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MLKAPS: Machine Learning and Adaptive Sampling for HPC Kernel Auto-tuning
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DOI：10.1145/3774418.png)
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Abstract
En 中文
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 article 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 (R) 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 x1.31. On the Intel (R) MKL dgeqrf QR kernel, MLKAPS improves performance on 85% of the inputs with a geomean speedup of x1.18.
Keywords:
Autotuning
input-aware optimization
math library
Journal
A
IF:
1.8
Papers:
96
Citations:
1.1K