Autotuning in High-Performance Computing Applications

TitleAutotuning in High-Performance Computing Applications
Publication TypeJournal Article
Year of Publication2018
AuthorsBalaprakash, P., J. Dongarra, T. Gamblin, M. Hall, J. Hollingsworth, B. Norris, and R. Vuduc
JournalProceedings of the IEEE
Volume106
Issue11
Pagination2068–2083
Date Published11-2018
KeywordsHigh-performance computing, performance tuning programming systems
Abstract

Autotuning refers to the automatic generation of a search space of possible implementations of a computation that are evaluated through models and/or empirical measurement to identify the most desirable implementation. Autotuning has the potential to dramatically improve the performance portability of petascale and exascale applications. To date, autotuning has been used primarily in high-performance applications through tunable libraries or previously tuned application code that is integrated directly into the application. This paper draws on the authors' extensive experience applying autotuning to high-performance applications, describing both successes and future challenges. If autotuning is to be widely used in the HPC community, researchers must address the software engineering challenges, manage configuration overheads, and continue to demonstrate significant performance gains and portability across architectures. In particular, tools that configure the application must be integrated into the application build process so that tuning can be reapplied as the application and target architectures evolve.

DOI10.1109/JPROC.2018.2841200
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