Comparative Analysis of GPU Stream Processing between Persistent and Non-persistent Kernels

Suhwan Kim, Changue Jung, Younghoon Kim

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

2 Scopus citations

Abstract

Employing GPU to accelerate stream data processing has shown to be a considerable success in recent research. The stream processing engines need to process continuous data. Persistent Thread (PT), where GPU threads remain in a loop throughout executions, rather than non-Persistent Thread (nonPT) kernels can give several advantages. PT provides high-performance low-latency data processing by reducing kernel launch overhead and hiding memory copy overhead to the GPU. In this paper, we comparatively analyze the performances of PT and nonPT in various aspects. We also figure out the cause of advantages and what needs to be considered in order to obtain actual performance gain. The evaluation was done with four distinct application scenarios and the result shows that PT yields at most x4.4 higher performance over nonPT under desirable conditions.

Original languageEnglish
Title of host publicationICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publicationAccelerating Digital Transformation with ICT Innovation
PublisherIEEE Computer Society
Pages2330-2332
Number of pages3
ISBN (Electronic)9781665499392
DOIs
StatePublished - 2022
Externally publishedYes
Event13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of
Duration: Oct 19 2022Oct 21 2022

Publication series

NameInternational Conference on ICT Convergence
Volume2022-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/TerritoryKorea, Republic of
CityJeju Island
Period10/19/2210/21/22

Keywords

  • GPU
  • Persistent Thread

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