Multi-Objective Auto-Tuning with Insieme: Optimization and Trade-Off Analysis for Time, Energy and Resource Usage

Abstract

AbstractThe increasing complexity of modern multi- and many-core hardware design makes performance tuning of parallel applications a difficult task. In the past, auto-tuners have been successfully applied to minimize execution time. However, besides execution time, additional optimization goals have recently arisen, such as energy consumption or computing costs. Therefore, more sophisticated methods capable of exploiting and identifying the trade-offs among these goals are required. In this work we present and discuss results of applying a multi-objective search-based auto-tuner to optimize for three conflicting criteria: execution time, energy consumption, and resource usage. We examine a method, called RS-GDE3, to tune HPC codes using the Insieme parallelizing and optimizing compiler. Our results demonstrate that RS-GDE3 offers solutions of superior quality than those provided by a hierarchical and a random search at a fraction of the required time (5%) or energy (8%). A comparison to a state-of-the-art multi-objective optimizer (NSGA-II) shows that RS-GDE3 computes solutions of higher quality. Finally, based on the trade-off solutions found by RS-GDE3, we provide a detailed analysis and several hints on how to improve the design of multi-objective auto-tuners and code optimization.KeywordsExecution TimeResource UsageRandom SearchTarget PlatformTile SizeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.