DQN-based OpenCL workload partition for performance optimization

  • Sanghyun Park
  • Taeweon SuhEmail author


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.


OpenCL DQN Workload partition 



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


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Hanwha Systems Co., Ltd.SeoulKorea
  2. 2.Computer Science and EngineeringKorea UniversitySeoulKorea

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