DQN-based OpenCL workload partition for performance optimization

Sanghyun Park, Taeweon Suh

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper proposes a deep Q network (DQN)-based method for the workload partition problem in OpenCL. The DQN, a reinforcement learning algorithm, optimizes the workload partition for each processing unit by the self-training, based on the accumulated performance data on the computing environment. Our experiments reveal that the DQN-based partition provides the performance improvement by up to 62.2% and 6.9% in JPEG decoding, compared to the LuxMark-based and target-based partitions, respectively. The DQN is able to capture the low-level contention in slave devices such as caches and memory, and the communication bottleneck between devices, and reflect it to the workload partition ratio.

Original languageEnglish
Pages (from-to)4875-4893
Number of pages19
JournalJournal of Supercomputing
Issue number8
Publication statusPublished - 2019 Aug 1

Bibliographical note

Funding Information:
This work was partially supported by the National Research Foundation of Korea under Grant NRF-2017R1D1A1B03028926.

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.


  • DQN
  • OpenCL
  • Workload partition

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture


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