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Benchmarking Optimization Algorithms for Auto-Tuning GPU Kernels by Richard Arnoud Schoonhoven; Ben van Werkhoven; Kees Joost Batenburg is a Computer Science article available to read on EtoBox.

What is Benchmarking Optimization Algorithms for Auto-Tuning GPU Kernels about?

Recent years have witnessed phenomenal growth in the application, and capabilities of Graphical Processing Units (GPUs) due to their high parallel computation power at relatively low cost. However, writing a computationally efficient GPU program (kernel) is challenging, and generally only certain specific kernel configurations lead to significant increases in performance. Auto-tuning is the process of automatically optimizing software for highly-efficient execution on a target hardware platform. Auto-tuning is particularly useful for GPU programming, as a single kernel requires re-tuning after code changes, for different input data, and for different architectures. However, the discrete, and non-convex nature of the search space creates a challenging optimization problem. In this work, we investigate which algorithm produces the fastest kernels if the time-budget for the tuning task is varied. We conduct a survey by performing experiments on 26 different kernel spaces, from 9 different GPUs, for 16 different evolutionary blackbox optimization algorithms. We then analyze these results and introduce a novel metric based on the PageRank centrality concept as a tool for gaining insight

Who reads Benchmarking Optimization Algorithms for Auto-Tuning GPU Kernels?

Author

Richard Arnoud Schoonhoven; Ben van Werkhoven; Kees Joost Batenburg

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)