MLKAPS: Machine Learning and Adaptive Sampling for HPC Kernel Auto-tuning
Résumé
Many High-Performance Computing (HPC) libraries rely on decision trees to select the best kernel hyperparameters at runtime,
depending on the input and environment. However, finding optimized configurations for each input and environment is challenging
and requires significant manual effort and computational resources. This paper presents MLKAPS, a tool that automates this task using
machine learning and adaptive sampling techniques. MLKAPS generates decision trees that tune HPC kernels’ design parameters to
achieve efficient performance for any user input. MLKAPS scales to large input and design spaces, outperforming similar state-of-the-art
auto-tuning tools in tuning time and mean speedup. We demonstrate the benefits of MLKAPS on the highly optimized Intel MKL
dgetrf LU kernel and show that MLKAPS finds blindspots in the manual tuning of HPC experts. It improves over 85% of the inputs
with a geomean speedup of ×1.30. On the Intel MKL dgeqrf QR kernel, MLKAPS improves performance on 85% of the inputs with a
geomean speedup of ×1.18.
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