Parallelism orchestration using DoPE: The degree of parallelism executive

Arun Raman, Hanjun Kim, Taewook Oh, Jae W. Lee, David I. August

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Scopus citations

Abstract

In writing parallel programs, programmers expose parallelism and optimize it to meet a particular performance goal on a single platform under an assumed set of workload characteristics. In the field, changing workload characteristics, new parallel platforms, and deployments with different performance goals make the programmer's development-time choices suboptimal. To address this problem, this paper presents the Degree of Parallelism Executive (DoPE), an API and run-time system that separates the concern of exposing parallelism from that of optimizing it. Using the DoPE API, the application developer expresses parallelism options. During program execution, DoPE's run-time system uses this information to dynamically optimize the parallelism options in response to the facts on the ground. We easily port several emerging parallel applications to DoPE's API and demonstrate the DoPE run-time system's effectiveness in dynamically optimizing the parallelism for a variety of performance goals.

Original languageEnglish (US)
Title of host publicationPLDI'11 - Proceedings of the 2011 ACM Conference on Programming Language Design and Implementation
PublisherAssociation for Computing Machinery
Pages26-37
Number of pages12
ISBN (Print)9781450306638
DOIs
StatePublished - 2011
Event32nd ACM Conference on Programming Language Design and Implementation, PLDI 2011 - San Jose, United States
Duration: Jun 4 2011Jun 8 2011

Publication series

NameProceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)

Conference

Conference32nd ACM Conference on Programming Language Design and Implementation, PLDI 2011
Country/TerritoryUnited States
CitySan Jose
Period6/4/116/8/11

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • dynamic
  • loop nest
  • loop-level
  • nested
  • optimization
  • parallelism
  • parallelization
  • parametric
  • pipeline
  • run-time
  • scheduling
  • task

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