Quantifying data locality in dynamic parallelism in GPUs

Xulong Tang, Ashutosh Pattnaik, Onur Kayiran, Adwait Jog, Mahmut Kandemir, Chitaranjan Das

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

2 Scopus citations

Abstract

Dynamic parallelism (DP) is a new feature of emerging GPUs that allows new kernels to be generated and scheduled from the deviceside (GPU) without the host-side (CPU) intervention. To eiciently support DP, one of the major challenges is to saturate the GPU processing elements and provide them with the required data in a timely fashion. In this paper, we irst conduct a limit study on the performance improvements that can be achieved by hardware schedulers that are provided with accurate data reuse information. We next propose LASER, a Locality-Aware SchedulER, where the hardware schedulers employ data reuse monitors to help make scheduling decisions to improve data locality at runtime. Experimental results on 16 benchmarks show that LASER, on an average, can improve performance by 11.3%.

Original languageEnglish (US)
Title of host publicationSIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
PublisherAssociation for Computing Machinery, Inc
Pages25-26
Number of pages2
ISBN (Electronic)9781450366786
DOIs
StatePublished - Jun 20 2019
Event14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019 - Phoenix, United States
Duration: Jun 24 2019Jun 28 2019

Publication series

NameSIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems

Conference

Conference14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019
CountryUnited States
CityPhoenix
Period6/24/196/28/19

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Computational Theory and Mathematics

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