Exploiting performance portability in search algorithms for autotuning

Amit Roy, Prasanna Balaprakash, Paul D. Hovland, Stefan M. Wild

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

12 Scopus citations

Abstract

Autotuning seeks the best configuration of an application by orchestrating hardware and software knobs that affect performance on a given machine. Autotuners adopt various search techniques to efficiently find the best configuration, but they often ignore lessons learned on one machine when tuning for another machine. We demonstrate that a surrogate model built from performance results on one machine can speedup the autotuning search by 1.6X to 130X on a variety of modern architectures.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1535-1544
Number of pages10
ISBN (Electronic)9781509021406
DOIs
StatePublished - Jul 18 2016
Externally publishedYes
Event30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 - Chicago, United States
Duration: May 23 2016May 27 2016

Publication series

NameProceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016

Conference

Conference30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016
Country/TerritoryUnited States
CityChicago
Period05/23/1605/27/16

Keywords

  • Autotuning
  • Empirical search heuristics
  • Performance portability

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