Author retrospective for adaptive reduction parallelization techniques

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

Abstract

Modern applications are dynamic and input dependent and algorithm performance is input and environment sensitive. This potential mismatch between algorithmic choice and performance is exacerbated in the case of parallel programs because the penalty for less than optimal locality grows with the size of the machine. Reductions, e.g., map-reduce are one of the most important algorithms used in parallel codes are also input sensitive. This led us to develop an adaptive framework that used a statistical method to learn how to select the best algorithm for every execution instance. We applied it to parallel reduction algorithm selection. The importance of better reduction methods as well as adaptive selection methods has only increased since the time this paper was first published. Copyright

Original languageEnglish (US)
Title of host publicationICS 2014 - Proceedings of the 28th ACM InternationaI Conference on Supercomputing
EditorsUtpal Banerjee
PublisherAssociation for Computing Machinery
Pages59-60
Number of pages2
ISBN (Electronic)9781450328401
DOIs
StatePublished - Jun 10 2014
Externally publishedYes
Event25th ACM International Conference on Supercomputing, ICS 2014 - Munich, Germany
Duration: Jun 10 2014Jun 13 2014

Publication series

NameProceedings of the International Conference on Supercomputing

Other

Other25th ACM International Conference on Supercomputing, ICS 2014
CountryGermany
CityMunich
Period6/10/146/13/14

Keywords

  • Adaptive algorithms
  • Machine learning
  • Matrix multiplication
  • Parallel algorithms
  • Reductions
  • Sorting

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Yu, H., & Rauchwerger, L. (2014). Author retrospective for adaptive reduction parallelization techniques. In U. Banerjee (Ed.), ICS 2014 - Proceedings of the 28th ACM InternationaI Conference on Supercomputing (pp. 59-60). (Proceedings of the International Conference on Supercomputing). Association for Computing Machinery. https://doi.org/10.1145/2591635.2591661