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.
Bibliographical noteFunding Information:
This work was partially supported by the National Research Foundation of Korea under Grant NRF-2017R1D1A1B03028926.
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- Workload partition
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture