Multi-Objective region-Aware optimization of parallel programs

摘要

Auto-tuning has become increasingly popular for optimizing non-functional parameters of parallel programs. The typically large search space requires sophisticated techniques to find well-performing parameter values in a reasonable amount of time. Different parts of a program often perform best with different parameter values. We therefore subdivide programs into several regions, and try to optimize the parameter values for each of these regions separately as opposed to setting the parameter values globally for the entire program. In order to manage this enlarged search space, we have to extend existing auto-tuning techniques to ensure high quality solutions to this optimization problem. In this paper we introduce a novel enhancement to the RS-GDE3 algorithm that is used to explore the search space for auto-tuning programs with multiple regions regarding several objectives. We have implemented our auto-tuner using the Insieme compiler and runtime system, and provide a detailed analysis of the obtained results with the aim of gaining a better understanding of non-functional inter-region behavior in the context of auto-tuning. In comparison to a non-optimized parallel version of the tested programs, our novel approach achieves improvements of up to 7.6X, 10.5X, and 61.6X for three tuned objectives wall time, energy consumption, and resource usage, respectively. (C) 2018 Elsevier B.V. All rights reserved.