Dynamic Resource Management for Efficient Utilization of Multitasking GPUs
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jason Jong Kyu | - |
dc.contributor.author | Park, Yongjun | - |
dc.contributor.author | Mahlke, Scott | - |
dc.date.accessioned | 2022-06-10T05:40:16Z | - |
dc.date.available | 2022-06-10T05:40:16Z | - |
dc.date.created | 2022-06-10 | - |
dc.date.issued | 2017-04 | - |
dc.identifier.issn | 0362-1340 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/28144 | - |
dc.description.abstract | As graphics processing units (GPUs) are broadly adopted, running multiple applications on a GPU at the same time is beginning to attract wide attention. Recent proposals on multitasking GPUs have focused on either spatial multitasking, which partitions GPU resource at a streaming multiprocessor (SM) granularity, or simultaneous multikernel (SMK), which runs multiple kernels on the same SM. However, multitasking performance varies heavily depending on the resource partitions within each scheme, and the application mixes. In this paper, we propose GPU Maestro that performs dynamic resource management for efficient utilization of multitasking GPUs. GPU Maestro can discover the best performing GPU resource partition exploiting both spatial multitasking and SMK. Furthermore, dynamism within a kernel and interference between the kernels are automatically considered because GPU Maestro finds the best performing partition through direct measurements. Evaluations show that GPU Maestro can improve average system throughput by 20.2% and 13.9% over the baseline spatial multitasking and SMK, respectively. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.subject | LEVEL | - |
dc.subject | MODEL | - |
dc.title | Dynamic Resource Management for Efficient Utilization of Multitasking GPUs | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Yongjun | - |
dc.identifier.doi | 10.1145/3093336.3037707 | - |
dc.identifier.wosid | 000408313700038 | - |
dc.identifier.bibliographicCitation | ACM SIGPLAN NOTICES, v.52, no.4, pp.527 - 540 | - |
dc.relation.isPartOf | ACM SIGPLAN NOTICES | - |
dc.citation.title | ACM SIGPLAN NOTICES | - |
dc.citation.volume | 52 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 527 | - |
dc.citation.endPage | 540 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | LEVEL | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Graphics Processing Unit | - |
dc.subject.keywordAuthor | Multitasking | - |
dc.subject.keywordAuthor | Resource Management | - |
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