Tracea: Mathematically Unified Universal GPU Optimization 🏛️✨

Tracea is a production-grade universal GPU kernel optimization framework. It transforms high-level Semantic IR into mathematically verified, high-performance kernels for NVIDIA (CUDA), AMD (ROCm), Apple (Metal), and CPU. By leveraging asynchronous pipelining, Tensor Core acceleration, and Bayesian auto-tuning, Tracea achieves "Zero-Configuration" peak performance across platforms.

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🚀 Latest Benchmark Results (v3.2)

Operation	Hardware	Performance	Notes
GEMM	RTX 3070	>20 TFLOPS	Tensor Core MMA, 2-stage pipeline
Low-Rank MLP	RTX 3070	18.2 TFLOPS	Policy Engine Guided, TTG Dispatch
Conv2d	RTX 3070	22.73 TFLOPS	Implicit GEMM, 3-stage Pipeline, 78% Peak
ResNet Block	Apple M1	17.65 ms	Fused (Conv+BN+ReLU), Metal Backend
Fork & Join	Apple M1	12.37 ms	Fused (Conv+Add), Zero-Copy
Classif. Head	Apple M1	0.61 ms	Fused Epilogue, Fused Attention Ready

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🆕 v3.2 Features

Topological Tile Graph (TTG) & Policy Engine

• Dynamic Execution Planning: Policy Engine generates optimized execution strategies based on hardware profiles and model topology.
• Topological Tile Graph (TTG): A unified representation for sparse and dense tiling, enabling zero-copy sparse execution.
• Architecture-Aware Dispatch: Automated kernel selection (GEMM, Low-Rank MLP, Attention) via Policy decisions.

Implicit GEMM Convolution Engine

• Zero-im2col Architecture: Coordinate mapping via fast-divmod primitives.
• Hybrid Hoisting: Adaptive SMEM management for coordinate tables.
• Fused Epilogue Pipeline: One-shot BiasAdd + SiLU/ReLU + Residual integration.

mbarrier Integration

• Asynchronous GPU pipelining with producer-consumer warp roles.

Python Integration (tracea-python)

import tracea
# Zero-copy PyTorch backend with automatic fallback
tracea.patch_conv2d()

C++ FFI (tracea-ffi)

#include "tracea.hpp"
tracea::conv2d(x, w, nullptr, out, params);

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🚀 Key Features

• Multi-Backend Excellence: Native support for CUDA, ROCm (HIP), Metal, and CPU (SIMD).
• Tracea Doctor: Intelligent environment diagnostic tool that profiles hardware capabilities and selects the optimal kernel variant.
• Persistent Bayesian Auto-Tuning: Hardware-aware search using Gaussian Processes with intelligent caching.
• Zero-Copy Integration: Python (PyTorch) and C++ bindings with borrowed pointer semantics.
• Graph-Level Intelligence: Optimize entire sequences of operations using priority-based scheduling.

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📦 Installation

Rust Library

cargo build --lib --release

Python Module

cd tracea-python && maturin develop

C++ FFI

cd tracea-ffi && cargo build --release
# Link against target/release/libtracea_ffi.a

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🐍 Python Usage

import tracea

# Create context (auto-detects hardware via Doctor)
ctx = tracea.Context()

# Build and optimize a graph
graph = tracea.Graph()
graph.add_gemm(m=2048, n=2048, k=2048)
config = ctx.optimize_graph(graph)

Monkey Patch for PyTorch

from tracea import patch_conv2d
patch_conv2d()  # Replaces torch.nn.Conv2d

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🔧 C++ Usage

#include "tracea.hpp"

tracea::TensorView x(x_ptr, {1, 64, 224, 224}, tracea::DType::Float32, 0);
tracea::TensorView w(w_ptr, {128, 64, 3, 3}, tracea::DType::Float32, 0);
tracea::TensorView out(out_ptr, {1, 128, 222, 222}, tracea::DType::Float32, 0);

tracea::Conv2dParams params;
params.stride_h = params.stride_w = 1;
params.stream = my_cuda_stream;

tracea::conv2d(x, w, nullptr, out, params);

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📂 Project Structure

src/
├── core/           # IR definitions, TTG types
├── semantic/       # Semantic IR, tiling abstractions
├── backend/        # Multi-backend hardware abstractions
├── runtime/        # TTG Builder, Runtime Manager, Device buffers
├── kernels/        # CUDA/ROCm/Metal/CPU implementations
├── policy/         # Policy Engine, Tiling/Exec policies
├── optimizer/      # Bayesian Tuner, Hero configs
├── emitter/        # Universal Code Generators
├── doctor/         # Multi-backend diagnostics
└── optimized/      # Pre-compiled high-performance variants

tracea-python/      # PyO3 Python extension
tracea-ffi/         # C ABI for C++ integration
docs/               # Architecture and usage guides
examples/           # TTG sparse/low-rank demonstration code

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📄 Documentation

• API Usage
• Architecture
• Optimization Guide
• Benchmark Report
• Developer Guide

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Tracea IS a "meaning-understanding optimization engine." 🏛️🚀