The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop:PMBS21: The 12th International Workshop on Performance Modeling, Benchmarking and Simulation of High-Performance Computer Systems
Authors: Floris-Jan Q. Willemsen and Rob V. van Nieuwpoort (Netherlands eScience Center, University of Amsterdam) and Ben van Werkhoven (Netherlands eScience Center)
Abstract: Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise for large search spaces, even when automated. This poses an optimization task on a non-convex search space, using an expensive-to-evaluate function with unknown derivative. These characteristics make a good candidate for Bayesian optimization, which previously has not been applied to this problem.
The application of Bayesian optimization to this problem, however, is challenging. We demonstrate how to deal with the rough, discrete, constrained search spaces, containing invalid configurations. We introduce a novel contextual variance exploration factor, as well as new acquisition functions with improved scalability, combined with an informed acquisition function selection mechanism. By comparing the performance of our Bayesian optimization implementation on various test cases to the existing search strategies in Kernel Tuner, as well as other Bayesian optimization implementations, we demonstrate that our search strategies generalize well and consistently outperform other search strategies by a wide margin.