DQN-based OpenCL workload partition for performance optimization
- Authors
- Park, Sanghyun; Suh, Taeweon
- Issue Date
- 8월-2019
- Publisher
- SPRINGER
- Keywords
- OpenCL; DQN; Workload partition
- Citation
- JOURNAL OF SUPERCOMPUTING, v.75, no.8, pp.4875 - 4893
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF SUPERCOMPUTING
- Volume
- 75
- Number
- 8
- Start Page
- 4875
- End Page
- 4893
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/63675
- DOI
- 10.1007/s11227-019-02766-0
- ISSN
- 0920-8542
- Abstract
- 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.
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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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