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gpu-kernel-performanceHere are 6 public repositories matching this topic...Language: All
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Runs a single CUDA/OpenCL kernel, taking its source from a file and arguments from the command-line
gpu opencl cuda runner gpgpu multi-language performance-analysis profiling performance-testing debugging-tool gpu-kernels gpu-kernel-performance
Sparse Matrix and Tensor Visualization Tool for Reorderings
performance-engineering sparse-data performance-visualization performance-monitoring matrix-visualizer tensor-visualization gpu-kernel-performance matrix-ordering
Inference Optimization by Economic Kernel Search
As part of this project, various classification algorithms like SVM, Decision Trees and XGBoost was used to classify a GPU Run as high or low time consuming process. The main purpose of this project is to test and compare the predictive capabilities of different classification algorithms
python numpy svm matrix scikit-learn machine-learning-algorithms cross-validation pandas seaborn matrix-multiplication matplotlib performance-analysis decision-trees svm-kernel classification-algorithms k-fold-cross-validation gpu-kernel-performance xgboost-algorithms
GPU Gaussian kernel density estimation
A C++/CUDA inference engine for Gemma 4 26B A4B optimised for 24GB consumer GPUs (NVIDIA RTX 3090 / 4090).
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