Load-TriggeredWarp approximation on GPU

Zhenhong Liu, Daniel Wong, Nam Sung Kim

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

Abstract

Value similarity of operands across warps have been exploited to improve energy efficiency of GPUs. Prior work, however, incurs significant overheads to check value similarity for every instruction and does not improve performance as it does not reduce the number of executed instructions. This work proposes Lock 'n Load (LnL) which triggers approximate execution of code regions by only checking similarity of values returned from load instructions and fuses multiple approximated warps into a single warp.

Original languageEnglish (US)
Title of host publicationISLPED 2018 - Proceedings of the 2018 International Symposium on Low Power Electronics and Design
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781450357043
DOIs
StatePublished - Jul 23 2018
Event23rd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2018 - Bellevue, United States
Duration: Jul 23 2018Jul 25 2018

Publication series

NameProceedings of the International Symposium on Low Power Electronics and Design
ISSN (Print)1533-4678

Other

Other23rd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2018
CountryUnited States
CityBellevue
Period7/23/187/25/18

Keywords

  • Approximate Computing
  • GPU

ASJC Scopus subject areas

  • Engineering(all)

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