DQN-based OpenCL workload partition for performance optimization
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sanghyun | - |
dc.contributor.author | Suh, Taeweon | - |
dc.date.accessioned | 2021-09-01T10:30:25Z | - |
dc.date.available | 2021-09-01T10:30:25Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-08 | - |
dc.identifier.issn | 0920-8542 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/63675 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | DQN-based OpenCL workload partition for performance optimization | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Suh, Taeweon | - |
dc.identifier.doi | 10.1007/s11227-019-02766-0 | - |
dc.identifier.scopusid | 2-s2.0-85061327493 | - |
dc.identifier.wosid | 000485886700045 | - |
dc.identifier.bibliographicCitation | JOURNAL OF SUPERCOMPUTING, v.75, no.8, pp.4875 - 4893 | - |
dc.relation.isPartOf | JOURNAL OF SUPERCOMPUTING | - |
dc.citation.title | JOURNAL OF SUPERCOMPUTING | - |
dc.citation.volume | 75 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 4875 | - |
dc.citation.endPage | 4893 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordAuthor | OpenCL | - |
dc.subject.keywordAuthor | DQN | - |
dc.subject.keywordAuthor | Workload partition | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.