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
-
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
-
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
-
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
Cited by 17 stored of 17
17 results