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Quantifying Data Locality in Dynamic Parallelism in GPUs

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Published:17 December 2019Publication History
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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%.

References

  1. Xulong Tang, Ashutosh Pattnaik, Huaipan Jiang, Onur Kayiran, Adwait Jog, Sreepathi Pai, Mohamed Ibrahim, Mahmut Kandemir, and Chita Das. 2017. Controlled Kernel Launch for Dynamic Parallelism in GPUs. In HPCA.Google ScholarGoogle Scholar
  2. JinWang, Norm Rubin, Albert Sidelnik, and Sudhakar Yalamanchili. 2016. LaPerm: Locality Aware Scheduler for Dynamic Parallelism on GPUs. In ISCA.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM SIGMETRICS Performance Evaluation Review
    ACM SIGMETRICS Performance Evaluation Review  Volume 47, Issue 1
    June 2019
    100 pages
    ISSN:0163-5999
    DOI:10.1145/3376930
    Issue’s Table of Contents

    Copyright © 2019 Copyright is held by the owner/author(s)

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 17 December 2019

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