Kubernetes-native framework for declarative testing of distributed systems.
Extra-P, automated performance modeling for HPC applications
The purpose of this project is to analyse the overall execution time, load time, output time and communication time obtained in an Apache-Spark Application by increasing the number of Workers, Cores per Worker and Dataset Size.
High-Performance Computing project: stencil computation on 2D matrices using MPI and OpenMP, benchmarked on CINECA's Galileo100 supercomputer
Rigid Body Simulation experiment at Computer Animation (slo. Računalniška Animacija)
Distributed vs. centralized ML for stroke prediction using Apache Spark. Benchmarks a per-partition scikit-learn ensemble (mapPartitions) against MLlib Random Forest, Logistic Regression, and Decision Tree models. Includes speed-up, size-up, and scale-up scalability analysis on a 43K-row clinical dataset.
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