Characteristics of workloads using the pipeline programming model

Christian Bienia, Kai Li

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

15 Scopus citations

Abstract

Pipeline parallel programming is a frequently used model to program applications on multiprocessors. Despite its popularity, there is a lack of studies of the characteristics of such workloads. This paper gives an overview of the pipeline model and its typical implementations for multiprocessors. We present implementation choices and analyze their impact on the program. We furthermore show that workloads that use the pipeline model have their own unique characteristics that should be considered when selecting a set of benchmarks. Such information can be beneficial for program developers as well as for computer architects who want to understand the behavior of applications.

Original languageEnglish (US)
Title of host publicationComputer Architecture - ISCA 2010 International Workshops, A4MMC, AMAS-BT, EAMA, WEED, WIOSCA, Revised Selected Papers
Pages161-171
Number of pages11
DOIs
StatePublished - Mar 8 2012
EventACM IEEE International Symposium on Computer Architecture, ISCA 2011 - Saint-Malo, France
Duration: Jun 19 2010Jun 23 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6161 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherACM IEEE International Symposium on Computer Architecture, ISCA 2011
CountryFrance
CitySaint-Malo
Period6/19/106/23/10

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Bienia, C., & Li, K. (2012). Characteristics of workloads using the pipeline programming model. In Computer Architecture - ISCA 2010 International Workshops, A4MMC, AMAS-BT, EAMA, WEED, WIOSCA, Revised Selected Papers (pp. 161-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6161 LNCS). https://doi.org/10.1007/978-3-642-24322-6_14