Benchmarking Optimization Algorithms for Auto-Tuning GPU Kernels

IEEE Transactions on Evolutionary Computation, vol. 27, pp. 550–564

Abstract

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 retuning after code changes, for different input data, and for different architectures. However, the discrete and nonconvex 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 nine different GPUs, for 16 different evolutionary black-box optimization algorithms. We then analyze these results and introduce a novel metric based on the PageRank centrality concept as a tool for gaining insight into the difficulty of the optimization problem. We demonstrate that our metric correlates strongly with the observed tuning performance.

Authors 3

1. Richard Schoonhoven Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging Group, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

2. Ben van Werkhoven

   Netherlands eScience Center · Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging Group, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

   Netherlands eScience Center, Amsterdam, The Netherlands

3. Kees Joost Batenburg Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging Group, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands