A Deep Q-Learning Approach for GPU Task Scheduling

Ryan S. Luley, Qinru Qiu

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

Abstract

Efficient utilization of resources is critical to system performance and effectiveness for high performance computing systems. In a graphics processing unit (GPU)-based system, one method for enabling higher utilization is concurrent kernel execution-allowing multiple independent kernels to simultaneously execute on the GPU. However, resource contention due to the manner in which kernel tasks are scheduled may still lead to suboptimal task performance and utilization. In this work, we present a deep Q-learning approach to identify an ordering for a given set of tasks which achieves near-optimal average task performance and high resource utilization. Our solution outperforms other similar approaches and has additional benefit of being adaptable to dynamic task characteristics or GPU resource configurations.

Original languageEnglish (US)
Title of host publication2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728192192
DOIs
StatePublished - Sep 22 2020
Event2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 - Virtual, Waltham, United States
Duration: Sep 21 2020Sep 25 2020

Publication series

Name2020 IEEE High Performance Extreme Computing Conference, HPEC 2020

Conference

Conference2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Country/TerritoryUnited States
CityVirtual, Waltham
Period9/21/209/25/20

Keywords

  • GPU
  • concurrent kernel execution
  • deep Q-learning
  • kernel scheduling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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