Auto-tuning Parameter Choices in HPC Applications using Bayesian Optimization

Abstract

High performance computing applications, runtimes, and platforms are becoming more configurable to enable applications to obtain better performance. As a result, users are increasingly presented with a multitude of options to configure application-specific as well as platform-level parameters. The combined effect of different parameter choices on application performance is difficult to predict, and an exhaustive evaluation of this combinatorial parameter space is practically infeasible. One approach to parameter selection is a user-guided exploration of a part of the space. However, such an ad hoc exploration of the parameter space can result in suboptimal choices. Therefore, an automatic approach that can efficiently explore the parameter space is needed. In this paper, we propose HiPerBOt, a Bayesian optimization based configuration selection framework to identify application and platform-level parameters that result in high performing configurations. We demonstrate the effectiveness of HiPerBOt in tuning parameters that include compiler flags, runtime settings, and application-level options for several parallel codes, including, Kripke, Hypre, LULESH, and OpenAtom.