Replication Data for: Automatic Tuning based on Hardware Performance Counters and Machine Learning

This dataset contains Hardware Performance Counters (HwPCs) measurements collected from parallel code regions executing on heterogeneous High Performance Computing platforms. The dataset includes comprehensive HwPC data from CPU architectures (Intel Xeon E5645 and Intel Xeon E5-4620) and GPU architectures (NVIDIA GeForce GTX 680 Kepler, GTX 750 Maxwell, GTX 1070 Pascal, RTX 2080 Turing, RTX 3090 Ampere, and RTX 4080 Ada Lovelace). Data was collected from multiple benchmark suites including STREAM, PolyBench, NAS Parallel Benchmarks, and GPU kernels (Convolution, Coulomb Sum, N-body, Transposition, GEMM, Reduction, Biconjugate Gradient, and Hotspot). The dataset encompasses measurements across varying problem sizes, thread configurations, affinity policies, scheduling strategies, and chunk sizes for OpenMP regions, as well as tuning parameters for GPU kernels including work-group size, work-item coarsening, memory caching strategies, tile sizes, loop unrolling, and vectorization. This data supports machine learning-based optimization of parallel applications through automated selection of minimal Hardware Performance Counter sets for code region identification and tuning parameter optimization.