    expX            =  readTestData( "expX.bin", M, N, D, D+2);
-    expXsqrt        =  readTestData( "expXsqrt.bin", M, N, D, D );
-    inputs          =  readTestData( "inputs.bin", M, N, D, M * D );
-    testing         =  readTestData( "testing.bin", M, N, D, N * D );
-    cdist_test_var1 =  readTestData( "cdist_test_var1.bin", M, N, D, M * D);
-    cdist_test_var2 =  readTestData( "cdist_test_var2.bin", M, N, D, N * D);
-    cdist_a         =  readTestData( "cdist_a.bin", M, N, D, M * N);
-    cdist_expa      =  readTestData( "cdist_expa.bin", M, N, D, M * N ); 
+    expX            =  readTestData( "expX.bin", Ninputs_t+2);
+    expXsqrt        =  readTestData( "expXsqrt.bin", Ninputs_t );
+    in_train           =  readTestData( "in_train.bin", Ntrain_t * Ninputs_t );
+    in_predict         =  readTestData( "in_predict.bin", Npredict_t * Ninputs_t );
+    cdist_test_var1 =  readTestData( "cdist_test_var1.bin", Ntrain_t * Ninputs_t);
+    cdist_test_var2 =  readTestData( "cdist_test_var2.bin", Npredict_t * Ninputs_t);
+    cdist_a         =  readTestData( "cdist_a.bin", Ntrain_t * Npredict_t);
+    cdist_expa      =  readTestData( "cdist_expa.bin", Ntrain_t * Npredict_t ); 
     
-    var_test1       =  readTestData( "var_test1.bin", M, N, D, M * N );
-    mu              =  readTestData( "mu.bin", M, N, D, M * N);
-    var             =  readTestData( "var.bin", M, N, D, N);
-    deriv           =  readTestData( "deriv.bin", M, N, D, M * N );
+    error_test1     =  readTestData( "error_test1.bin", Ntrain_t * Npredict_t );
+    result_t          =  readTestData( "result.bin", Ntrain_t * Npredict_t);
+    error_t           =  readTestData( "error.bin", Npredict_t);
+    deriv_t           =  readTestData( "deriv.bin", Ntrain_t * Npredict_t );
     
     return 0;
-    
 }
 
-
-
-
-
-
 int clean_suite(void)
 {
     free(expXsqrt);
-    free(inputs);
-    free(testing);
+    free(in_train);
+    free(in_predict);
     free(cdist_a);
     free(cdist_test_var1);
-    free(cdist_test_var2);
+    free(cdist_test_var2);// have all variable been fully cleaned??
     return 0;
 }
 
-static void tests_VecTimesMat(void)
+void tests_VecTimesMat(void)
 {
-    testVecTimesMat(expXsqrt, inputs, cdist_test_var1, D, M, D );
-    testVecTimesMat(expXsqrt, testing, cdist_test_var2, D, N, D );
+    testVecTimesMat(expXsqrt, in_train, cdist_test_var1, Ninputs_t, Ntrain_t, Ninputs_t );
+    testVecTimesMat(expXsqrt, in_predict, cdist_test_var2, Ninputs_t, Npredict_t, Ninputs_t );
 }
 
 void tests_cdist(void)
 {
-    testCdist(cdist_test_var1,cdist_test_var2, cdist_a, M, N, D);
+    testCdist(cdist_test_var1,cdist_test_var2, cdist_a, Ntrain_t, Npredict_t, Ninputs_t);
 }
 
 
 void tests_matrixExp(void)
 {
-    testMatrixExp( cdist_a, cdist_expa, -0.5, expX[D], M * N );
+    testMatrixExp( cdist_a, cdist_expa, -0.5, expX[Ninputs_t], Ntrain_t * Npredict_t );
 }
 
 void tests_cublasgemm(void)
 {
-   testCublasgemm(invQ, cdist_expa, var_test1, M, M, M, N);
+   testCublasgemm(invQ, cdist_expa, error_test1, Ntrain_t, Ntrain_t, Ntrain_t, Npredict_t);
 }
-
-
-
-void tests_predict(void)
-{
-   testPredict(expX, inputs, invQt, invQ, testing, mu, var, deriv, M, N, D);
-}
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
 
 
 int main(int argc, char *argv[])
 {
-   
     if( argc != 4)
     {
-        printf("ERROR: number of arguments is wrong (M, N, D)\n");
+        printf("ERROR: number of arguments is wrong (Ntrain_t, Npredict_t, Ninputs_t)\n");
     }
     
-    M = atoi(argv[1]);
-    N = atoi(argv[2]);
-    D = atoi(argv[3]);
+    Ntrain_t = atoi(argv[1]);
+    Npredict_t = atoi(argv[2]);
+    Ninputs_t = atoi(argv[3]);
+    theta_size_t = Ninputs_t + 2;
+    printf("===============================\n");
+    printf("Testing with problem size:\n( npredict = %d, ntrain = %d, ninputs = %d )\n", Npredict_t, Ntrain_t, Ninputs_t);
+    printf("===============================");
 
-    printf("%d,%d,%d\n", M, N, D);
     CU_pSuite pSuite = NULL;
 
    /* initialize the CUnit test registry */
@@ -123,7 +89,7 @@ int main(int argc, char *argv[])
       return CU_get_error();
 
    /* add a suite to the registry */
-   pSuite = CU_add_suite("Testing data set (3e4, 250, 10)", init_suite1, clean_suite);
+   pSuite = CU_add_suite("Unit Tests", init_suite1, clean_suite);
    
    if (NULL == pSuite) {
       CU_cleanup_registry();
@@ -137,7 +103,7 @@ int main(int argc, char *argv[])
        (NULL == CU_add_test(pSuite, "test of gpu_cdist", tests_cdist))||
        (NULL == CU_add_test(pSuite, "test of gpu_MatrixExp", tests_matrixExp))||
        (NULL == CU_add_test(pSuite, "test of cublasgemm", tests_cublasgemm))||
-       (NULL == CU_add_test(pSuite, "test of gpu_predict", tests_predict))
+       (NULL == CU_add_test(pSuite, "test of gpu_predict", testPredict))
        )
    {
       CU_cleanup_registry();
@@ -148,40 +114,8 @@ int main(int argc, char *argv[])
    CU_basic_set_mode(CU_BRM_VERBOSE);
    CU_basic_run_tests();
    CU_cleanup_registry();
+   printf("\n");
    return CU_get_error();
-
-
- 
-  /* 
-   
-   pSuite = CU_add_suite("Testing data set (100, 250, 10)", init_suite2, clean_suite);
-   
-   if (NULL == pSuite) {
-      CU_cleanup_registry();
-      return CU_get_error();
-   }
-
-   // add the tests to the suite 
-   if ((NULL == CU_add_test(pSuite, "test of gpu_vectorTimesMatrix", tests_VecTimesMat)) ||
-       (NULL == CU_add_test(pSuite, "test of gpu_cdist", tests_cdist))||
-       (NULL == CU_add_test(pSuite, "test of gpu_MatrixExp", tests_matrixExp))||
-       (NULL == CU_add_test(pSuite, "test of cublasgemm", tests_cublasgemm))||
-       (NULL == CU_add_test(pSuite, "test of gpu_predict", tests_predict))
-     )
-   {
-      CU_cleanup_registry();
-      return CU_get_error();
-   }
-
-
-   // Run all tests using the CUnit Basic interface 
-   CU_basic_set_mode(CU_BRM_VERBOSE);
-   CU_basic_run_tests();
-   CU_cleanup_registry();
-   return CU_get_error();
-   */
- 
-
 }
 
 

From a7fa427668e8d624ad878c748390a66dd07c0c1b Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 11:47:19 +0100
Subject: [PATCH 100/117] cpp -> cu

---
 gp_emulator/gpu/CMakeLists.txt       |   3 +-
 gp_emulator/gpu/predict.cu           | 148 +++++++++------------
 gp_emulator/gpu/predictcpp.cu        | 188 ---------------------------
 gp_emulator/gpu/tests/CMakeLists.txt |   3 +-
 tests/unit_tests.py                  |  12 +-
 5 files changed, 68 insertions(+), 286 deletions(-)
 delete mode 100644 gp_emulator/gpu/predictcpp.cu

diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 90f9664..dade973 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -13,8 +13,7 @@ include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
 set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
 
 cuda_add_library(_gpu_predict  _gpu_predict.cpp 
-    #                 predict.cu 
-                 predictcpp.cu
+                 predict.cu
                  computeTranspose.cu 
                  kernel_vectorTimesMatrix.cu 
                  kernel_init_array.cu
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index dafe9a2..48a4f52 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -1,3 +1,4 @@
+
 /*********************************************//** 
  * CUDA implementation of derived from GaussianProcess
  * in python code. 
@@ -8,6 +9,35 @@
 #include <stdlib.h>
 #define debug
 
+
+void gpuPredict::init_gpu(void)
+{
+    cublasCreate(&handle);
+    cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
+    cudaMalloc( (void **)&d_theta_exp_sqrt, sizeof(real) * theta_size );
+    cudaMalloc( (void **)&d_invQt, sizeof(real) * Ntrain);
+    cublasCheckErrors(cublasSetVector( 
+                theta_size, sizeof(real), c_theta_exp_sqrt, 1, d_theta_exp_sqrt, 1 ));
+    cublasCheckErrors(cublasSetVector( 
+                theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
+    cublasCheckErrors(cublasSetVector( 
+                Ntrain, sizeof(real), c_invQt, 1, d_invQt, 1));
+
+    //allocate and copy matrix on device
+    cudaMalloc( (void **)&d_train, sizeof(real) * Ntrain * Ninputs );
+    cudaMalloc( (void **)&d_invQ, sizeof(real) * Ntrain * Ntrain );
+    cudaMalloc( (void **)&d_predict, sizeof(real) * Npredict * Ninputs );
+    cublasCheckErrors(cublasSetMatrix( 
+                Ntrain, Ninputs, sizeof(real), c_train, Ntrain, d_train, Ntrain ));
+    cublasCheckErrors(cublasSetMatrix( 
+                Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
+    cublasCheckErrors(cublasSetMatrix( 
+                Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
+
+
+
+}
+
 /*********************************//*
  * Euclidian distance calculation
  * 1) res_mv1 = theta_exp_sqrt_{,Ninputs} * train
@@ -16,12 +46,9 @@
  * 4) dist_matrix = -0.5 * exp(expX_{Ninputs})
  * Notice: distance is equivalent to distance^T (a^T) in python
  ********************************/ 
-real *compute_distance(const real *d_predict, const real *d_train, 
-                      const real *c_theta_exp,const real *d_theta_exp, 
-                      const real *d_theta_exp_sqrt,
-                      const int Npredict, const int Ntrain, const int Ninputs)
+void gpuPredict::compute_distance(void)
 {
-    real *d_res_mv1, *d_res_mv2, *d_dist_matrix;
+    real *d_res_mv1, *d_res_mv2;
     cudaMalloc((void **)&d_res_mv1, sizeof(real) * Ntrain * Ninputs);
     cudaMalloc((void **)&d_res_mv2, sizeof(real) * Npredict * Ninputs);
     cudaMalloc((void **)&d_dist_matrix, sizeof(real) * Ntrain * Npredict);
@@ -34,16 +61,13 @@ real *compute_distance(const real *d_predict, const real *d_train,
 
     cudaFree(d_res_mv1);
     cudaFree(d_res_mv2);
-    return(d_dist_matrix);
 }
 
-
 /*********************************//*
  * compute result:
  * c_result = dist_matrix * invQt (dot product)
  ********************************/
-void compute_result(cublasHandle_t handle, real *c_result, const real *d_dist_matrix, const real *d_invQt,
-                const int Npredict, const int Ntrain, const int Ninputs)
+void gpuPredict::compute_result(void)
 {
     real *d_result;
     cudaMalloc((void **)&d_result, sizeof(real) * Npredict);
@@ -55,7 +79,8 @@ void compute_result(cublasHandle_t handle, real *c_result, const real *d_dist_ma
     cudaFree(d_result);
 }
 
-real *gpu_transpose(cublasHandle_t handle, real *d_matrix, const int nrow, const int ncol)
+
+real * gpuPredict::gpu_transpose(real *d_matrix, const int nrow, const int ncol)
 {
     real *d_matrix_T;
     real alpha = 1.f;
@@ -66,16 +91,13 @@ real *gpu_transpose(cublasHandle_t handle, real *d_matrix, const int nrow, const
                 d_matrix, nrow, d_matrix_T, nrow));
     return( d_matrix_T );
 }
-  
+
 /*********************************
  * compute error:
  * c_error = b - rowsum(a * dot(invQ, d_dist_matrix_T))
  * arguments d_invQ, d_dist_matrix have been freed.
  ********************************/
-void compute_error(cublasHandle_t handle, real *c_error,
-        real *d_dist_matrix,const  real *d_dist_matrix_T, 
-        real *d_invQ, const real *c_theta_exp,
-        const int Npredict, const int Ntrain, const int Ninputs)
+void gpuPredict::compute_error()
 {
     real alpha = 1.f;
     real beta = 0.f;
@@ -88,7 +110,8 @@ void compute_error(cublasHandle_t handle, real *c_error,
                 Ntrain, Npredict, Ntrain, 
                 &alpha, d_invQ, Ntrain, 
                 d_dist_matrix, Npredict, 
-                &beta, d_res_dot, Ntrain));    // dot(invQ, d_dist_matrix_T)
+                &beta, d_res_dot, Ntrain));  // dot(invQ, d_dist_matrix_T)
+    d_dist_matrix_T = gpuPredict::gpu_transpose(d_dist_matrix, Ntrain, Npredict);
     gpu_elementwiseMult(d_dist_matrix_T, d_res_dot, Ntrain * Npredict);
     d_error = gpu_rowSum(d_res_dot, Ntrain, Npredict);
     gpu_scalarMinusVec(d_error, c_theta_exp[Ninputs], Npredict );
@@ -101,14 +124,13 @@ void compute_error(cublasHandle_t handle, real *c_error,
     cudaFree(d_res_dot);
 }
 
+
+
 /*********************************//*
  * compute deriv:
  *  
  ********************************/
-void compute_deriv( cublasHandle_t handle, real *c_deriv, const real *c_theta_exp, 
-        real *d_predict, real *d_train,
-        const real *d_invQt, const real *d_dist_matrix_T,
-        const int Npredict, const int Ntrain, const int Ninputs)
+void gpuPredict::compute_deriv( void )
 {
     int i;
     real alpha;
@@ -138,79 +160,29 @@ void compute_deriv( cublasHandle_t handle, real *c_deriv, const real *c_theta_ex
      cudaFree(d_aa);
 }
 
+void gpuPredict::free_gpu(void)
+{
+     cublasDestroy(handle);
+     cudaFree(d_invQt);
+     cudaFree(d_dist_matrix_T);
+     cudaFree(d_train);
+     cudaFree(d_theta_exp);
+     cudaFree(d_predict);
+     cudaFree(d_theta_exp_sqrt);
+}
 
-
-
- 
-/*********************************************//**
- * predict function:
- * CUDA version of the corresponding python code
- * GaussianPredict::predict
- *********************************************/
-extern "C"{
-void predict(const real *c_theta_exp, const real *c_train,const real *c_invQt,
-        const real *c_invQ, const real *c_predict,  
-        real *c_result, real *c_error, real *c_deriv,
-        const int Npredict,const int Ntrain, const int  Ninputs, const int theta_size)
+void gpuPredict::predict( void )
 {
-    int i;
-    cublasHandle_t handle;
-    cublasCreate(&handle);
+     gpuPredict::init_gpu();
+     gpuPredict::compute_distance();
+     gpuPredict::compute_result();
+     gpuPredict::compute_error();
+     gpuPredict::compute_deriv();
+     gpuPredict::free_gpu();
+}
 
-    real *c_theta_exp_sqrt;
-    c_theta_exp_sqrt = (real *)malloc( sizeof(real) * theta_size );
-    for( i=0; i < theta_size; i++ )
-    {
-        c_theta_exp_sqrt[i] = sqrt( c_theta_exp[i] );
-    }
 
 
-    //define device vector and matrices
-    real *d_train, *d_theta_exp, *d_theta_exp_sqrt, *d_invQt, *d_invQ, *d_predict;
 
-    //allocate and copy vector on device 
-    cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
-    cudaMalloc( (void **)&d_theta_exp_sqrt, sizeof(real) * theta_size );
-    cudaMalloc( (void **)&d_invQt, sizeof(real) * Ntrain);
-    cublasCheckErrors(cublasSetVector( 
-                theta_size, sizeof(real), c_theta_exp_sqrt, 1, d_theta_exp_sqrt, 1 ));
-    cublasCheckErrors(cublasSetVector( 
-                theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
-    cublasCheckErrors(cublasSetVector( 
-                Ntrain, sizeof(real), c_invQt, 1, d_invQt, 1));
 
-    //allocate and copy matrix on device
-    cudaMalloc( (void **)&d_train, sizeof(real) * Ntrain * Ninputs );
-    cudaMalloc( (void **)&d_invQ, sizeof(real) * Ntrain * Ntrain );
-    cudaMalloc( (void **)&d_predict, sizeof(real) * Npredict * Ninputs );
-    cublasCheckErrors(cublasSetMatrix( 
-                Ntrain, Ninputs, sizeof(real), c_train, Ntrain, d_train, Ntrain ));
-    cublasCheckErrors(cublasSetMatrix( 
-                Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
-    cublasCheckErrors(cublasSetMatrix( 
-                Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
 
-    real *d_dist_matrix, *d_dist_matrix_T;
-    
-    d_dist_matrix = compute_distance(d_predict, d_train, 
-            c_theta_exp, d_theta_exp, 
-            d_theta_exp_sqrt, 
-            Npredict, Ntrain, Ninputs);
-    compute_result(handle, c_result, 
-            d_dist_matrix, d_invQt, 
-            Npredict, Ntrain, Ninputs);
-    d_dist_matrix_T = gpu_transpose(handle, d_dist_matrix, Ntrain, Npredict);
-    compute_error(handle, c_error, d_dist_matrix, 
-            d_dist_matrix_T, d_invQ, c_theta_exp,
-            Npredict, Ntrain, Ninputs); 
-    compute_deriv( handle, c_deriv, c_theta_exp, 
-            d_predict, d_train, d_invQt, d_dist_matrix_T,
-            Npredict, Ntrain, Ninputs);
-    
-    cudaFree(d_invQt);
-    cudaFree(d_dist_matrix_T);
-    cudaFree(d_train);
-    cudaFree(d_theta_exp);
-    cudaFree(d_predict);
-}
-}
diff --git a/gp_emulator/gpu/predictcpp.cu b/gp_emulator/gpu/predictcpp.cu
deleted file mode 100644
index 48a4f52..0000000
--- a/gp_emulator/gpu/predictcpp.cu
+++ /dev/null
@@ -1,188 +0,0 @@
-
-/*********************************************//** 
- * CUDA implementation of derived from GaussianProcess
- * in python code. 
- * 
- * Sinan Shi 
- *********************************************/
-#include "gpu_predict.h"
-#include <stdlib.h>
-#define debug
-
-
-void gpuPredict::init_gpu(void)
-{
-    cublasCreate(&handle);
-    cudaMalloc( (void **)&d_theta_exp, sizeof(real) * theta_size );
-    cudaMalloc( (void **)&d_theta_exp_sqrt, sizeof(real) * theta_size );
-    cudaMalloc( (void **)&d_invQt, sizeof(real) * Ntrain);
-    cublasCheckErrors(cublasSetVector( 
-                theta_size, sizeof(real), c_theta_exp_sqrt, 1, d_theta_exp_sqrt, 1 ));
-    cublasCheckErrors(cublasSetVector( 
-                theta_size, sizeof(real), c_theta_exp, 1, d_theta_exp, 1 ));
-    cublasCheckErrors(cublasSetVector( 
-                Ntrain, sizeof(real), c_invQt, 1, d_invQt, 1));
-
-    //allocate and copy matrix on device
-    cudaMalloc( (void **)&d_train, sizeof(real) * Ntrain * Ninputs );
-    cudaMalloc( (void **)&d_invQ, sizeof(real) * Ntrain * Ntrain );
-    cudaMalloc( (void **)&d_predict, sizeof(real) * Npredict * Ninputs );
-    cublasCheckErrors(cublasSetMatrix( 
-                Ntrain, Ninputs, sizeof(real), c_train, Ntrain, d_train, Ntrain ));
-    cublasCheckErrors(cublasSetMatrix( 
-                Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
-    cublasCheckErrors(cublasSetMatrix( 
-                Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
-
-
-
-}
-
-/*********************************//*
- * Euclidian distance calculation
- * 1) res_mv1 = theta_exp_sqrt_{,Ninputs} * train
- * 2) res_mv2 = theta_exp_sqrt_{,Ninputs} * predict
- * 3) dist_matrix = cidist(res_mv1, res_mv2)
- * 4) dist_matrix = -0.5 * exp(expX_{Ninputs})
- * Notice: distance is equivalent to distance^T (a^T) in python
- ********************************/ 
-void gpuPredict::compute_distance(void)
-{
-    real *d_res_mv1, *d_res_mv2;
-    cudaMalloc((void **)&d_res_mv1, sizeof(real) * Ntrain * Ninputs);
-    cudaMalloc((void **)&d_res_mv2, sizeof(real) * Npredict * Ninputs);
-    cudaMalloc((void **)&d_dist_matrix, sizeof(real) * Ntrain * Npredict);
-
-    gpu_vectorTimesMatrix(d_train, d_theta_exp_sqrt, d_res_mv1, Ntrain, Ninputs);
-    gpu_vectorTimesMatrix(d_predict, d_theta_exp_sqrt, d_res_mv2, Npredict, Ninputs);
-    gpu_init_array( d_dist_matrix, 0.0, Npredict * Ntrain );
-    gpu_cdist(d_res_mv1, d_res_mv2, d_dist_matrix, Ntrain, Ninputs, Npredict, Ninputs);
-    gpu_matrixExp(d_dist_matrix, -0.5, c_theta_exp[Ninputs], Ntrain * Npredict);
-
-    cudaFree(d_res_mv1);
-    cudaFree(d_res_mv2);
-}
-
-/*********************************//*
- * compute result:
- * c_result = dist_matrix * invQt (dot product)
- ********************************/
-void gpuPredict::compute_result(void)
-{
-    real *d_result;
-    cudaMalloc((void **)&d_result, sizeof(real) * Npredict);
-    real alpha = 1.f;
-    real beta = 0.f;
-    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_N, Npredict, Ntrain, &alpha, d_dist_matrix, 
-                Npredict, d_invQt, 1, &beta, d_result, 1));
-    cudaMemcpy(c_result, d_result, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
-    cudaFree(d_result);
-}
-
-
-real * gpuPredict::gpu_transpose(real *d_matrix, const int nrow, const int ncol)
-{
-    real *d_matrix_T;
-    real alpha = 1.f;
-    real beta = 0.f;
-    cudaMalloc((void **)&d_matrix_T, sizeof(real) * nrow * ncol );
-    cublasCheckErrors(CUBLAS_GEAM(handle, CUBLAS_OP_T, CUBLAS_OP_N, 
-                nrow, ncol, &alpha, d_matrix, ncol, &beta, 
-                d_matrix, nrow, d_matrix_T, nrow));
-    return( d_matrix_T );
-}
-
-/*********************************
- * compute error:
- * c_error = b - rowsum(a * dot(invQ, d_dist_matrix_T))
- * arguments d_invQ, d_dist_matrix have been freed.
- ********************************/
-void gpuPredict::compute_error()
-{
-    real alpha = 1.f;
-    real beta = 0.f;
-    real *d_res_dot;
-    real *d_error;
-    
-    cudaMalloc((void **)&d_res_dot, sizeof(real) * Ntrain * Npredict);
-    cublasCheckErrors(CUBLAS_GEMM(
-                handle, CUBLAS_OP_N, CUBLAS_OP_T, 
-                Ntrain, Npredict, Ntrain, 
-                &alpha, d_invQ, Ntrain, 
-                d_dist_matrix, Npredict, 
-                &beta, d_res_dot, Ntrain));  // dot(invQ, d_dist_matrix_T)
-    d_dist_matrix_T = gpuPredict::gpu_transpose(d_dist_matrix, Ntrain, Npredict);
-    gpu_elementwiseMult(d_dist_matrix_T, d_res_dot, Ntrain * Npredict);
-    d_error = gpu_rowSum(d_res_dot, Ntrain, Npredict);
-    gpu_scalarMinusVec(d_error, c_theta_exp[Ninputs], Npredict );
- 
-    cudaMemcpy(c_error, d_error, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
-
-    cudaFree(d_dist_matrix);
-    cudaFree(d_invQ);
-    cudaFree(d_error);
-    cudaFree(d_res_dot);
-}
-
-
-
-/*********************************//*
- * compute deriv:
- *  
- ********************************/
-void gpuPredict::compute_deriv( void )
-{
-    int i;
-    real alpha;
-    real beta = 0.f;
- 
-    real *d_deriv, *d_aa;
-    cudaMalloc((void **)&d_deriv, sizeof(real) * Npredict );
-    cudaMalloc((void **)&d_aa, sizeof(real) * Ntrain * Npredict);
-    real *ptr_train, *ptr_predict, *ptr_deriv;
-    ptr_train = d_train;
-    ptr_predict = d_predict;
-    ptr_deriv = c_deriv;
-
-     for( i = 0; i < Ninputs; ++i){
-        gpu_crossMinus(ptr_train, ptr_predict, d_aa, Ntrain, Npredict );
-        ptr_train = ptr_train + Ntrain;
-        ptr_predict = ptr_predict + Npredict;
-        alpha = c_theta_exp[i];
-        gpu_elementwiseMult(d_dist_matrix_T, d_aa, Ntrain * Npredict);
-        cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, Ntrain, Npredict, &alpha, d_aa, Ntrain, d_invQt, 1, &beta, d_deriv,1));
-
-        cudaMemcpy(ptr_deriv, d_deriv, sizeof(real) * Npredict, cudaMemcpyDeviceToHost);
-        ptr_deriv = ptr_deriv + Npredict;
-     }
-
-     cudaFree(d_deriv);
-     cudaFree(d_aa);
-}
-
-void gpuPredict::free_gpu(void)
-{
-     cublasDestroy(handle);
-     cudaFree(d_invQt);
-     cudaFree(d_dist_matrix_T);
-     cudaFree(d_train);
-     cudaFree(d_theta_exp);
-     cudaFree(d_predict);
-     cudaFree(d_theta_exp_sqrt);
-}
-
-void gpuPredict::predict( void )
-{
-     gpuPredict::init_gpu();
-     gpuPredict::compute_distance();
-     gpuPredict::compute_result();
-     gpuPredict::compute_error();
-     gpuPredict::compute_deriv();
-     gpuPredict::free_gpu();
-}
-
-
-
-
-
-
diff --git a/gp_emulator/gpu/tests/CMakeLists.txt b/gp_emulator/gpu/tests/CMakeLists.txt
index 357f7cd..70e9af1 100644
--- a/gp_emulator/gpu/tests/CMakeLists.txt
+++ b/gp_emulator/gpu/tests/CMakeLists.txt
@@ -16,8 +16,7 @@ cuda_add_executable(gpu_predict_test unit_test.cu
                     testPredict.cu      
                     testVecTimesMat.cu
                     testInitArray.cu
-                    #                    ../predict.cu
-                    ../predictcpp.cu
+                    ../predict.cu
                     ../computeTranspose.cu 
                     ../kernel_vectorTimesMatrix.cu
                     ../kernel_init_array.cu
diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index 69a987a..ddbfeb4 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -37,16 +37,16 @@ def get_testing_val (self, testing, do_unc = True):
         
         t_expX.tofile("./data/expX.bin")
         t_expXsqrt.tofile("./data/expXsqrt.bin")
-        t_inputs.tofile("./data/inputs.bin")
-        t_testing.tofile("./data/testing.bin")
+        t_inputs.tofile("./data/in_train.bin")
+        t_testing.tofile("./data/in_predict.bin")
         cdist_test_var_1.tofile("./data/cdist_test_var1.bin")
         cdist_test_var_2.tofile("./data/cdist_test_var2.bin")
         cdist_test_var_3.tofile("./data/cdist_a.bin")
         cdist_test_var_4.tofile("./data/cdist_expa.bin")    
         
         t_invQ.tofile("./data/invQ.bin")
-        mu.tofile("./data/mu.bin")
-        var_test_1.tofile("./data/var_test1.bin")
+        mu.tofile("./data/result.bin")
+        var_test_1.tofile("./data/error_test1.bin")
         self.invQt.tofile("./data/invQt.bin")
 
 
@@ -69,7 +69,7 @@ def get_testing_val (self, testing, do_unc = True):
             aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
             c = a*aa.T
             deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
-        var.tofile("./data/var.bin")
+        var.tofile("./data/error.bin")
         deriv.tofile("./data/deriv.bin")
 
         if do_unc:
@@ -79,7 +79,7 @@ def get_testing_val (self, testing, do_unc = True):
 
 
 if __name__ == '__main__':
-    for size in xrange(int(1e4), int(2e5),int(5e4)):
+    for size in xrange(int(12345), int(1e5),int(6e4)):
         N=size
         M=250
         P=10

From 16924bd03e4bcf576dbf7b24c9832cc845869dcd Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 11:54:13 +0100
Subject: [PATCH 101/117] remove _gpu_predict.c

---
 gp_emulator/gpu/_gpu_predict.c | 162 ---------------------------------
 1 file changed, 162 deletions(-)
 delete mode 100644 gp_emulator/gpu/_gpu_predict.c

diff --git a/gp_emulator/gpu/_gpu_predict.c b/gp_emulator/gpu/_gpu_predict.c
deleted file mode 100644
index 7ecd0fd..0000000
--- a/gp_emulator/gpu/_gpu_predict.c
+++ /dev/null
@@ -1,162 +0,0 @@
-/**********************************//**
- * source code of share library _gpu_predict.so
- * importing inputs from python
- * calling GPU predict function by predict_wrap()
- * python utility functions are based on the following template:
- *  http://wiki.scipy.org/Cookbook/C_Extensions/NumPy_arrays
- **********************************/
-
-#include "gpu_predict.h"
-#include<errno.h>
-
-/*********************************//**
- * Set up the methods table
- *********************************/
-static PyMethodDef gpuMethods[] = 
-{
-    {"predict_wrap", predict_wrap, METH_VARARGS},
-    {NULL, NULL}     /* Sentinel - marks the end of this structure */
-};
-
-/**********************************//**
- * Initialize the C_test functions 
- * Module name must be _C_arraytest in compile and linked 
- **********************************/
-void init_gpu_predict()  
-{
-    (void) Py_InitModule("_gpu_predict", gpuMethods);
-    import_array();  // Must be present for NumPy.  Called first after above line.
-}
-
-
-/**********************************//**
- * Check the dimension and data type 
- * of input python arrays.
- **********************************/
-void checkRealVector(PyArrayObject *vec)  
-{
-    if( sizeof( real ) == sizeof( double ) )
-    {
-        if (vec->descr->type_num != NPY_DOUBLE || vec->nd != 1)  
-        {
-            printf( "In checkRealVector: array must be of type Double vector\n");
-            exit(EXIT_FAILURE);
-        }
-       
-    }
-    if( sizeof( real ) == sizeof( float ) ) 
-    {
-        if (vec->descr->type_num != NPY_FLOAT32 || vec->nd != 1)  
-        {
-            printf("In checkRealVector: array must be of type Float32 vector\n");
-            exit(EXIT_FAILURE);
-        }
-    }
-}
-
-
-
-/**********************************//**
- * Convert data (vector) from PyArrayObject to real
- **********************************/
-real *pyvector_to_Carrayptrs(PyArrayObject *arrayin)  
-{
-    checkRealVector(arrayin);
-    return  (real *) arrayin->data;  /* pointer to arrayin data as real */
-}
-
-
-
-/**********************************//**
- * getPredictDataFromPython
- * Assign multiple input python arrays, which are all in 1D
- * to C array.
- **********************************/
-void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt, real **c_invQ, 
-                              real **c_predict, real **c_train,
-                              real **c_result, real **c_error, real **c_deriv,
-                              int *Npredict, int *Ntrain, int *Ninputs, int *theta_size)
-{
-    PyArrayObject *py_theta_exp, *py_train, *py_invQt, *py_invQ, *py_predict;
-    PyArrayObject *py_result, *py_error, *py_deriv;
-
-    PyArg_ParseTuple ( args, "O!O!O!O!O!O!O!O!iiii",
-        &PyArray_Type, &py_theta_exp,
-        &PyArray_Type, &py_train,
-        &PyArray_Type, &py_invQt,
-        &PyArray_Type, &py_invQ,
-        &PyArray_Type, &py_predict,
-        &PyArray_Type, &py_result,
-        &PyArray_Type, &py_error,
-        &PyArray_Type, &py_deriv,
-        Npredict, Ntrain, Ninputs, theta_size);
-
-    *c_theta_exp = pyvector_to_Carrayptrs( py_theta_exp);
-    *c_train = pyvector_to_Carrayptrs( py_train );
-    *c_invQt = pyvector_to_Carrayptrs( py_invQt );
-    *c_invQ = pyvector_to_Carrayptrs( py_invQ );
-    *c_predict = pyvector_to_Carrayptrs( py_predict );
-    *c_result = pyvector_to_Carrayptrs( py_result );
-    *c_error = pyvector_to_Carrayptrs( py_error );
-    *c_deriv = pyvector_to_Carrayptrs( py_deriv );
-}
-
-
-
-/**********************************//**
- * predict_wrap
- * 1) call getPredictDataFromPython;
- * 2) transpose 2D arrays (data arranged in 1D) to column major;
- * 3) calling GPU predict function;
- **********************************/
-PyObject *predict_wrap ( PyObject *self, PyObject *args )
-{
-    int Npredict, Ntrain, Ninputs, theta_size;
-    real *c_theta_exp;
-    real *c_train, *c_invQt, *c_invQ, *c_predict;
-    real *c_result, *c_error, *c_deriv;
-   
-    getPredictDataFromPython(args, &c_theta_exp, &c_invQt, &c_invQ,
-                             &c_predict, &c_train,
-                             &c_result, &c_error, &c_deriv,
-                             &Npredict, &Ntrain, &Ninputs,  &theta_size);
-
-    //transpose 2D array to column major to cope with cublas
-    real *c_invQ_T, *c_train_T, *c_predict_T;
-    c_invQ_T = computeTranspose( c_invQ, Ntrain, Ntrain );
-    c_train_T = computeTranspose( c_train, Ninputs, Ntrain );
-    c_predict_T = computeTranspose( c_predict, Ninputs, Npredict);
-
-    predict( c_theta_exp, c_train_T, c_invQt, c_invQ_T, c_predict_T, 
-            c_result, c_error, c_deriv,
-            Npredict, Ntrain, Ninputs, theta_size );
-
-    free(c_invQ_T);
-    free(c_train_T);
-    free(c_predict_T);
-   
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

From aa5ce51b5b93bf92fe597c63daebb806e5b95f63 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 12:02:02 +0100
Subject: [PATCH 102/117] setup benchmark and test

---
 README.rst |  3 +++
 setup.py   | 12 +++++++++++-
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 21dd3c5..129530a 100644
--- a/README.rst
+++ b/README.rst
@@ -30,6 +30,9 @@ Install with GPU predict:
     
 Tests (only with predict):
 ----------
+``` python setup.py test
+    python setup.py benchmark
+```
 1. Unit testing ([unit_tests.py](https://github.com/UCL/gp_emulator/blob/master/tests/unit_tests.py)):
    * random inputs generated by python predict
    * operate unit testings of GPU functions. 
diff --git a/setup.py b/setup.py
index f273184..b6ba8aa 100644
--- a/setup.py
+++ b/setup.py
@@ -48,10 +48,20 @@ def run(self):
         run_cmake()
         os.chdir(cwd)
         _build.build.run(self)
+
 class test(_install.install):
     def run(self):
         os.system("python tests/benchmark.py")
 
+class test(_install.install):
+    def run(self):
+        cwd = os.getcwd()
+        testdir = op.join(op.split(__file__)[0], 'tests')
+        os.chdir(testdir)
+        os.system("python unit_tests.py")
+        os.chdir(cwd)
+
+
 
 
 setup(name='gp_emulator',
@@ -61,5 +71,5 @@ def run(self):
       author_email='j.gomez-dans@ucl.ac.uk',
       url='http://bitbucket.org/gomezdansj/gp_emulator',
       packages=['gp_emulator'],
-      cmdclass={'build':build, 'install':install, 'test':test},
+      cmdclass={'build':build, 'install':install, 'test':test, 'test':test},
      )

From 579e78b3e12cf97cf33d6df7e61f9a2bbbbef1a6 Mon Sep 17 00:00:00 2001
From: Sinan Shi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 12:11:45 +0100
Subject: [PATCH 103/117] Update README.rst

---
 README.rst | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/README.rst b/README.rst
index 129530a..bdd96bf 100644
--- a/README.rst
+++ b/README.rst
@@ -21,25 +21,25 @@ Requirements:
 Install with GPU predict:
 ---------
 1. decide precision by modifying  CMakeList.txt 
-   ```add_definition(-DDOUBLE_PRECISION) # double
-   ````
+   `add_definition(-DDOUBLE_PRECISION) # double`
 2. install
-   ```
-   python setup.py install
-   ```
+   `python setup.py install`
     
-Tests (only with predict):
+Tests (for GPU predict):
 ----------
-``` python setup.py test
-    python setup.py benchmark
-```
+
+test: `python setup.py test`
+
+benchmark:`python setup.py benchmark`
+
 1. Unit testing ([unit_tests.py](https://github.com/UCL/gp_emulator/blob/master/tests/unit_tests.py)):
    * random inputs generated by python predict
    * operate unit testings of GPU functions. 
    * compare GPU and python outputs
-2. benchmark ([benchmark.py](https://github.com/UCL/gp_emulator/blob/master/tests/benchmark.py))
+2. benchmark [benchmark.py](https://github.com/UCL/gp_emulator/blob/master/tests/benchmark.py)
    * obtain speedup of GPU predict  
    * random inputs
-3. testing emulator ([testing_emulator](https://github.com/UCL/gp_emulator/blob/master/tests/test_perband_emulator.py))
+3. testing emulator ([testing_emulator.py](https://github.com/UCL/gp_emulator/blob/master/tests/test_perband_emulator.py))
+   * read real data from .zpn file
    * run emulator with and without GPU
    

From b330d1f6286549f8fa9728b9d385839572601950 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 13:05:22 +0100
Subject: [PATCH 104/117] fix setup.py benchmark

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index b6ba8aa..4e053e8 100644
--- a/setup.py
+++ b/setup.py
@@ -49,7 +49,7 @@ def run(self):
         os.chdir(cwd)
         _build.build.run(self)
 
-class test(_install.install):
+class benchmark(_install.install):
     def run(self):
         os.system("python tests/benchmark.py")
 
@@ -71,5 +71,5 @@ def run(self):
       author_email='j.gomez-dans@ucl.ac.uk',
       url='http://bitbucket.org/gomezdansj/gp_emulator',
       packages=['gp_emulator'],
-      cmdclass={'build':build, 'install':install, 'test':test, 'test':test},
+      cmdclass={'build':build, 'install':install, 'test':test, 'benchmark':benchmark},
      )

From a15e8f3f2eaef667bed60940ba7c4a4beae7e29e Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 13:54:22 +0100
Subject: [PATCH 105/117] modifications for legion compilers, error_t has
 already been defined.

---
 gp_emulator/gpu/tests/gpu_predict_test.h | 4 ++--
 gp_emulator/gpu/tests/testCublasgemm.cu  | 6 ++----
 gp_emulator/gpu/tests/testPredict.cu     | 6 +++---
 gp_emulator/gpu/tests/unit_test.cu       | 8 ++++----
 4 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index c14cb6c..a83c8ed 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -9,7 +9,7 @@ extern real *cdist_a, *cdist_expa;
 extern real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
 extern real *error_test1;
 
-extern real *result_t, *error_t, *deriv_t;
+extern real *result_py, *error_py, *deriv_py;
 
 extern int Npredict_t, Ntrain_t, Ninputs_t;
 extern int theta_size_t;
@@ -47,5 +47,5 @@ void testCublasgemm(const real *c_mat1,
 void testPredict(real *expX, real *expXsqrt, 
         real *invQt, real *invQ, real *in_predict, real *in_train, 
         int Npredict_t, int  Ntrain_t, int Ninputs_t, int theta_size_t, 
-        real *result_t, real *error_t, real *deriv_t);*/
+        real *result_py, real *error_t, real *deriv_py);*/
 void testPredict(void);
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index 0329f3c..f04107c 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -8,8 +8,6 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res,
     cublasCreate(&handle);
     
 
-    int i;
-
     real *d_mat1, *d_mat2, *d_res;
     real *c_gpu_res;
     c_gpu_res = (real *)malloc( sizeof(real) * mat2_nrows * mat2_ncols);
@@ -30,13 +28,13 @@ void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res,
 
     real alpha = 1.f;
     real beta = 0.f;
-    cublasCheckErrors( CUBLAS_GEMM( handle, CUBLAS_OP_N, CUBLAS_OP_T, 
+    CUBLAS_GEMM( handle, CUBLAS_OP_N, CUBLAS_OP_T, 
                 mat1_nrows, mat2_ncols,  mat1_ncols,
                 &alpha, 
                 d_mat1, mat1_nrows, 
                 d_mat2, mat2_ncols, 
                 &beta, 
-                d_res, mat1_ncols ));
+                d_res, mat1_ncols );
 
     cudaMemcpy( c_gpu_res, d_res, sizeof(real) * mat1_nrows *  mat2_ncols, cudaMemcpyDeviceToHost);
     c_gpu_res = computeTranspose(c_gpu_res, mat1_nrows, mat2_ncols); 
diff --git a/gp_emulator/gpu/tests/testPredict.cu b/gp_emulator/gpu/tests/testPredict.cu
index 5717adb..d95ebb2 100644
--- a/gp_emulator/gpu/tests/testPredict.cu
+++ b/gp_emulator/gpu/tests/testPredict.cu
@@ -24,9 +24,9 @@ void testPredict(void)
 
     gpu_deriv = computeTranspose( gpu_deriv, Npredict_t, Ninputs_t);
     
-    compare_result( gpu_result, result_t, Npredict_t, EPSILON_AVG, EPSILON_MAX, "result");
-    compare_result( gpu_error, error_t, Npredict_t, EPSILON_AVG, EPSILON_MAX,  "error");
-    compare_result( gpu_deriv, deriv_t, Npredict_t * Ninputs_t, EPSILON_AVG, EPSILON_MAX, "deriv");
+    compare_result( gpu_result, result_py, Npredict_t, EPSILON_AVG, EPSILON_MAX, "result");
+    compare_result( gpu_error, error_py, Npredict_t, EPSILON_AVG, EPSILON_MAX,  "error");
+    compare_result( gpu_deriv, deriv_py, Npredict_t * Ninputs_t, EPSILON_AVG, EPSILON_MAX, "deriv");
     
     free(invQ_T);
     free(train_T);
diff --git a/gp_emulator/gpu/tests/unit_test.cu b/gp_emulator/gpu/tests/unit_test.cu
index c2e4173..07cf933 100644
--- a/gp_emulator/gpu/tests/unit_test.cu
+++ b/gp_emulator/gpu/tests/unit_test.cu
@@ -7,7 +7,7 @@ real *expX, *expXsqrt, *in_train, *in_predict, *invQ, *invQt;
 real *cdist_a, *cdist_expa;
 real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
 real *error_test1;
-real *result_t, *error_t, *deriv_t;
+real *result_py, *error_py, *deriv_py;
 int Npredict_t, Ntrain_t, Ninputs_t;
 int theta_size_t;
 
@@ -26,9 +26,9 @@ int init_suite1(void)
     cdist_expa      =  readTestData( "cdist_expa.bin", Ntrain_t * Npredict_t ); 
     
     error_test1     =  readTestData( "error_test1.bin", Ntrain_t * Npredict_t );
-    result_t          =  readTestData( "result.bin", Ntrain_t * Npredict_t);
-    error_t           =  readTestData( "error.bin", Npredict_t);
-    deriv_t           =  readTestData( "deriv.bin", Ntrain_t * Npredict_t );
+    result_py          =  readTestData( "result.bin", Ntrain_t * Npredict_t);
+    error_py           =  readTestData( "error.bin", Npredict_t);
+    deriv_py           =  readTestData( "deriv.bin", Ntrain_t * Npredict_t );
     
     return 0;
 }

From 787fce0e8797638c92cdd87c65422cff1ee58b23 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 14:19:24 +0100
Subject: [PATCH 106/117] deviceQuery for jenkins testing only

---
 deviceQuery | Bin 0 -> 16312 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100755 deviceQuery

diff --git a/deviceQuery b/deviceQuery
new file mode 100755
index 0000000000000000000000000000000000000000..9b7a4d6ff122005f00cce449db98d6b737b83b31
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From dfaf578affd54626e20984302723649a04409c48 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 14:52:23 +0100
Subject: [PATCH 107/117] traspose: ld_in -> lead_dim_in, etc.

---
 gp_emulator/gpu/computeTranspose.cu | 17 ++++++-----------
 1 file changed, 6 insertions(+), 11 deletions(-)

diff --git a/gp_emulator/gpu/computeTranspose.cu b/gp_emulator/gpu/computeTranspose.cu
index 37f4eb1..1041385 100644
--- a/gp_emulator/gpu/computeTranspose.cu
+++ b/gp_emulator/gpu/computeTranspose.cu
@@ -1,23 +1,18 @@
 #include "gpu_predict.h"
-//ld_in is the leading dimension of input matrix
-//ld_out is the leading dimension of the output matrix
-extern "C"{
-real *computeTranspose(const real *matrix, const  int ld_in, const  int ld_out)
+
+real *computeTranspose(const real *matrix, const  int lead_dim_in, const  int lead_dim_out)
 {
     int x, y;
     real * temp;
-    temp = ( real *)malloc(sizeof(real) * ld_in * ld_out);
-//    for ( i = 0; i < ld_in * ld_out; ++i)
-//        temp[i] = matrix[i];
+    temp = ( real *)malloc(sizeof(real) * lead_dim_in * lead_dim_out);
 
-    for ( y = 0; y < ld_out; ++y )
+    for ( y = 0; y < lead_dim_out; ++y )
     {
-        for ( x = 0; x < ld_in; ++x )
+        for ( x = 0; x < lead_dim_in; ++x )
         {
-            temp[(x * ld_out) + y] = matrix[(y * ld_in) + x];                                                                
+            temp[(x * lead_dim_out) + y] = matrix[(y * lead_dim_in) + x];                                                                
 
         }   
     }
     return(temp);
 }
-}

From 313fa5d80036932fce61ef70f12640df10c1b4ec Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 15:40:38 +0100
Subject: [PATCH 108/117] change variables' name of kernels

---
 CMakeLists.txt                              |  2 +-
 gp_emulator/gpu/CMakeLists.txt              |  1 -
 gp_emulator/gpu/_gpu_predict.cpp            | 16 +++---------
 gp_emulator/gpu/gpu_predict.h               |  9 ++++---
 gp_emulator/gpu/kernel_cdist.cu             |  1 +
 gp_emulator/gpu/kernel_crossMinus.cu        | 19 ++++++++-------
 gp_emulator/gpu/kernel_init_array.cu        | 10 ++++----
 gp_emulator/gpu/kernel_matrixExp.cu         | 10 ++++----
 gp_emulator/gpu/kernel_rowSum.cu            | 21 ++++++++--------
 gp_emulator/gpu/kernel_scalarMinusVec.cu    |  8 +++---
 gp_emulator/gpu/kernel_vectorTimesMatrix.cu | 25 +++++++++----------
 gp_emulator/gpu/predict.cu                  |  6 -----
 tests/benchmark.py                          | 27 ++++++++++-----------
 13 files changed, 70 insertions(+), 85 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 18601e8..6ce70b0 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -6,7 +6,7 @@ subdirs(gp_emulator/gpu)
 set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib)
 set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/tests)
 string(ASCII 27 Esc)
-add_definitions(-DDOUBLE__PRECISION)
+#add_definitions(-DDOUBLE__PRECISION)
 
 set(ColourReset "${Esc}[m")
 set(Red  "${Esc}[1;31m")
diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index dade973..69b912e 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -1,6 +1,5 @@
 cmake_minimum_required (VERSION 2.8)
 
-
 find_package(Numpy REQUIRED)
 find_package(CoherentPython REQUIRED)
 find_package(CUDA QUIET REQUIRED)
diff --git a/gp_emulator/gpu/_gpu_predict.cpp b/gp_emulator/gpu/_gpu_predict.cpp
index a826372..6f5cd39 100644
--- a/gp_emulator/gpu/_gpu_predict.cpp
+++ b/gp_emulator/gpu/_gpu_predict.cpp
@@ -22,7 +22,6 @@ static PyMethodDef gpuMethods[] =
  * Initialize the C_test functions 
  * Module name must be _C_arraytest in compile and linked 
  **********************************/
-
 extern "C"{
 void init_gpu_predict()  
 {
@@ -132,19 +131,15 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
     c_train_T = computeTranspose( c_train, Ninputs, Ntrain );
     c_predict_T = computeTranspose( c_predict, Ninputs, Npredict);
 
-    
+    //get c_theta_exp_sqrt
     real *c_theta_exp_sqrt;
     c_theta_exp_sqrt = (real *)malloc( sizeof(real) * theta_size );
-    for( i=0; i < theta_size; i++ )
+    for( i = 0; i < theta_size; i++ )
     {
         c_theta_exp_sqrt[i] = sqrt( c_theta_exp[i] );
     }
 
-
-    // predict( c_theta_exp, c_train_T, c_invQt, c_invQ_T, c_predict_T, 
-    //         c_result, c_error, c_deriv,
-    //         Npredict, Ntrain, Ninputs, theta_size );
-
+    //predict
     gpuPredict gpu_predict(
         c_theta_exp, c_theta_exp_sqrt,
         c_invQt, c_invQ_T, 
@@ -154,13 +149,10 @@ PyObject *predict_wrap ( PyObject *self, PyObject *args )
     gpu_predict.predict();
 
 
-
-
-
-
     free(c_invQ_T);
     free(c_train_T);
     free(c_predict_T);
+    free(c_theta_exp_sqrt);
    
     Py_INCREF(Py_None);
     return Py_None;
diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 8a4a259..f5d0965 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -11,11 +11,12 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
-//#define DOUBLE__PRECISION
-#define CUDA_BLOCK 2
+
+#define CUDA_BLOCK 5
 #define MIN_NPREDICT 1000 //ensure there will be enough amount of threads to launch. 
 #define MAX_NUM_THREAD 1024 //sm_20
 #define MAX_NUM_BLOCK 65536
+
 #ifdef DOUBLE__PRECISION
   #define real double
 #else
@@ -53,11 +54,11 @@ void getPredictDataFromPython(PyObject *args, real **c_theta_exp, real **c_invQt
 #ifdef __cplusplus
 extern "C"{
 #endif
-void predict(const real *c_theta_exp,const  real *c_inputs,const real *c_invQt, const real *c_invQ, const real *c_testing, real *c_mu, real *c_var, real *c_deriv, const int N, const int M, const int  D, const int theta_size);
 real *computeTranspose(const real *matrix, const  int size_in, const  int size_out);
 void gpu_vectorTimesMatrix(const real *A, const real *v, real *res, int nrows, int ncols);
 void gpu_init_array(real *vec, const int init_val, const int vec_len);
-void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1, const int nrow2, const int ncol2);
+void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1,
+        const int nrow2, const int ncol2);
 void gpu_matrixExp( real *matrix,const real alpha,const real beta, const int size );
 void gpu_elementwiseMult( const real *v1, real *v2, const int size );
 void gpu_scalarMinusVec( real *matrix, const real scalar, const int size );
diff --git a/gp_emulator/gpu/kernel_cdist.cu b/gp_emulator/gpu/kernel_cdist.cu
index f98a76b..c6f9bb2 100644
--- a/gp_emulator/gpu/kernel_cdist.cu
+++ b/gp_emulator/gpu/kernel_cdist.cu
@@ -25,6 +25,7 @@ void kernel_cdist(const real *input1, const real *input2, real *output, const in
 
 void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1, const int nrow2, const int ncol2)
 {
+    printf("cdist  ");
     if( nrow2 < MIN_NPREDICT )
     {
         printf("gpu_cdist: %d [nrow2(Npredict)] < %d\n",nrow2, MIN_NPREDICT);
diff --git a/gp_emulator/gpu/kernel_crossMinus.cu b/gp_emulator/gpu/kernel_crossMinus.cu
index ff8c4a3..2e5ad94 100644
--- a/gp_emulator/gpu/kernel_crossMinus.cu
+++ b/gp_emulator/gpu/kernel_crossMinus.cu
@@ -4,30 +4,31 @@
  * aa_{ix, iy} = inputs_{ix} - testing_{iy}
  *********************************************/
 __global__
-void kernel_crossMinus(const real *v1,const real *v2, real *mat_res, const int v1_len, const int v2_len)
+void kernel_crossMinus(const real *vec1,const real *vec2, real *matrix_result, const int vec1_len, const int vec2_len)
 {
     int ix = blockIdx.x * blockDim.x + threadIdx.x;
     int iy = blockIdx.y * blockDim.y + threadIdx.y;
 
-    if(ix < v1_len && iy < v2_len)
-        mat_res[IDX2D(ix, iy, v1_len)] = v1[ix] - v2[iy];
+    if(ix < vec1_len && iy < vec2_len)
+        matrix_result[IDX2D(ix, iy, vec1_len)] = vec1[ix] - vec2[iy];
 }
 
 
 
-void gpu_crossMinus(const real *v1, const real *v2, real *mat_res, const int v1_len, const int v2_len)
+void gpu_crossMinus(const real *vec1, const real *vec2, real *matrix_result, const int vec1_len, const int vec2_len)
 {
+printf("crossMinus  ");
     dim3 nthread, nblock;
-    if( v2_len < MAX_NUM_THREAD )
-        nthread.y = v2_len;
+    if( vec2_len < MAX_NUM_THREAD )
+        nthread.y = vec2_len;
     else
         nthread.y = MAX_NUM_THREAD;
     
     nthread.x = 1;
-    nblock.x = v1_len;
-    nblock.y = ceil( float(v2_len) / float(nthread.y) );
+    nblock.x = vec1_len;
+    nblock.y = ceil( float(vec2_len) / float(nthread.y) );
 
-    kernel_crossMinus<<< nblock, nthread >>>(v1, v2, mat_res, v1_len, v2_len);
+    kernel_crossMinus<<< nblock, nthread >>>(vec1, vec2, matrix_result, vec1_len, vec2_len);
 }
 
 
diff --git a/gp_emulator/gpu/kernel_init_array.cu b/gp_emulator/gpu/kernel_init_array.cu
index 5916d2f..39f6ce0 100644
--- a/gp_emulator/gpu/kernel_init_array.cu
+++ b/gp_emulator/gpu/kernel_init_array.cu
@@ -21,20 +21,20 @@ void kernel_init_array(real *vec, const real init_val, const int vec_len)
 void gpu_init_array(real *vec, const int init_val, const int vec_len)
 {
     int nthread, nblock;
-
-    if( CUDA_BLOCK * vec_len < 1024)
+    
+    if( CUDA_BLOCK * vec_len < MAX_NUM_THREAD)
     {
         nthread = ceil( vec_len / CUDA_BLOCK );
         nblock = 1;
     }
     else
     {
-        if( vec_len / CUDA_BLOCK > 65536 * 1024 ) // largest block size for sm_2.0
+        if( vec_len / CUDA_BLOCK > MAX_NUM_BLOCK * MAX_NUM_THREAD ) // largest block size for sm_2.0
         {
-            printf("gpu_init_array: vector length / CUDA_BLOCK = %d > 1024 * 1024.\n", vec_len/CUDA_BLOCK);
+            printf("gpu_init_array: vector length / CUDA_BLOCK = %d > MAX_NUM_BLOCK * MAX_NUM_THREAD.\n", vec_len/CUDA_BLOCK);
             exit(EXIT_FAILURE);
         }
-        nthread = 1024;
+        nthread = MAX_NUM_THREAD;
         nblock = ceil( float(vec_len) / nthread / float(CUDA_BLOCK) );
     }
     kernel_init_array<<< nblock, nthread >>>(vec, init_val, vec_len);
diff --git a/gp_emulator/gpu/kernel_matrixExp.cu b/gp_emulator/gpu/kernel_matrixExp.cu
index 79bab30..95b56de 100644
--- a/gp_emulator/gpu/kernel_matrixExp.cu
+++ b/gp_emulator/gpu/kernel_matrixExp.cu
@@ -20,19 +20,19 @@ void gpu_matrixExp( real *matrix,const real alpha,const real beta, const int siz
 {
     int nthread, nblock;
 
-    if( size < float(1024) / float(CUDA_BLOCK) )
+    if( size < float( MAX_NUM_THREAD ) / float(CUDA_BLOCK) )
     {
-        printf("gpu_matrixExp: size = %d [ < 1024 / CUDA_BLOCK ].\n", size);
+        printf("gpu_matrixExp: size = %d [ < MAX_NUM_THREAD / CUDA_BLOCK ].\n", size);
         exit(EXIT_FAILURE);
     }
 
-    if( size > 65536 * 1024 )
+    if( size > MAX_NUM_BLOCK * MAX_NUM_THREAD )
     {
-        printf("gpu_matrixExp: size = %d [ > 65536 * 1024 / CUDA_BLOCK ].\n", size);
+        printf("gpu_matrixExp: size = %d [ > MAX_NUM_BLOCK * MAX_NUM_THREAD / CUDA_BLOCK ].\n", size);
         exit(EXIT_FAILURE);
     }
     
-    nthread = 1024; 
+    nthread = MAX_NUM_THREAD; 
     nblock = ceil( float(size) / float(CUDA_BLOCK) / float(nthread) );
     kernel_matrixExp<<<nblock,nthread>>>(matrix, alpha, beta, size);
 }
diff --git a/gp_emulator/gpu/kernel_rowSum.cu b/gp_emulator/gpu/kernel_rowSum.cu
index 6094c91..49e5cf7 100644
--- a/gp_emulator/gpu/kernel_rowSum.cu
+++ b/gp_emulator/gpu/kernel_rowSum.cu
@@ -1,29 +1,28 @@
 /*********************************************//**
  * row sum:
- * return vector rowSum(A)
+ * return vector rowSum(matrix)
  *********************************************/
 #include "gpu_predict.h"
 
-real* gpu_rowSum(const real *A, const int A_nrows,const int A_ncols)
+real* gpu_rowSum(const real *matrix, const int matrix_nrows,const int matrix_ncols)
 {
-    cublasStatus_t stat;
     cublasHandle_t handle;
-    stat=cublasCreate(&handle);
+    cublasCreate(&handle);
 
     real alpha = 1.f;
     real beta = 0.f;
     real *vec_one;
-    real *d_var;
+    real *d_result;
 
-    cudaMalloc((void **)&vec_one, sizeof(real) * A_ncols );
-    cudaMalloc((void **)&d_var, sizeof(real) * A_ncols);
+    cudaMalloc((void **)&vec_one, sizeof(real) * matrix_ncols);
+    cudaMalloc((void **)&d_result, sizeof(real) * matrix_ncols);
 
-    gpu_init_array(vec_one, 1.0, A_ncols);
-    gpu_init_array(d_var, 0.0, A_ncols);
+    gpu_init_array(vec_one, 1.0, matrix_ncols);
+    gpu_init_array(d_result, 0.0, matrix_ncols);
 
-    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, A_nrows, A_ncols, &alpha, A, A_nrows, vec_one, 1, &beta, d_var, 1));
+    cublasCheckErrors(CUBLAS_GEMV(handle, CUBLAS_OP_T, matrix_nrows, matrix_ncols, &alpha, matrix, matrix_nrows, vec_one, 1, &beta, d_result, 1));
 
     cudaFree(vec_one);
     cublasDestroy(handle);
-    return d_var;
+    return d_result;
 }
diff --git a/gp_emulator/gpu/kernel_scalarMinusVec.cu b/gp_emulator/gpu/kernel_scalarMinusVec.cu
index 2ca2fd8..c24b959 100644
--- a/gp_emulator/gpu/kernel_scalarMinusVec.cu
+++ b/gp_emulator/gpu/kernel_scalarMinusVec.cu
@@ -15,20 +15,20 @@ void kernel_scalarMinusVec(real *vec, const real scalar, const int size)
 void gpu_scalarMinusVec( real *matrix, const real scalar, const int size )
 {
     int nthread, nblock;
-    if( size > 65536 * 1024 )
+    if( size > MAX_NUM_BLOCK * MAX_NUM_THREAD )
     {
-        printf("gpu_scalarMinusVec: size = %d [ > 65536 * 1024 ].\n", size);
+        printf("gpu_scalarMinusVec: size = %d [ > MAX_NUM_BLOCK * MAX_NUM_THREAD ].\n", size);
         exit(EXIT_FAILURE);
     }
     
-    if( size < 1024 )
+    if( size < MAX_NUM_THREAD )
     {
         nthread = size;
         nblock = 1;
     }
     else
     {
-        nthread = 1024;
+        nthread = MAX_NUM_THREAD;
         nblock = ceil( float(size) / float(nthread) );
     }
 
diff --git a/gp_emulator/gpu/kernel_vectorTimesMatrix.cu b/gp_emulator/gpu/kernel_vectorTimesMatrix.cu
index 3ae3bf3..63283be 100644
--- a/gp_emulator/gpu/kernel_vectorTimesMatrix.cu
+++ b/gp_emulator/gpu/kernel_vectorTimesMatrix.cu
@@ -1,30 +1,30 @@
 /*********************************************//** 
  * vector matrix elementwise multiplication
- * res_{ix,iy} = A_{ix,iy} * v_{iy}
- * A_ld: leading dimension of matrix A
- * A_sd: secondary dimension of matrix A
+ * res_{ix,iy} = matrix_{ix,iy} * v_{iy}
+ * matrix_lead_dim: leading dimension of matrix matrix
+ * matrix_second_dim: secondary dimension of matrix matrix
  *********************************************/
 #include "gpu_predict.h"
+#define VTM_THREADX 100 //best thread number in x dim for vectorTimesMatrix
 __global__ 
-void kernel_vectorTimesMatrix(const real *A, const real * v, real *res, int A_ld, int A_sd)
+void kernel_vectorTimesMatrix(const real *matrix, const real * vector, real *res, int matrix_lead_dim, int matrix_second_dim)
 {
     int ix, iy;
     ix = blockIdx.x * blockDim.x + threadIdx.x;
     iy = blockIdx.y * blockDim.y + threadIdx.y;
-    if( ix < A_ld && iy < A_sd)
-    res[IDX2D(ix, iy, A_ld)] = A[IDX2D(ix, iy, A_ld)] * v[iy];
+    if( ix < matrix_lead_dim && iy < matrix_second_dim)
+        res[IDX2D(ix, iy, matrix_lead_dim)] = matrix[IDX2D(ix, iy, matrix_lead_dim)] * vector[iy];
 }
 
 
-extern "C"{
-void gpu_vectorTimesMatrix(const real *A, const real *v, real *res, int nrows, int ncols)
+void gpu_vectorTimesMatrix(const real *matrix, const real *vector, real *res, int nrows, int ncols)
 {
     dim3 nthread, nblock;
-    if( nrows > 1000 )
+    if( nrows > MIN_NPREDICT )
     {
-        nthread.x = 100;
+        nthread.x = VTM_THREADX;
         nthread.y = ncols;
-        nblock.x = ceil(float(nrows)/100);
+        nblock.x = ceil(float(nrows)/VTM_THREADX);
         nblock.y = 1;
     }
     else
@@ -34,8 +34,7 @@ void gpu_vectorTimesMatrix(const real *A, const real *v, real *res, int nrows, i
         nblock.x = nrows;
         nblock.y = 1;
     }
-    kernel_vectorTimesMatrix<<<nblock, nthread>>>(A, v, res, nrows, ncols);
-}
+    kernel_vectorTimesMatrix<<<nblock, nthread>>>(matrix, vector, res, nrows, ncols);
 }
 
 
diff --git a/gp_emulator/gpu/predict.cu b/gp_emulator/gpu/predict.cu
index 48a4f52..87737c3 100644
--- a/gp_emulator/gpu/predict.cu
+++ b/gp_emulator/gpu/predict.cu
@@ -7,8 +7,6 @@
  *********************************************/
 #include "gpu_predict.h"
 #include <stdlib.h>
-#define debug
-
 
 void gpuPredict::init_gpu(void)
 {
@@ -33,9 +31,6 @@ void gpuPredict::init_gpu(void)
                 Ntrain, Ntrain, sizeof(real), c_invQ, Ntrain, d_invQ, Ntrain ));
     cublasCheckErrors(cublasSetMatrix( 
                 Npredict, Ninputs, sizeof(real), c_predict, Npredict, d_predict, Npredict));
-
-
-
 }
 
 /*********************************//*
@@ -128,7 +123,6 @@ void gpuPredict::compute_error()
 
 /*********************************//*
  * compute deriv:
- *  
  ********************************/
 void gpuPredict::compute_deriv( void )
 {
diff --git a/tests/benchmark.py b/tests/benchmark.py
index beb0853..72b5a1b 100644
--- a/tests/benchmark.py
+++ b/tests/benchmark.py
@@ -8,11 +8,11 @@
 from types import MethodType
 import sys
 
-def set_testing_val (self, P, N, M):
-    self.D = P
-    self.theta = np.random.random((P+2))
-    self.invQ = np.random.random((M,M))
-    self.invQt = np.random.random ((M))
+def set_testing_val (self, Ninputs, Npredict, Ntrain):
+    self.D = Ninputs
+    self.theta = np.random.random((Ninputs+2))
+    self.invQ = np.random.random(( Ntrain, Ntrain ))
+    self.invQt = np.random.random ((Ntrain))
 
 
 if __name__ == '__main__':
@@ -21,16 +21,15 @@ def set_testing_val (self, P, N, M):
     print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
     print '-----------------------------'
     
-    for size in xrange(np.int(1e3), np.int(1e5), np.int(1e4)):
-        N = size
-        M = 250
-        P = 10
+    for Npredict in xrange(np.int(1e5), np.int(1e6), np.int(1e5)):
+        Ntrain = 250
+        Ninputs = 10
 
-        inputs = np.random.random((M,P))
-        testing=np.random.random((N,P))
+        inputs = np.random.random(( Ntrain, Ninputs))
+        testing = np.random.random(( Npredict, Ninputs))
         
         gp = GaussianProcess(inputs, [])
-        gp.set_testing_val(P, N, M)
+        gp.set_testing_val(Ninputs, Npredict, Ntrain)
 
         #CPU predict
     	start = time.time()
@@ -40,10 +39,10 @@ def set_testing_val (self, P, N, M):
     	
         #GPU predict
         start = time.time()
-        [mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, precision = np.float64, threshold = 1e4)
+        [mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, precision = np.float32, threshold = 1e5)
     	end =time.time()
     	gputime = end - start
-        print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (N, cputime, gputime, cputime/gputime),
+        print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (Npredict, cputime, gputime, cputime/gputime),
 
     
     

From c0fb98a08f52eb37fdc9938952bf0fb96e686200 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 15:50:25 +0100
Subject: [PATCH 109/117] remove print debug for cdist and crossmius

---
 gp_emulator/gpu/gpu_predict.h        | 2 +-
 gp_emulator/gpu/kernel_cdist.cu      | 1 -
 gp_emulator/gpu/kernel_crossMinus.cu | 1 -
 3 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index f5d0965..3019365 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -12,7 +12,7 @@
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
 
-#define CUDA_BLOCK 5
+#define CUDA_BLOCK 2
 #define MIN_NPREDICT 1000 //ensure there will be enough amount of threads to launch. 
 #define MAX_NUM_THREAD 1024 //sm_20
 #define MAX_NUM_BLOCK 65536
diff --git a/gp_emulator/gpu/kernel_cdist.cu b/gp_emulator/gpu/kernel_cdist.cu
index c6f9bb2..f98a76b 100644
--- a/gp_emulator/gpu/kernel_cdist.cu
+++ b/gp_emulator/gpu/kernel_cdist.cu
@@ -25,7 +25,6 @@ void kernel_cdist(const real *input1, const real *input2, real *output, const in
 
 void gpu_cdist(const real *input1, const real *input2, real *output, const int nrow1, const int ncol1, const int nrow2, const int ncol2)
 {
-    printf("cdist  ");
     if( nrow2 < MIN_NPREDICT )
     {
         printf("gpu_cdist: %d [nrow2(Npredict)] < %d\n",nrow2, MIN_NPREDICT);
diff --git a/gp_emulator/gpu/kernel_crossMinus.cu b/gp_emulator/gpu/kernel_crossMinus.cu
index 2e5ad94..80c4cac 100644
--- a/gp_emulator/gpu/kernel_crossMinus.cu
+++ b/gp_emulator/gpu/kernel_crossMinus.cu
@@ -17,7 +17,6 @@ void kernel_crossMinus(const real *vec1,const real *vec2, real *matrix_result, c
 
 void gpu_crossMinus(const real *vec1, const real *vec2, real *matrix_result, const int vec1_len, const int vec2_len)
 {
-printf("crossMinus  ");
     dim3 nthread, nblock;
     if( vec2_len < MAX_NUM_THREAD )
         nthread.y = vec2_len;

From b829ac0eea478430aa71a445b2adf98aa28b7318 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 17:25:34 +0100
Subject: [PATCH 110/117] tidy up

---
 deviceQuery                              | Bin 16312 -> 0 bytes
 gp_emulator/gpu/tests/gpu_predict_test.h |   7 ---
 gp_emulator/gpu/tests/testCdist.cu       |  56 ++++++++++-------------
 gp_emulator/gpu/tests/testCublasgemm.cu  |  50 ++++++++++----------
 gp_emulator/gpu/tests/testInitArray.cu   |  16 +++----
 gp_emulator/gpu/tests/testVecTimesMat.cu |  32 ++++++-------
 tests/unit_tests.py                      |   2 -
 7 files changed, 74 insertions(+), 89 deletions(-)
 delete mode 100755 deviceQuery

diff --git a/deviceQuery b/deviceQuery
deleted file mode 100755
index 9b7a4d6ff122005f00cce449db98d6b737b83b31..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 16312
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diff --git a/gp_emulator/gpu/tests/gpu_predict_test.h b/gp_emulator/gpu/tests/gpu_predict_test.h
index a83c8ed..f689678 100644
--- a/gp_emulator/gpu/tests/gpu_predict_test.h
+++ b/gp_emulator/gpu/tests/gpu_predict_test.h
@@ -8,9 +8,7 @@ extern real *expX, *expXsqrt, *in_train, *in_predict, *invQ, *invQt;
 extern real *cdist_a, *cdist_expa;
 extern real *cdist_test_var1, *cdist_test_var2, *cdist_test_var3;
 extern real *error_test1;
-
 extern real *result_py, *error_py, *deriv_py;
-
 extern int Npredict_t, Ntrain_t, Ninputs_t;
 extern int theta_size_t;
 
@@ -43,9 +41,4 @@ void testCublasgemm(const real *c_mat1,
         const real *c_mat2, const real *c_res, 
         const int mat1_nrows, const int mat1_ncols, 
         const int mat2_nrows, const int mat2_ncols);
-/*
-void testPredict(real *expX, real *expXsqrt, 
-        real *invQt, real *invQ, real *in_predict, real *in_train, 
-        int Npredict_t, int  Ntrain_t, int Ninputs_t, int theta_size_t, 
-        real *result_py, real *error_t, real *deriv_py);*/
 void testPredict(void);
diff --git a/gp_emulator/gpu/tests/testCdist.cu b/gp_emulator/gpu/tests/testCdist.cu
index e450ca4..95527c1 100644
--- a/gp_emulator/gpu/tests/testCdist.cu
+++ b/gp_emulator/gpu/tests/testCdist.cu
@@ -1,43 +1,37 @@
 #include "gpu_predict_test.h"
-#define IDX2D(i,j,ld) (((j)*(ld))+(i))
 
-
-void testCdist( const real *in1,const real *in2, const real *res, 
-        const int in1_nrows, const int in2_nrows, const int in_ncols )
+void testCdist( const real *matrix1,const real *matrix2, const real *result, 
+        const int matrix1_nrows, const int matrix2_nrows, const int matrix_ncols )
 {
-    int i, error;
-    real *in1_T, *in2_T, *gpu_res;
-    real *d_in1, *d_in2, *d_res; 
+    int i;
+    real *matrix1_T, *matrix2_T, *gpu_result;
+    real *d_matrix1, *d_matrix2, *d_result; 
 
-//    in1_T = (real *)malloc( sizeof(real) * in1_nrows * in_ncols );
-//    in2_T = (real *)malloc( sizeof(real) * in2_nrows * in_ncols );
-    gpu_res = (real *)malloc( sizeof(real) * in1_nrows * in2_nrows ); 
-    in1_T = computeTranspose( in1, in_ncols, in1_nrows );
-    in2_T = computeTranspose( in2, in_ncols, in2_nrows );
-    
-    
+    gpu_result = (real *)malloc( sizeof(real) * matrix1_nrows * matrix2_nrows ); 
+    matrix1_T = computeTranspose( matrix1, matrix_ncols, matrix1_nrows );
+    matrix2_T = computeTranspose( matrix2, matrix_ncols, matrix2_nrows );
     
     /*GPU part*/
-    for( i = 0; i < in1_nrows * in2_nrows; i++ )
-        gpu_res[i] = 0;
+    for( i = 0; i < matrix1_nrows * matrix2_nrows; i++ )
+        gpu_result[i] = 0;
     
     
-    cudaMalloc((void **)&d_in1, sizeof(real) * in1_nrows * in_ncols );
-    cudaMalloc((void **)&d_in2, sizeof(real) * in2_nrows * in_ncols );
-    cudaMalloc((void **)&d_res, sizeof(real) * in2_nrows * in1_nrows );
-    cublasCheckErrors(cublasSetMatrix( in1_nrows, in_ncols, sizeof(real), in1_T, in1_nrows, d_in1, in1_nrows) );
-    cublasCheckErrors(cublasSetMatrix( in2_nrows, in_ncols, sizeof(real), in2_T, in2_nrows, d_in2, in2_nrows) );
-    cublasCheckErrors(cublasSetMatrix( in2_nrows, in1_nrows, sizeof(real), gpu_res, in2_nrows, d_res,in2_nrows) );
-    gpu_cdist(d_in1, d_in2, d_res, in1_nrows, in_ncols, in2_nrows, in_ncols);
-    cudaMemcpy(gpu_res, d_res, sizeof(real) * in2_nrows * in1_nrows, cudaMemcpyDeviceToHost);
+    cudaMalloc((void **)&d_matrix1, sizeof(real) * matrix1_nrows * matrix_ncols );
+    cudaMalloc((void **)&d_matrix2, sizeof(real) * matrix2_nrows * matrix_ncols );
+    cudaMalloc((void **)&d_result, sizeof(real) * matrix2_nrows * matrix1_nrows );
+    cublasCheckErrors(cublasSetMatrix( matrix1_nrows, matrix_ncols, sizeof(real), matrix1_T, matrix1_nrows, d_matrix1, matrix1_nrows) );
+    cublasCheckErrors(cublasSetMatrix( matrix2_nrows, matrix_ncols, sizeof(real), matrix2_T, matrix2_nrows, d_matrix2, matrix2_nrows) );
+    cublasCheckErrors(cublasSetMatrix( matrix2_nrows, matrix1_nrows, sizeof(real), gpu_result, matrix2_nrows, d_result,matrix2_nrows) );
+    gpu_cdist(d_matrix1, d_matrix2, d_result, matrix1_nrows, matrix_ncols, matrix2_nrows, matrix_ncols);
+    cudaMemcpy(gpu_result, d_result, sizeof(real) * matrix2_nrows * matrix1_nrows, cudaMemcpyDeviceToHost);
     
-    compare_result(gpu_res, res, in1_nrows * in2_nrows, EPSILON_AVG, EPSILON_MAX, "RESULT");
+    compare_result(gpu_result, result, matrix1_nrows * matrix2_nrows, EPSILON_AVG, EPSILON_MAX, "RESULT");
     
-    free(in1_T);
-    free(in2_T);
-    free(gpu_res);
+    free(matrix1_T);
+    free(matrix2_T);
+    free(gpu_result);
    
-    cudaFree(d_in1);
-    cudaFree(d_in2);
-    cudaFree(d_res);
+    cudaFree(d_matrix1);
+    cudaFree(d_matrix2);
+    cudaFree(d_result);
 }
diff --git a/gp_emulator/gpu/tests/testCublasgemm.cu b/gp_emulator/gpu/tests/testCublasgemm.cu
index f04107c..6fc6e4b 100644
--- a/gp_emulator/gpu/tests/testCublasgemm.cu
+++ b/gp_emulator/gpu/tests/testCublasgemm.cu
@@ -1,48 +1,48 @@
 #include "gpu_predict_test.h"
 
-void testCublasgemm(const real *c_mat1, const real *c_mat2, const real *c_res, 
-        const int mat1_nrows, const int mat1_ncols, const int mat2_nrows, 
-        const int mat2_ncols)
+void testCublasgemm(const real *c_matrix1, const real *c_matrix2, const real *c_result, 
+        const int matrix1_nrows, const int matrix1_ncols, const int matrix2_nrows, 
+        const int matrix2_ncols)
 {
     cublasHandle_t handle;
     cublasCreate(&handle);
     
 
-    real *d_mat1, *d_mat2, *d_res;
-    real *c_gpu_res;
-    c_gpu_res = (real *)malloc( sizeof(real) * mat2_nrows * mat2_ncols);
+    real *d_matrix1, *d_matrix2, *d_result;
+    real *c_gpu_result;
+    c_gpu_result = (real *)malloc( sizeof(real) * matrix2_nrows * matrix2_ncols);
     
-    cudaMalloc( (void **)&d_mat1, sizeof(real) * mat1_nrows * mat1_ncols );
-    cudaMalloc( (void **)&d_mat2, sizeof(real) * mat2_nrows * mat2_ncols );
-    cudaMalloc( (void **)&d_res, sizeof(real) * mat1_nrows * mat2_ncols );
+    cudaMalloc( (void **)&d_matrix1, sizeof(real) * matrix1_nrows * matrix1_ncols );
+    cudaMalloc( (void **)&d_matrix2, sizeof(real) * matrix2_nrows * matrix2_ncols );
+    cudaMalloc( (void **)&d_result, sizeof(real) * matrix1_nrows * matrix2_ncols );
 
 
-    real *c_mat1_T;
+    real *c_matrix1_T;
     
     
-   c_mat1_T = computeTranspose(c_mat1, mat1_nrows, mat1_ncols); 
+   c_matrix1_T = computeTranspose(c_matrix1, matrix1_nrows, matrix1_ncols); 
 
-    cudaMemcpy( d_mat1, c_mat1_T, sizeof(real) * mat1_nrows * mat1_ncols, cudaMemcpyHostToDevice);
-    cudaMemcpy( d_mat2, c_mat2, sizeof(real) * mat1_nrows * mat2_ncols, cudaMemcpyHostToDevice);
+    cudaMemcpy( d_matrix1, c_matrix1_T, sizeof(real) * matrix1_nrows * matrix1_ncols, cudaMemcpyHostToDevice);
+    cudaMemcpy( d_matrix2, c_matrix2, sizeof(real) * matrix1_nrows * matrix2_ncols, cudaMemcpyHostToDevice);
 
 
     real alpha = 1.f;
     real beta = 0.f;
     CUBLAS_GEMM( handle, CUBLAS_OP_N, CUBLAS_OP_T, 
-                mat1_nrows, mat2_ncols,  mat1_ncols,
+                matrix1_nrows, matrix2_ncols,  matrix1_ncols,
                 &alpha, 
-                d_mat1, mat1_nrows, 
-                d_mat2, mat2_ncols, 
+                d_matrix1, matrix1_nrows, 
+                d_matrix2, matrix2_ncols, 
                 &beta, 
-                d_res, mat1_ncols );
+                d_result, matrix1_ncols );
 
-    cudaMemcpy( c_gpu_res, d_res, sizeof(real) * mat1_nrows *  mat2_ncols, cudaMemcpyDeviceToHost);
-    c_gpu_res = computeTranspose(c_gpu_res, mat1_nrows, mat2_ncols); 
-    compare_result( c_gpu_res, c_res, mat1_nrows * mat2_ncols, EPSILON_AVG, EPSILON_MAX, "RESULTS");
+    cudaMemcpy( c_gpu_result, d_result, sizeof(real) * matrix1_nrows *  matrix2_ncols, cudaMemcpyDeviceToHost);
+    c_gpu_result = computeTranspose(c_gpu_result, matrix1_nrows, matrix2_ncols); 
+    compare_result( c_gpu_result, c_result, matrix1_nrows * matrix2_ncols, EPSILON_AVG, EPSILON_MAX, "RESULTS");
     
-    free(c_mat1_T);
-    cudaFree(d_mat1);
-    cudaFree(d_mat2);
-    cudaFree(d_res);
-    free(c_gpu_res);
+    free(c_matrix1_T);
+    cudaFree(d_matrix1);
+    cudaFree(d_matrix2);
+    cudaFree(d_result);
+    free(c_gpu_result);
 }
diff --git a/gp_emulator/gpu/tests/testInitArray.cu b/gp_emulator/gpu/tests/testInitArray.cu
index b1a229c..dc00e62 100644
--- a/gp_emulator/gpu/tests/testInitArray.cu
+++ b/gp_emulator/gpu/tests/testInitArray.cu
@@ -2,29 +2,29 @@
 
 void testInitArray(void)
 {
-    real *vec_c, *vec_g;
+    real *c_vec, *d_vec;
     real val;
     int len;
     int i, test, error;
-    vec_c = (real *)malloc( int(sizeof(real) * 1e7) );
-    cudaMalloc( (void **)&vec_g, int(sizeof(real) * 1e7) );
+    c_vec = (real *)malloc( int(sizeof(real) * 1e7) );
+    cudaMalloc( (void **)&d_vec, int(sizeof(real) * 1e7) );
  
     for( test = 0; test < 6; ++test )
     {
         val = pow( 10.0, test-5);
         len = pow( 10.0, test );
-        gpu_init_array(vec_g, val, len);
-        cudaMemcpy(vec_g, vec_c, sizeof(real) * len, cudaMemcpyDeviceToHost);
+        gpu_init_array(d_vec, val, len);
+        cudaMemcpy(d_vec, c_vec, sizeof(real) * len, cudaMemcpyDeviceToHost);
         
         error = 0;
         for( i = 0; i < len; ++i )
         {
-            if( ( vec_c[i] - val ) / val > 1e-15 )
+            if( ( c_vec[i] - val ) / val > 1e-15 )
                 error++;
         }
         CU_ASSERT(error == 0);
     }
 
-    free(vec_c);
-    cudaFree(vec_g);
+    free(c_vec);
+    cudaFree(d_vec);
 }
diff --git a/gp_emulator/gpu/tests/testVecTimesMat.cu b/gp_emulator/gpu/tests/testVecTimesMat.cu
index 7bd4bee..227b7c2 100644
--- a/gp_emulator/gpu/tests/testVecTimesMat.cu
+++ b/gp_emulator/gpu/tests/testVecTimesMat.cu
@@ -1,36 +1,36 @@
 #include "gpu_predict_test.h"
 
 
-void testVecTimesMat(const real *c_vec,const  real *c_mat, const real *c_res,const int vec_len, const int mat_nrows, const int mat_ncols)
+void testVecTimesMat(const real *c_vec,const  real *c_matrix, const real *c_res,const int vec_len, const int matrix_nrows, const int matrix_ncols)
 {
-    real *d_vec, *d_mat, *d_res;
+    real *d_vec, *d_matrix, *d_res;
     real *c_res_gpu;
-    real *c_mat_T;
+    real *c_matrix_T;
 
-    CU_ASSERT (vec_len == mat_ncols);
+    CU_ASSERT (vec_len == matrix_ncols);
     cudaMalloc((void **)&d_vec, sizeof(real) * vec_len );
-    cudaMalloc((void **)&d_mat, sizeof(real) * mat_nrows * mat_ncols );
-    cudaMalloc((void **)&d_res, sizeof(real) * mat_nrows * mat_ncols );
+    cudaMalloc((void **)&d_matrix, sizeof(real) * matrix_nrows * matrix_ncols );
+    cudaMalloc((void **)&d_res, sizeof(real) * matrix_nrows * matrix_ncols );
     
-    c_mat_T = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols);
-    c_res_gpu = (real *)malloc( sizeof(real) * mat_nrows * mat_ncols );
+    c_matrix_T = (real *)malloc( sizeof(real) * matrix_nrows * matrix_ncols);
+    c_res_gpu = (real *)malloc( sizeof(real) * matrix_nrows * matrix_ncols );
 
-    c_mat_T = computeTranspose(c_mat, mat_ncols, mat_nrows);
+    c_matrix_T = computeTranspose(c_matrix, matrix_ncols, matrix_nrows);
 
     cublasCheckErrors(cublasSetVector( vec_len, sizeof(real), c_vec, 1, d_vec, 1 ));
-    cublasCheckErrors(cublasSetMatrix( mat_nrows, mat_ncols, sizeof(real), c_mat_T, mat_nrows, d_mat, mat_nrows));
+    cublasCheckErrors(cublasSetmatrixrix( matrix_nrows, matrix_ncols, sizeof(real), c_matrix_T, matrix_nrows, d_matrix, matrix_nrows));
     
-    gpu_vectorTimesMatrix( d_mat, d_vec, d_res, mat_nrows, mat_ncols);
+    gpu_vectorTimesmatrixrix( d_matrix, d_vec, d_res, matrix_nrows, matrix_ncols);
 
-    cudaMemcpy(c_res_gpu, d_res, sizeof(real) * mat_nrows * mat_ncols, cudaMemcpyDeviceToHost);
-    c_res_gpu = computeTranspose(c_res_gpu, mat_nrows, mat_ncols);
-    compare_result( c_res_gpu, c_res, mat_nrows * mat_ncols, EPSILON_AVG, EPSILON_MAX, "RESULT");
+    cudaMemcpy(c_res_gpu, d_res, sizeof(real) * matrix_nrows * matrix_ncols, cudaMemcpyDeviceToHost);
+    c_res_gpu = computeTranspose(c_res_gpu, matrix_nrows, matrix_ncols);
+    compare_result( c_res_gpu, c_res, matrix_nrows * matrix_ncols, EPSILON_AVG, EPSILON_MAX, "RESULT");
 
     free(c_res_gpu);
-    free(c_mat_T);
+    free(c_matrix_T);
 
     cudaFree(d_vec);
-    cudaFree(d_mat);
+    cudaFree(d_matrix);
     cudaFree(d_res);
 
 }
diff --git a/tests/unit_tests.py b/tests/unit_tests.py
index ddbfeb4..9933a9b 100755
--- a/tests/unit_tests.py
+++ b/tests/unit_tests.py
@@ -16,8 +16,6 @@ def get_testing_val (self, testing, do_unc = True):
         ( nn, D ) = testing.shape
         assert D == self.D
         expX=np.exp(self.theta)
-
-
         
         t_expX = expX
         t_expXsqrt = np.sqrt(expX[:(self.D)])

From ce5317b55155e45ad458d50fbdd131eed98cb2fd Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Thu, 25 Jun 2015 17:28:15 +0100
Subject: [PATCH 111/117] correct typo

---
 gp_emulator/gpu/gpu_predict.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gp_emulator/gpu/gpu_predict.h b/gp_emulator/gpu/gpu_predict.h
index 3019365..e31aabd 100644
--- a/gp_emulator/gpu/gpu_predict.h
+++ b/gp_emulator/gpu/gpu_predict.h
@@ -1,4 +1,4 @@
-/* A file to test imorting C modules for handling arrays to Python */
+/* A file to test importing C modules for handling arrays to Python */
 
 #include "Python.h"
 #include "arrayobject.h"

From c32e28cde3f08f1ac53b566c0a0e4e64c94815eb Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Fri, 26 Jun 2015 10:41:07 +0100
Subject: [PATCH 112/117] typo

---
 gp_emulator/gpu/tests/testVecTimesMat.cu | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/gp_emulator/gpu/tests/testVecTimesMat.cu b/gp_emulator/gpu/tests/testVecTimesMat.cu
index 227b7c2..ca39dbf 100644
--- a/gp_emulator/gpu/tests/testVecTimesMat.cu
+++ b/gp_emulator/gpu/tests/testVecTimesMat.cu
@@ -1,6 +1,5 @@
 #include "gpu_predict_test.h"
 
-
 void testVecTimesMat(const real *c_vec,const  real *c_matrix, const real *c_res,const int vec_len, const int matrix_nrows, const int matrix_ncols)
 {
     real *d_vec, *d_matrix, *d_res;
@@ -18,9 +17,9 @@ void testVecTimesMat(const real *c_vec,const  real *c_matrix, const real *c_res,
     c_matrix_T = computeTranspose(c_matrix, matrix_ncols, matrix_nrows);
 
     cublasCheckErrors(cublasSetVector( vec_len, sizeof(real), c_vec, 1, d_vec, 1 ));
-    cublasCheckErrors(cublasSetmatrixrix( matrix_nrows, matrix_ncols, sizeof(real), c_matrix_T, matrix_nrows, d_matrix, matrix_nrows));
+    cublasCheckErrors(cublasSetMatrix( matrix_nrows, matrix_ncols, sizeof(real), c_matrix_T, matrix_nrows, d_matrix, matrix_nrows));
     
-    gpu_vectorTimesmatrixrix( d_matrix, d_vec, d_res, matrix_nrows, matrix_ncols);
+    gpu_vectorTimesMatrix( d_matrix, d_vec, d_res, matrix_nrows, matrix_ncols);
 
     cudaMemcpy(c_res_gpu, d_res, sizeof(real) * matrix_nrows * matrix_ncols, cudaMemcpyDeviceToHost);
     c_res_gpu = computeTranspose(c_res_gpu, matrix_nrows, matrix_ncols);

From f8ebde498a6fb26aac8a9bd4ffc1244a7858e7b3 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 1 Jul 2015 13:53:22 +0100
Subject: [PATCH 113/117] benchmark2: timing cpu times by operations

---
 tests/benchmark2.py | 142 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 142 insertions(+)
 create mode 100644 tests/benchmark2.py

diff --git a/tests/benchmark2.py b/tests/benchmark2.py
new file mode 100644
index 0000000..7b59dfa
--- /dev/null
+++ b/tests/benchmark2.py
@@ -0,0 +1,142 @@
+#!/usr/local/bin/python
+
+import numpy as np
+import scipy.spatial.distance as dist
+import _gpu_predict
+import time
+from gp_emulator import GaussianProcess 
+from types import MethodType
+import sys
+
+def set_testing_val (self, Ninputs, Npredict, Ntrain):
+    self.D = Ninputs
+    self.theta = np.random.random((Ninputs+2))
+    self.invQ = np.random.random(( Ntrain, Ntrain ))
+    self.invQt = np.random.random ((Ntrain))
+
+
+def predict_benchmark( self, testing, do_unc=True ):
+    start_predict = time.time()
+    ( nn, D ) = testing.shape
+    assert D == self.D
+    expX = np.exp ( self.theta )
+    
+    start_mvel = time.time()
+    in_dist1 = np.sqrt(expX[:(self.D)])*self.inputs
+    in_dist2 = np.sqrt(expX[:(self.D)])*testing
+    end_mvel = time.time()
+    print 'mvel(2)', end_mvel - start_mvel
+
+    start_dist = time.time()
+    a = dist.cdist ( in_dist1, in_dist2, 'sqeuclidean')
+    end_dist = time.time()
+    print 'dist(1)', end_dist - start_dist
+
+    start_exp = time.time()
+    a = expX[self.D]*np.exp(-0.5*a)
+    end_exp = time.time()
+    print 'exp-ele(3)', end_exp - start_exp
+
+    b = expX[self.D]
+    
+    start_mv =time.time()
+    mu = np.dot( a.T, self.invQt)
+    end_mv = time.time()
+    print 'mv(1)', end_mv - start_mv
+    
+    if do_unc:
+        start_mm = time.time()
+        k = np.dot(self.invQ,a)
+        end_mm = time.time()
+        print 'mm(1)', end_mm - start_mm
+
+        start_mulele = time.time()
+        k = a * k 
+        end_mulele = time.time()
+        print 'mul-ele(1)', end_mulele - start_mulele
+
+        start_sum = time.time()
+        var = b - np.sum (b, axis=0)
+        end_sum = time.time()
+        print 'sum(1)', end_sum - start_sum
+        # Derivative and partial derivatives of the function
+    deriv = np.zeros ( ( nn, self.D ) )
+
+    crossminus = 0
+    mul_ele2 = 0
+    mv2 = 0
+
+    for d in xrange ( self.D ):
+        s_crossminus = time.time()
+        aa = self.inputs[:,d].flatten()[None,:] - testing[:,d].flatten()[:,None]
+        e_crossminus = time.time()
+        crossminus = crossminus + e_crossminus - s_crossminus
+
+        s_mul_ele2 = time.time()
+        c = a*aa.T
+        e_mul_ele2 = time.time()
+        mul_ele2 = mul_ele2 + e_mul_ele2 - s_mul_ele2
+
+        s_mv2 = time.time()
+        deriv[:, d] = expX[d]*np.dot(c.T, self.invQt)
+        e_mv2 = time.time()
+        mv2 = mv2 + e_mv2 - s_mv2
+    end_predict = time.time()
+       
+    print 'crossminus(10)', crossminus
+    print 'mul_ele2(10)', mul_ele2
+    print 'mv2(10)', mv2
+    print 'predict',end_predict - start_predict
+
+    if do_unc:
+        return mu, var, deriv
+    else:
+        return mu, deriv
+
+
+if __name__ == '__main__':
+
+    GaussianProcess.set_testing_val = MethodType(set_testing_val, None, GaussianProcess)
+    GaussianProcess.predict_benchmark = MethodType(predict_benchmark, None, GaussianProcess)
+    print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
+    print '-----------------------------'
+    
+    for Npredict in xrange(np.int(1e6), np.int(2e6), np.int(1e6)):
+        Ntrain = 250
+        Ninputs = 10
+
+        inputs = np.random.random(( Ntrain, Ninputs))
+        testing = np.random.random(( Npredict, Ninputs))
+        
+        gp = GaussianProcess(inputs, [])
+        gp.set_testing_val(Ninputs, Npredict, Ntrain)
+
+        #CPU predict
+    	start = time.time()
+    	[mu_c, var_c, deriv_c] = gp.predict_benchmark(testing )
+    	end = time.time()
+    	cputime = end -start
+    	
+        #GPU predict
+        start = time.time()
+#        [mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, precision = np.float32, threshold = 1e5)
+    	end =time.time()
+    	gputime = end - start
+        print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (Npredict, cputime, gputime, cputime/gputime),
+
+    
+    
+        # checking results
+#        try: 
+#            e_mu  = max(abs(mu_c - mu_g) ) / np.max(abs(mu_c))
+#            e_var = max(abs(var_c - var_g)) / np.max(abs(var_c))
+#            e_deriv = np.max(abs(deriv_c - deriv_g)) / np.max(abs(deriv_c))
+#        except ValueError:
+#            print 'Results have invalid data type or dimension.'
+#        if e_mu > 1e-5 or e_var > 1e-5 or e_deriv > 1e-5:
+#            print 'FAILED\t',
+#        else:
+#            print 'Pass\t',
+#        print 'e_mu=%.2g\te_var=%.2g\te_deriv=%.2g\t'%(e_mu, e_var, e_deriv)
+
+

From 92f3460bc0dcfedf05cf4cd91318278cf9ed02f4 Mon Sep 17 00:00:00 2001
From: sinanshi <s.shi@ucl.ac.uk>
Date: Wed, 1 Jul 2015 16:37:16 +0100
Subject: [PATCH 114/117] print out properly

---
 tests/benchmark2.py | 53 +++++++++++++++------------------------------
 1 file changed, 17 insertions(+), 36 deletions(-)

diff --git a/tests/benchmark2.py b/tests/benchmark2.py
index 7b59dfa..18cc4a0 100644
--- a/tests/benchmark2.py
+++ b/tests/benchmark2.py
@@ -25,40 +25,40 @@ def predict_benchmark( self, testing, do_unc=True ):
     in_dist1 = np.sqrt(expX[:(self.D)])*self.inputs
     in_dist2 = np.sqrt(expX[:(self.D)])*testing
     end_mvel = time.time()
-    print 'mvel(2)', end_mvel - start_mvel
+    print 'mvel',2, end_mvel - start_mvel
 
     start_dist = time.time()
     a = dist.cdist ( in_dist1, in_dist2, 'sqeuclidean')
     end_dist = time.time()
-    print 'dist(1)', end_dist - start_dist
+    print 'dist',1, end_dist - start_dist
 
     start_exp = time.time()
     a = expX[self.D]*np.exp(-0.5*a)
     end_exp = time.time()
-    print 'exp-ele(3)', end_exp - start_exp
+    print 'exp-ele',3, end_exp - start_exp
 
     b = expX[self.D]
     
     start_mv =time.time()
     mu = np.dot( a.T, self.invQt)
     end_mv = time.time()
-    print 'mv(1)', end_mv - start_mv
+    print 'mv',1,end_mv - start_mv
     
     if do_unc:
         start_mm = time.time()
         k = np.dot(self.invQ,a)
         end_mm = time.time()
-        print 'mm(1)', end_mm - start_mm
+        print 'mm',1,end_mm - start_mm
 
         start_mulele = time.time()
         k = a * k 
         end_mulele = time.time()
-        print 'mul-ele(1)', end_mulele - start_mulele
+        print 'mul-ele',1, end_mulele - start_mulele
 
         start_sum = time.time()
         var = b - np.sum (b, axis=0)
         end_sum = time.time()
-        print 'sum(1)', end_sum - start_sum
+        print 'sum',1, end_sum - start_sum
         # Derivative and partial derivatives of the function
     deriv = np.zeros ( ( nn, self.D ) )
 
@@ -83,10 +83,10 @@ def predict_benchmark( self, testing, do_unc=True ):
         mv2 = mv2 + e_mv2 - s_mv2
     end_predict = time.time()
        
-    print 'crossminus(10)', crossminus
-    print 'mul_ele2(10)', mul_ele2
-    print 'mv2(10)', mv2
-    print 'predict',end_predict - start_predict
+    print 'crossminus',10, crossminus
+    print 'mul_ele2',10,mul_ele2
+    print 'mv2',10, mv2
+    print 'predict',1,end_predict - start_predict
 
     if do_unc:
         return mu, var, deriv
@@ -101,8 +101,10 @@ def predict_benchmark( self, testing, do_unc=True ):
     print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
     print '-----------------------------'
     
-    for Npredict in xrange(np.int(1e6), np.int(2e6), np.int(1e6)):
-        Ntrain = 250
+    for i in xrange(10):
+        
+        Npredict = np.int(1e6)
+	Ntrain = 250
         Ninputs = 10
 
         inputs = np.random.random(( Ntrain, Ninputs))
@@ -116,27 +118,6 @@ def predict_benchmark( self, testing, do_unc=True ):
     	[mu_c, var_c, deriv_c] = gp.predict_benchmark(testing )
     	end = time.time()
     	cputime = end -start
-    	
-        #GPU predict
-        start = time.time()
-#        [mu_g, var_g, deriv_g] = gp.predict(testing, is_gpu = True, precision = np.float32, threshold = 1e5)
-    	end =time.time()
-    	gputime = end - start
-        print "%d\t%.2fs\t%.2fs\t%.2fx\t" % (Npredict, cputime, gputime, cputime/gputime),
-
-    
-    
-        # checking results
-#        try: 
-#            e_mu  = max(abs(mu_c - mu_g) ) / np.max(abs(mu_c))
-#            e_var = max(abs(var_c - var_g)) / np.max(abs(var_c))
-#            e_deriv = np.max(abs(deriv_c - deriv_g)) / np.max(abs(deriv_c))
-#        except ValueError:
-#            print 'Results have invalid data type or dimension.'
-#        if e_mu > 1e-5 or e_var > 1e-5 or e_deriv > 1e-5:
-#            print 'FAILED\t',
-#        else:
-#            print 'Pass\t',
-#        print 'e_mu=%.2g\te_var=%.2g\te_deriv=%.2g\t'%(e_mu, e_var, e_deriv)
-
+	print 'cputime', cputime
+	print '=================='
 

From 227276a97346d359eac67646e51c6bf157750027 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Mon, 6 Jul 2015 18:22:42 +0100
Subject: [PATCH 115/117] report

---
 doc/Gomez_Lewis.pdf | Bin 0 -> 223401 bytes
 doc/build.sh        |   1 +
 doc/kernel.png      | Bin 0 -> 24774 bytes
 doc/report.latex    |  48 +++++++++++++++++++++
 doc/report.md       | 100 ++++++++++++++++++++++++++++++++++++++++++++
 doc/report.pdf      | Bin 0 -> 233791 bytes
 doc/speedup.png     | Bin 0 -> 86970 bytes
 7 files changed, 149 insertions(+)
 create mode 100644 doc/Gomez_Lewis.pdf
 create mode 100755 doc/build.sh
 create mode 100644 doc/kernel.png
 create mode 100644 doc/report.latex
 create mode 100644 doc/report.md
 create mode 100644 doc/report.pdf
 create mode 100644 doc/speedup.png

diff --git a/doc/Gomez_Lewis.pdf b/doc/Gomez_Lewis.pdf
new file mode 100644
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HcmV?d00001

diff --git a/doc/build.sh b/doc/build.sh
new file mode 100755
index 0000000..e24c8de
--- /dev/null
+++ b/doc/build.sh
@@ -0,0 +1 @@
+pandoc report.md -o report.pdf -H report.latex
diff --git a/doc/kernel.png b/doc/kernel.png
new file mode 100644
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diff --git a/doc/report.latex b/doc/report.latex
new file mode 100644
index 0000000..daa08fc
--- /dev/null
+++ b/doc/report.latex
@@ -0,0 +1,48 @@
+\let\oldlongtable\longtable
+\def\longtable{\tiny \oldlongtable}
+\usepackage[svgnames]{xcolor}
+\usepackage{libertine}
+\usepackage{framed}
+\let\oldquote\quote
+\let\endoldquote\endquote
+\definecolor{verypalegreen}{RGB}{252,255,250}
+\colorlet{shadecolor}{verypalegreen}
+
+\makeatletter
+\def\shadequote{\begin{snugshade}\begin{oldquote}}
+\def\endshadequote{%
+  \end{oldquote}\end{snugshade}}
+\makeatother
+\renewenvironment{quote}{\begin{shadequote}}{\end{shadequote}}
+\usepackage{lettrine} % Package to accentuate the first letter of the text  
+\usepackage{fix-cm}	 % Custom font sizes - used for the initial letter in the document
+
+\definecolor{uclmidgreen}{RGB}{130,141,55}
+
+\newcommand{\initial}[1]{ % Defines the command and style for the first letter
+\lettrine[lines=3,lhang=0.3,nindent=0em]{
+\color{DarkGreen}
+{\textsf{#1}}}{}}
+
+\usepackage{sectsty} % Enables custom section titles
+\sectionfont{\color{uclmidgreen} \usefont{OT1}{phv}{b}{n}} % Change the font of all section commands
+\subsectionfont{\color{DarkSeaGreen} \usefont{OT1}{phv}{b}{n}} % Change the font of all section commands
+
+\usepackage{titling} % Allows custom title configuration 
+\newcommand{\HorRule}{\color{DarkSeaGreen} \rule{\linewidth}{1pt}} % Defines the gold horizontal rule around the title
+
+\pretitle{
+\vspace{-30pt} \begin{flushleft} \HorRule \fontsize{38}{38} \usefont{OT1}{phv}{b}{n} \color{uclmidgreen} \selectfont} % Horizontal rule before the title
+
+\posttitle{\par\end{flushleft}\begin{flushleft}\fontsize{25}{25} \usefont{OT1}{phv}{b}{n} \color{Black} GPU acceleration of Gaussian process emulator\selectfont \end{flushleft}\vskip 0.5em } % Whitespace under the title and subtitle
+
+\preauthor{\begin{flushleft}\large \lineskip 0.5em \usefont{OT1}{phv}{b}{sl} \color{uclmidgreen} Sinan Shi}% Author font configuration
+
+\postauthor{\footnotesize \usefont{OT1}{phv}{m}{sl} \color{Black} % Configuration for the institution name
+
+
+University College London% Your institution
+
+\par\end{flushleft}\HorRule} % Horizontal rule after the title
+
+\hypersetup{linkcolor=black}
diff --git a/doc/report.md b/doc/report.md
new file mode 100644
index 0000000..666ece3
--- /dev/null
+++ b/doc/report.md
@@ -0,0 +1,100 @@
+% Project Report
+# Introduction
+Land surface coverage data from satellite observation must be interpreted using physical models, which are computationally expansive. Using a surrogate Gaussian process (GP) emulator instead of complex physical models has been proved effective. The GP emulator is written in python, which is part of a python-based Earth Observation Land Data Assimilation System (EO-LDAS). However the current performance of the GP emulator is still far away from making it as a practical approach to process vast data from the satellites.
+
+A _predict_ function will be called recursively, which is the most expansive function in the emulator. The objective of this project is to investigate the performance behaviour of it on different platform (i.e. CPUs and GPUs in this case), and to provide an appropriate parallel solution to improve the performance. In this project, we have also developed a prototype GPU CUDA/C++ implementation of the _predict_ function, which has achieved 65x speedup on K20 GPU comparing to the python CPU implementation.
+
+
+# Python predict function
+## Code explanation
+
+The emulator calculates a hyper-parameters set first and uses these hyper-parameters to calculate the approximation of the input data, uncertainty and the associated gradient. Inside the emulator, the hyper-parameter calculation and the predict function are the most computational intensive part and meanwhile the most time-consuming. The difference between them is that the hyper-parameters are reusable. Once hyper-parameters have been calculated, it can be stored and be reused. It makes the impact of hyper-parameter calculation insignificant comparing to the _predict_ function. 
+
+As a result, we will only focus on the _predict_ function in this project. The _predict_ function receives an input data of (Npredict x Nparam) and the inverted hyper-parameter covariance array (Ntrain x Ntrain). The input data of the _predict_ function is a 2-D array that consists of multiple parameter vectors, which can be biophysical parameters or state variables (e.g. leaf area index). The number of these parameters (Nparam) is around 10 while the length of these parameter vectors (Npredict), according to the proposal, will reach 1e8. 
+
+## Performance analysis
+
+The original python predict function is wrapped in GaussianProcess class, which has around 30 lines with six major functions/operations. The python predict function consists of scipy and numpy functions. Interestingly, the most computational intensive functions are not the most time consuming ones. For example, the matrix multiplication costs same amount of time per call as the element wise array multiplication where the previous has one magnitude higher the computational complexity than the later one. This inconsistency is caused by different level of optimisation of scipy. If we take a look at the numpy source code, its matrix multiplication function has a C extension with CBLAS. That’s the reason why numpy matrix operations are also comparable to the C++ implementation [Figure 1]. 
+ 
+| Rank  | Operation                                         | Call  | Percentage of total wall time (%)     |
+|:----: |-------------------------------------------------- |:----: |:---------------------------------:    |
+|   1   | Array element wise multiplication (Matmul - Ele)  |  10   |               58.01                   |
+|   2   | Matrix vector subtraction (MvSub)                 |  10   |               15.08                   |
+|   3   | Array update: exp (Exp)                           |   3   |                9.71                   |
+|   4   | Matrix multiplication                             |   1   |                8.47                   |
+|   5   | Euclidean distance (Dist)                         |   1   |                5.54                   |
+|   6   | Matrix vector multiplication (Mv)                 |  10   |                3.00                   |
+|       | Total                                             |       |               99.81                   |
+
+On this table, we can see two different types of operations, matrix operations (linear algebra operations), and array element wise operations. The element wise operations take 88% of total CPU wall time. On the contrary, due to a good optimisation under numpy and less number of calling, the matrix operations take only 12% of total wall time. 
+
+
+## Comparison with C-BLAS implementation
+![Kernel Performance with Vector Size 2e6](kernel.png)
+C/C++ is regarded as one of the most efficient languages in general on CPU platforms. Before we go to the GPU, we would like to know how much potential does a CPU have. A very primitive C++ and BLAS implementation of the predict function has been carried out for this purpose. 
+
+All major functions except distance calculation have a performance improvement. The performance of the element wise array multiplication has been largely improved. However what we didn’t expect is that the distance function of C++ is even slower than that in python. The distance function in python is a function in scipy library. Like numpy matrix multiplication, the distance function in scipy also has a C extension beneath. On the other hand, the memory access pattern of our primitive C++ implementation is very bad. That’s the reason why the distance in C++ is even slower than scipy distance function. We will just keep it in mind, and will not do any further optimisation on it since this implementation is just to give us an idea about how fast can CPU achieve. The scipy distance performance can more or less represent the level of C/C++ implementation. 
+
+The result of the performance comparison shows the C++ predict function can be 1.3x faster than its python counterpart. If we ignore impact of the slowdown of distance function (in this case scipy can be regard as the highest performance on CPU), C++ implementation can achieve around 2.5x – 5x to original python implementation. The performance can vary from platforms, compilers and python versions. 
+
+
+# GPU implementation of GP-Emulator
+## CUDA/C++ implementation
+CUDA with C/C++ extension is one of the most common and straightforward ways of heterogeneous programming on CUDA (NVidia) device. The CUDA APIs enable programs to control the CUDA device and memory explicitly.  
+
+In spite that there is no standard python CUDA extension provided by NVidia, there are many third party APIs or wrappers that allow users to do heterogeneous programming in python, such as numbapro, theano, and pycuda.  They are usually easier to use, like theano, wrapped entirely the CUDA derivatives in python functions, leaving nothing for users to control the device explicitly. Some other APIs, such as Numbapro, which introduces not only wrapped functions, but also a CUDA dialect. In general, the third party APIs are easier to implement and easier to maintain but more difficult to control and their behaviour is more difficult to understand. 
+
+Good software should find a balance between the software sustainability and the performance. However in this project we are more interested in investigating the behaviour of GPU and exploiting the all the resource on it. The prototype that has been built in this project is the most straightforward C++/CUDA implementation linking with python by C-Python API. With the performance of straightforward CUDA, we can set a performance reference of GPU for future development.
+
+## Memory issues
+In the emulator, Npredict can reach 108. In the case that we have to at least hold two Npredict x Ntrain (~250) matrix (93G) at the same time, the GPU memory (~5G) cannot cope with such a large demand. Fortunately, elements inside each parameter vector are independent to each other; in other word, we can truncate the large input vectors (Npredict) into multiple pieces and feed them slice by slice to the _predict_ function. This feature of the emulator solves the memory scarcity issue and moreover enables multiple GPUs implementation in the future. We will go through this in the later sessions. The size of input vector we feed every time to the GPU predict function is 2x105(~400M).
+ 
+## Performance of GPU Kernels
+| Rank  | Operation     | Call  | Percentage of total wall time (%)     |
+|:----: |------------   |:----: |:---------------------------------:    |
+|   1   | MvSub         |  10   |                21.1                   |
+|   2   | Matmul-Ele    |  10   |                17.1                   |
+|   3   | Dist          |   1   |                16.3                   |
+|   4   | Mv            |  10   |                6.5                    |
+|   5   | Matmul        |   2   |                3.8                    |
+|   6   | Exp           |   1   |                0.1                    |
+|       | Total         |       |               99.81                   |
+
+To avoid GPU/CPU memory transferring overhead, the whole predict function instead of only major functions will be operated only on GPU. The GPU _predict_ is formed by multiple kernels, which correspond to its CPU counterparts. 
+
+On the one hand we made array element wise operations kernels on GPU. GPU is very good at dealing with these problems due to its embarrassingly parallel feature. On the other hand, cuBLAS has been implemented for the matrix operation component of the _predict_. 
+
+There are three major indicators to measure the GPU optimisation. The first is occupancy (active warp/ maximum warp) indicates if there are sufficient warp (32 threads) of GPU has been launched so that like memory latency can be covered; the rest two are the indicators of utilisation GPU resource including memory bandwidth (achieved bandwidth/device maximum bandwidth) and computation (flop achieved/maximum flops). 
+
+Let us examine the two most expansive kernels here. These element wise operations are always memory bound, i.e. each floating-point operation needs two times memory access. The memory bandwidth of both kernels has been largely saturated, i.e. utilisation of memory bandwidth is 85%, while the computation is relatively low, 20% - 35%, due to the nature of element wise operations. It means the computation units of GPU are not fully exploited, due to the latency. The occupancy of both kernels are around 75% which is already very high, it means the GPU has almost tried its best to overlap the latency by launching more concurrent threads. At this point, the further optimisation can only lie upon the improvement to memory access pattern so that the access of memory can be more efficient. 
+
+
+## Predict function performance 
+GPU doesn’t create a very large overhead in the predict function. Memory copy between device and host takes only 0.1% of total run time. The entire GPU wall time is only 65.8% of total wall time, which means these once very expansive kernels are no longer the major performance bottleneck anymore, instead the python part of the predict (34.2%), (mainly truncating and distributing data to feed GPU) becomes the new bottleneck. To optimise this part will be the first priority of future optimisation. 
+
+Conventionally, speedup is used as a metric to measure the performance gain of both serial and parallel optimisation. Strictly taken, one can only use it to compare the performance on the same platform. In heterogeneous programming, the speedup is just for giving an idea about the performance rather than a strict measurement, since the chose of different platform as a base can change entirely the final result. The speedup will be based on the performance of Intel Xeon 5500 CPU on Legion, which is the fastest CPU platform we get in this project. 
+
+The tests have been carried out on 3 nvidia GPU cards, M2070 on Legion (tests are not ready due to the temporary outage of legion GPU), M2090 and K20 on Emerald. K20 is in Kepler architecture GPU, which is the latest architecture of NVidia GPU, while M2070/90 Fermi. The most distinctive difference between them is that K20 has more than 4 times thread processors than M series. _predict_ function on K20 shows 3 times faster than of M series. It takes K20 875 second to finish the _predict_ function with the vector size of 1e8. 
+
+Since GPU is optimised on its single precision operations, the single precision _predict_ function is much faster than the double precision one. Both implementations have been applied in the source code. It is the user who to decide which precision to chose in light of the balance of the performance and the errors. 
+
+GPU Predict performance  
+
+![Kernel Performance with Vector Size 2e6](speedup.png)
+
+
+## Software development details
+
+The main objective of this project is to accelerate the GPU Gaussian Process predict function, which is a very small part of the code. There is no major change of the structure. The predict function C++/CUDA extension will be by cross platform compilation tool cmake which has also been integrated in the python setup. 
+
+Unit tests have been introduced to the program for the purpose of verifying the correctness of GPU implementation especially by comparing the GPU results with original python results. Tests are carried out recursively with the changing the lengths input vectors under the CUnit testing framework. 
+
+The code is compiled and tested every night on RSDT continuous integration system Jenkins.
+
+# Summary and the future work
+
+In this project, we have investigated the performance predict function in GP emulator. The most computational intensive operations, such as matrix multiplication and distance calculation are not necessary to be the most time consuming operation, due to its optimisation by scipy and numpy library. The major bottleneck of _predict_ function is the array element wise operations. A GPU implementation prototype has also been made, by porting element wise operations as kernels on GPU and using cuBLAS library to deal with matrix operations. It achieves 65x speedup when running on K20 GPU with single precision. 
+
+The further single GPU optimisation of the predict function should focus on data distribution part in python. Kernels on GPU still have some space to improve, if the memory access pattern can be improved. It will also be interesting to checkout the performance of some third party APIs such as Numbapro. Third party APIs can reduce largely the code complexity. In the future, developers can balance code sustainability and performance with the performance reference of the CUDA implementation. 
+
+With the base of single GPU, it seems to be very promising to have multiple GPUs implementation. First, a single GPU has already achieved a quite good performance. Second, the predict function is embarrassingly paralleled which means all inputs can be divided and distributed arbitrarily, as what we did in the single GPU case. Third, communications among GPU nodes will be relatively small, which occurs only at the beginning and in the end. Multiple GPUs implementation can fundamentally change the performance to meet the final scientific target. 
diff --git a/doc/report.pdf b/doc/report.pdf
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From 4f1fcd128ba55dc7732dbd717a362b2a2ff6f5a1 Mon Sep 17 00:00:00 2001
From: SinanShi <s.shi@ucl.ac.uk>
Date: Wed, 8 Jul 2015 16:22:02 +0100
Subject: [PATCH 116/117] modification of report

---
 doc/report.md  |  55 ++++++++++++++++++++++---------------------------
 doc/report.pdf | Bin 233791 -> 234456 bytes
 2 files changed, 25 insertions(+), 30 deletions(-)

diff --git a/doc/report.md b/doc/report.md
index 666ece3..5dfba6e 100644
--- a/doc/report.md
+++ b/doc/report.md
@@ -1,21 +1,20 @@
 % Project Report
 # Introduction
-Land surface coverage data from satellite observation must be interpreted using physical models, which are computationally expansive. Using a surrogate Gaussian process (GP) emulator instead of complex physical models has been proved effective. The GP emulator is written in python, which is part of a python-based Earth Observation Land Data Assimilation System (EO-LDAS). However the current performance of the GP emulator is still far away from making it as a practical approach to process vast data from the satellites.
+Land surface coverage data from satellite observations must be interpreted using physical models, which are computationally expansive. Using a surrogate Gaussian process (GP) emulator instead of complex physical models has been proved much more efficient. The GP emulator is written in python, which is part of a python-based Earth Observation Land Data Assimilation System (EO-LDAS). However the current performance of the GP emulator is still far away from ready to process vast data from the satellites.
 
-A _predict_ function will be called recursively, which is the most expansive function in the emulator. The objective of this project is to investigate the performance behaviour of it on different platform (i.e. CPUs and GPUs in this case), and to provide an appropriate parallel solution to improve the performance. In this project, we have also developed a prototype GPU CUDA/C++ implementation of the _predict_ function, which has achieved 65x speedup on K20 GPU comparing to the python CPU implementation.
+The performance bottleneck of the emulator is the  _predict_ function. The objective of this project is to provide an appropriate parallel solution for this predict function based on an investigate the performance of both CPU and GPU platforms. A prototype GPU CUDA/C++ implementation of the _predict_ function has been integrated to the python emulator, which has achieved 65x speedup on K20 GPU comparing to the python CPU implementation. 
 
 
 # Python predict function
 ## Code explanation
 
-The emulator calculates a hyper-parameters set first and uses these hyper-parameters to calculate the approximation of the input data, uncertainty and the associated gradient. Inside the emulator, the hyper-parameter calculation and the predict function are the most computational intensive part and meanwhile the most time-consuming. The difference between them is that the hyper-parameters are reusable. Once hyper-parameters have been calculated, it can be stored and be reused. It makes the impact of hyper-parameter calculation insignificant comparing to the _predict_ function. 
+The emulator calculates a hyper-parameters set first and uses these hyper-parameters to calculate the approximation of the parameters, uncertainty and the associated gradient by the _predict_ function. The _predict_ function has two inputs, first is the 2D array consists of Nparam (~10) parameter vectors (Npredict), such as biophysical parameters or state variables (e.g leaf area index) by satellites, and second the inverted hyper-parameter covariance array (Ntrain x Ntrain)(~250) provided by the emulator. The length of the input vectors can reach 1e8.
 
-As a result, we will only focus on the _predict_ function in this project. The _predict_ function receives an input data of (Npredict x Nparam) and the inverted hyper-parameter covariance array (Ntrain x Ntrain). The input data of the _predict_ function is a 2-D array that consists of multiple parameter vectors, which can be biophysical parameters or state variables (e.g. leaf area index). The number of these parameters (Nparam) is around 10 while the length of these parameter vectors (Npredict), according to the proposal, will reach 1e8. 
+The python _predict_ function is wrapped in GaussianProcess class, which has around 30 lines with six major numpy or scipy functions/operations of either matrix operations or array element wise operations. There are two features of  _predict_ that can benefit the GPU implementation, which is first, there is no branching; second, inside each parameter vector (~ 1e8), elements can be calculated independently, i.e. we can arbitrarily decide the size based on the performance tuning and the capability of GPU. We will go through this in the later sections.
 
 ## Performance analysis
+Tests have been carried out on Legion with Xeon 5500 CPU. Interestingly, according to the performance table below, the most computational intensive functions are not the most time consuming ones, e.g. the matrix multiplication costs the same amount of time per call as the element wise array multiplication where the previous one has one magnitude higher the computational complexity than the later one. This inconsistency is caused by a different level of optimisation of numpy/scipy. If we take a close look at the numpy source code, its matrix multiplication function has a C extension with CBLAS. That’s the reason why the performance numpy matrix operations is almost comparable to the C++ implementation. The element wise operations take 88% of total CPU wall time. On the contrary, due to a good optimisation under numpy and less number of calling, the matrix operations take only 12%. 
 
-The original python predict function is wrapped in GaussianProcess class, which has around 30 lines with six major functions/operations. The python predict function consists of scipy and numpy functions. Interestingly, the most computational intensive functions are not the most time consuming ones. For example, the matrix multiplication costs same amount of time per call as the element wise array multiplication where the previous has one magnitude higher the computational complexity than the later one. This inconsistency is caused by different level of optimisation of scipy. If we take a look at the numpy source code, its matrix multiplication function has a C extension with CBLAS. That’s the reason why numpy matrix operations are also comparable to the C++ implementation [Figure 1]. 
- 
 | Rank  | Operation                                         | Call  | Percentage of total wall time (%)     |
 |:----: |-------------------------------------------------- |:----: |:---------------------------------:    |
 |   1   | Array element wise multiplication (Matmul - Ele)  |  10   |               58.01                   |
@@ -26,16 +25,15 @@ The original python predict function is wrapped in GaussianProcess class, which
 |   6   | Matrix vector multiplication (Mv)                 |  10   |                3.00                   |
 |       | Total                                             |       |               99.81                   |
 
-On this table, we can see two different types of operations, matrix operations (linear algebra operations), and array element wise operations. The element wise operations take 88% of total CPU wall time. On the contrary, due to a good optimisation under numpy and less number of calling, the matrix operations take only 12% of total wall time. 
-
-
 ## Comparison with C-BLAS implementation
 ![Kernel Performance with Vector Size 2e6](kernel.png)
-C/C++ is regarded as one of the most efficient languages in general on CPU platforms. Before we go to the GPU, we would like to know how much potential does a CPU have. A very primitive C++ and BLAS implementation of the predict function has been carried out for this purpose. 
 
-All major functions except distance calculation have a performance improvement. The performance of the element wise array multiplication has been largely improved. However what we didn’t expect is that the distance function of C++ is even slower than that in python. The distance function in python is a function in scipy library. Like numpy matrix multiplication, the distance function in scipy also has a C extension beneath. On the other hand, the memory access pattern of our primitive C++ implementation is very bad. That’s the reason why the distance in C++ is even slower than scipy distance function. We will just keep it in mind, and will not do any further optimisation on it since this implementation is just to give us an idea about how fast can CPU achieve. The scipy distance performance can more or less represent the level of C/C++ implementation. 
+In general, C/C++ is regarded as one of the most efficient languages on CPU platforms. Before implementing the GPU _predict_, we would like to know how much potential does a CPU has on C/C++ with reasonable amount of effort. A very primitive C++ with CBLAS implementation has been carried out for this purpose. 
 
-The result of the performance comparison shows the C++ predict function can be 1.3x faster than its python counterpart. If we ignore impact of the slowdown of distance function (in this case scipy can be regard as the highest performance on CPU), C++ implementation can achieve around 2.5x – 5x to original python implementation. The performance can vary from platforms, compilers and python versions. 
+All major functions except distance calculation have a performance improvement. The performance of the element wise array multiplication has a significant improvement. However what we didn’t expect is that the distance function of C++ is even slower the python scipy _cdist_.
+The reason of that is maybe the bad memory access pattern obtained in the our distance function. We will just keep it in mind, and not do any further optimisation on it since this implementation is just to give us an idea about how fast can CPU achieve. The scipy distance performance can more or less represent the highest level of CPU performance with reasonable amount of effort. 
+
+The result of the performance comparison shows the C++ predict function can be 1.3x faster than its python counterpart. If we ignore impact of distance function, C++ implementation is around 2.5x – 5x faster than original python implementation. The performance can vary from platforms, compilers and python versions. 
 
 
 # GPU implementation of GP-Emulator
@@ -44,10 +42,10 @@ CUDA with C/C++ extension is one of the most common and straightforward ways of
 
 In spite that there is no standard python CUDA extension provided by NVidia, there are many third party APIs or wrappers that allow users to do heterogeneous programming in python, such as numbapro, theano, and pycuda.  They are usually easier to use, like theano, wrapped entirely the CUDA derivatives in python functions, leaving nothing for users to control the device explicitly. Some other APIs, such as Numbapro, which introduces not only wrapped functions, but also a CUDA dialect. In general, the third party APIs are easier to implement and easier to maintain but more difficult to control and their behaviour is more difficult to understand. 
 
-Good software should find a balance between the software sustainability and the performance. However in this project we are more interested in investigating the behaviour of GPU and exploiting the all the resource on it. The prototype that has been built in this project is the most straightforward C++/CUDA implementation linking with python by C-Python API. With the performance of straightforward CUDA, we can set a performance reference of GPU for future development.
+Good software should find a balance between the software sustainability and the performance. However in this project we are more interested in investigating the behaviour of GPU and exploiting the all the resource of it. CUDA can give us more freedom in tuning. The prototype has been made in C++/CUDA, integrated to python emulator with C-Python API. With the CUDA implementation, we can set a performance reference of future GPU development.
 
 ## Memory issues
-In the emulator, Npredict can reach 108. In the case that we have to at least hold two Npredict x Ntrain (~250) matrix (93G) at the same time, the GPU memory (~5G) cannot cope with such a large demand. Fortunately, elements inside each parameter vector are independent to each other; in other word, we can truncate the large input vectors (Npredict) into multiple pieces and feed them slice by slice to the _predict_ function. This feature of the emulator solves the memory scarcity issue and moreover enables multiple GPUs implementation in the future. We will go through this in the later sessions. The size of input vector we feed every time to the GPU predict function is 2x105(~400M).
+In the emulator, we have to at least hold two Npredict x Ntrain (~ 1e8 x 250) matrix (93G) at the same time. GPU memory (~5G) cannot cope with such a large demand. Fortunately, elements inside each parameter vector are independent to each other; in other word, we can truncate the large input vectors (Npredict) into multiple pieces and feed them slice by slice to the _predict_ function. This feature of the emulator solves the memory scarcity issue and moreover enables multiple GPUs implementation in the future. We will go through this in the later sessions. The size of input vector we feed every time to the GPU predict function is 2e5(~400M).
  
 ## Performance of GPU Kernels
 | Rank  | Operation     | Call  | Percentage of total wall time (%)     |
@@ -60,41 +58,38 @@ In the emulator, Npredict can reach 108. In the case that we have to at least ho
 |   6   | Exp           |   1   |                0.1                    |
 |       | Total         |       |               99.81                   |
 
-To avoid GPU/CPU memory transferring overhead, the whole predict function instead of only major functions will be operated only on GPU. The GPU _predict_ is formed by multiple kernels, which correspond to its CPU counterparts. 
-
-On the one hand we made array element wise operations kernels on GPU. GPU is very good at dealing with these problems due to its embarrassingly parallel feature. On the other hand, cuBLAS has been implemented for the matrix operation component of the _predict_. 
+To avoid GPU/CPU memory transferring overhead, the whole _predict_ has been migrated on GPU, rather than only computational intensive operations. The GPU _predict_ is formed by multiple kernels, which correspond to its CPU counterparts, i.e. six major operations.
 
-There are three major indicators to measure the GPU optimisation. The first is occupancy (active warp/ maximum warp) indicates if there are sufficient warp (32 threads) of GPU has been launched so that like memory latency can be covered; the rest two are the indicators of utilisation GPU resource including memory bandwidth (achieved bandwidth/device maximum bandwidth) and computation (flop achieved/maximum flops). 
+On the one hand we made array element wise operations kernels on GPU. GPU is very good at dealing with these problems due to its embarrassingly parallel feature. Meanwhile, cuBLAS has been implemented for the matrix operation component of the _predict_. 
 
-Let us examine the two most expansive kernels here. These element wise operations are always memory bound, i.e. each floating-point operation needs two times memory access. The memory bandwidth of both kernels has been largely saturated, i.e. utilisation of memory bandwidth is 85%, while the computation is relatively low, 20% - 35%, due to the nature of element wise operations. It means the computation units of GPU are not fully exploited, due to the latency. The occupancy of both kernels are around 75% which is already very high, it means the GPU has almost tried its best to overlap the latency by launching more concurrent threads. At this point, the further optimisation can only lie upon the improvement to memory access pattern so that the access of memory can be more efficient. 
+There are three major indicators to measure the GPU optimisation. First is occupancy (active warp/ maximum warp), which indicates if there are sufficient warp (32 threads) of GPU has been launched so that the latency may be covered; the rest two are the indicators of utilisation GPU resource including memory bandwidth (achieved bandwidth/device maximum bandwidth) and computation (flop achieved/maximum flops). 
 
+Array element wise operations still are the most time consuming part of the _predict_. Let us examine the two most expansive kernels (MvSub & Matmul-Ele) here. These kernels are memory bound, which means the memory operations are heavier than computation, i.e. each floating-point operation needs two times memory access. The memory bandwidth of both kernels has been largely saturated, i.e. utilisation of memory bandwidth is 85% respectively, while the computation is relatively low, 20% - 35%, due to its memory bound nature. It means the computation units of GPU are not fully exploited, due to the latency. The occupancy achieved by both kernels are very high, around 75%, which means the GPU has almost tried its best to overlap the latency by launching more concurrent threads. At this point, the further optimisation can only lie upon the improvement to memory access pattern so that the access of memory can be more efficient, which requires much more effort. 
 
 ## Predict function performance 
-GPU doesn’t create a very large overhead in the predict function. Memory copy between device and host takes only 0.1% of total run time. The entire GPU wall time is only 65.8% of total wall time, which means these once very expansive kernels are no longer the major performance bottleneck anymore, instead the python part of the predict (34.2%), (mainly truncating and distributing data to feed GPU) becomes the new bottleneck. To optimise this part will be the first priority of future optimisation. 
-
-Conventionally, speedup is used as a metric to measure the performance gain of both serial and parallel optimisation. Strictly taken, one can only use it to compare the performance on the same platform. In heterogeneous programming, the speedup is just for giving an idea about the performance rather than a strict measurement, since the chose of different platform as a base can change entirely the final result. The speedup will be based on the performance of Intel Xeon 5500 CPU on Legion, which is the fastest CPU platform we get in this project. 
+Based on the performance benchmark on Emerald K20 GPU. GPU doesn’t introduce a very large overhead in the _predict_. Memory copy between device and host takes only 0.1% of total run time. The entire GPU wall time is only 65.8% of total wall time, which means these once very expansive kernels are no longer the major performance bottleneck anymore, instead the python part of the _predict_ (34.2%), (mainly truncating and distributing data to feed GPU) becomes the new bottleneck. To optimise this part will be the first priority of future optimisation. 
 
-The tests have been carried out on 3 nvidia GPU cards, M2070 on Legion (tests are not ready due to the temporary outage of legion GPU), M2090 and K20 on Emerald. K20 is in Kepler architecture GPU, which is the latest architecture of NVidia GPU, while M2070/90 Fermi. The most distinctive difference between them is that K20 has more than 4 times thread processors than M series. _predict_ function on K20 shows 3 times faster than of M series. It takes K20 875 second to finish the _predict_ function with the vector size of 1e8. 
+Conventionally, speedup is used as a metric to measure the performance gain of both serial and parallel optimisation. But strictly taken, one can only use it to compare the performance on the same platform. In heterogeneous programming, the speedup is just for giving an idea about the performance rather than a strict measurement, since the chose of different platform as a base of comparison can change entirely the final result. The speedup in our project will be based on the performance of Intel Xeon 5500 CPU on Legion, which is the fastest CPU platform we get in this project. 
 
-Since GPU is optimised on its single precision operations, the single precision _predict_ function is much faster than the double precision one. Both implementations have been applied in the source code. It is the user who to decide which precision to chose in light of the balance of the performance and the errors. 
+The tests have been carried out on 3 nvidia GPU cards, M2070 on Legion (tests are not ready due to the temporary outage of legion GPU), M2090 and K20 on Emerald. K20 is a Kepler architecture GPU, which is the latest architecture of NVidia GPU, while M2070/90 is Fermi. The most distinctive difference between them is that K20 has more than 4 times thread processors than M series. _predict_ function on K20 shows 3 times faster than of M series. It takes K20 875 second to finish the _predict_ function with the vector size of 1e8. 
 
-GPU Predict performance  
+Since GPU is optimised on its single precision operations, the single precision _predict_ function is much faster than the double precision one. Both implementations have been applied in the source code which can be switched by the users in light of the the performance and the errors. 
 
 ![Kernel Performance with Vector Size 2e6](speedup.png)
 
 
 ## Software development details
 
-The main objective of this project is to accelerate the GPU Gaussian Process predict function, which is a very small part of the code. There is no major change of the structure. The predict function C++/CUDA extension will be by cross platform compilation tool cmake which has also been integrated in the python setup. 
+The main objective of this project is to accelerate the GPU Gaussian Process _predict_ function, which is a very small part of the code. There is no major change of the structure. The predict function C++/CUDA extension compilation is managed by a cross platform compilation tool cmake which has also been integrated in the python setup. 
 
 Unit tests have been introduced to the program for the purpose of verifying the correctness of GPU implementation especially by comparing the GPU results with original python results. Tests are carried out recursively with the changing the lengths input vectors under the CUnit testing framework. 
 
-The code is compiled and tested every night on RSDT continuous integration system Jenkins.
+The code is compiled and tested every overnightly on RSDT continuous integration system Jenkins.
 
 # Summary and the future work
 
-In this project, we have investigated the performance predict function in GP emulator. The most computational intensive operations, such as matrix multiplication and distance calculation are not necessary to be the most time consuming operation, due to its optimisation by scipy and numpy library. The major bottleneck of _predict_ function is the array element wise operations. A GPU implementation prototype has also been made, by porting element wise operations as kernels on GPU and using cuBLAS library to deal with matrix operations. It achieves 65x speedup when running on K20 GPU with single precision. 
+In this project, we have investigated the performance _predict_ in the GP emulator. The most computational intensive operations, such as matrix multiplication and distance calculation are not necessary to be the most time consuming operation on CPU, due to its optimisation by scipy and numpy library. The major bottleneck of _predict_ function is the array element wise operations which can be easily implemented on GPU.  A GPU implementation prototype has also been made, by porting element wise operations as kernels on GPU and using cuBLAS library to deal with matrix operations. It achieves 65x speedup when running on K20 GPU with single precision. 
 
-The further single GPU optimisation of the predict function should focus on data distribution part in python. Kernels on GPU still have some space to improve, if the memory access pattern can be improved. It will also be interesting to checkout the performance of some third party APIs such as Numbapro. Third party APIs can reduce largely the code complexity. In the future, developers can balance code sustainability and performance with the performance reference of the CUDA implementation. 
+The further single GPU optimisation of _predict_ should focus on data distribution part in python. Kernels on GPU still have some space to improve, if the memory access pattern can be improved. It will also be interesting to checkout the performance of some third party APIs such as Numbapro. Third party APIs can reduce the code complexity. In the future, developers can balance code sustainability and performance with the performance reference of the CUDA implementation. 
 
-With the base of single GPU, it seems to be very promising to have multiple GPUs implementation. First, a single GPU has already achieved a quite good performance. Second, the predict function is embarrassingly paralleled which means all inputs can be divided and distributed arbitrarily, as what we did in the single GPU case. Third, communications among GPU nodes will be relatively small, which occurs only at the beginning and in the end. Multiple GPUs implementation can fundamentally change the performance to meet the final scientific target. 
+With the base of single GPU, it seems to be very promising to have multiple GPUs implementation. First, the single GPU implementation has already achieved quite a good performance. Second, the predict function is embarrassingly paralleled which means all inputs can be divided and distributed arbitrarily, as what we did in the single GPU case, so to scale to multiple GPUs should not be difficult. Third, the communications among GPU nodes will be relatively small, which occurs only at the beginning and in the end. Multiple GPUs implementation can fundamentally change the performance to meet the final scientific target. 
diff --git a/doc/report.pdf b/doc/report.pdf
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From 5dddc0834346ca335f6035d40e85173c95285e8c Mon Sep 17 00:00:00 2001
From: sinanshi <shisinancn@gmail.com>
Date: Wed, 21 Oct 2015 13:57:25 +0100
Subject: [PATCH 117/117] restructuring cmake files, make double precision as
 an option

---
 CMakeLists.txt                 | 22 +++++++++++++++++++---
 gp_emulator/gpu/CMakeLists.txt | 11 -----------
 2 files changed, 19 insertions(+), 14 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6ce70b0..8fae155 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,13 +1,29 @@
 cmake_minimum_required (VERSION 2.8)
 
-
 include(cmake/LookUp-GreatCMakeCookOff.cmake)
-subdirs(gp_emulator/gpu)
 set(LIBRARY_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/build/lib)
 set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/tests)
 string(ASCII 27 Esc)
-#add_definitions(-DDOUBLE__PRECISION)
+
+option(double "Enable testing" off)
+
+if(double)
+  add_definitions(-DDOUBLE__PRECISION)
+endif()
+
 
 set(ColourReset "${Esc}[m")
 set(Red  "${Esc}[1;31m")
 message("${Red}export PYTHONPATH=$PYTHONPATH:${LIBRARY_OUTPUT_PATH} ${ColourReset}")
+
+find_package(Numpy REQUIRED)
+find_package(CoherentPython REQUIRED)
+find_package(CUDA QUIET REQUIRED)
+
+find_library(FOUND_CUBLAS cublas)
+include_directories(${PYTHON_INCLUDE_DIRS})
+include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
+
+set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
+
+subdirs(gp_emulator/gpu)
diff --git a/gp_emulator/gpu/CMakeLists.txt b/gp_emulator/gpu/CMakeLists.txt
index 69b912e..b4246d7 100644
--- a/gp_emulator/gpu/CMakeLists.txt
+++ b/gp_emulator/gpu/CMakeLists.txt
@@ -1,16 +1,5 @@
 cmake_minimum_required (VERSION 2.8)
 
-find_package(Numpy REQUIRED)
-find_package(CoherentPython REQUIRED)
-find_package(CUDA QUIET REQUIRED)
-
-find_library(FOUND_CUBLAS cublas)
-include_directories(${PYTHON_INCLUDE_DIRS})
-include_directories(${NUMPY_INCLUDE_DIRS}/numpy)
-
-
-set(CUBLAS_TARGET_LINK ${FOUND_CUBLAS})
-
 cuda_add_library(_gpu_predict  _gpu_predict.cpp 
                  predict.cu
                  computeTranspose.cu