Compar: Optimized multi-compiler for automatic openmp s2s parallelization

Idan Mosseri, Lee Or Alon, Re’Em ’E Harel, Gal Oren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Parallelization schemes are essential in order to exploit the full benefits of multi-core architectures, which have become widespread in recent years. In shared-memory architectures, the most comprehensive parallelization API is OpenMP. However, the introduction of correct and optimal OpenMP parallelization to applications is not always a simple task, due to common parallel shared-memory management pitfalls, architecture heterogeneity and the current necessity for human expertise in order to comprehend many fine details and abstract correlations. To ease this process, many automatic parallelization compilers were created over the last decade. [2] tested several source-to-source compilers and concluded that each has its advantages and disadvantages and no compiler is superior to all other compilers in all tests. This indicates that a fusion of the compilers’ best outputs under the best hyper-parameters for the current hardware setups can yield greater speedups. To create such a fusion, one should execute a computationally intensive hyper-parameter sweep, in which the performance of each option is estimated and the best option is chosen. We created a novel parallelization source-to-source multi-compiler named ComPar, which uses code segmentation-and-fusion with hyper-parameters tuning to achieve the best parallel code possible without any human intervention while maintaining the program’s validity. In this paper we present ComPar and analyze its results on NAS and PolyBench benchmarks. We conclude that although the resources ComPar requires to produce parallel code are greater than other source-to-source parallelization compilers – as it depends on the number of parameters the user wishes to consider, and their combinations – ComPar achieves the best performance overall compared to the serial code version and other tested parallelization compilers. ComPar is publicly available at: https://github.com/Scientific-Computing-Lab-NRCN/compar.

Original languageEnglish
Title of host publicationOpenMP
Subtitle of host publicationPortable Multi-Level Parallelism on Modern Systems - 16th International Workshop on OpenMP, IWOMP 2020, Proceedings
EditorsKent Milfeld, Lars Koesterke, Bronis R. de Supinski, Jannis Klinkenberg
PublisherSpringer Science and Business Media Deutschland GmbH
Pages247-262
Number of pages16
ISBN (Print)9783030581435
DOIs
StatePublished - 1 Jan 2020
Event16th International Workshop on OpenMP, IWOMP 2020 - Austin, United States
Duration: 22 Sep 202024 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12295 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Workshop on OpenMP, IWOMP 2020
Country/TerritoryUnited States
CityAustin
Period22/09/2024/09/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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