DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, Oh-Kyoung | ko |
dc.contributor.author | Kang, Ji-Hoon | ko |
dc.contributor.author | Lee, Seungchul | ko |
dc.contributor.author | Kim, Wonjung | ko |
dc.contributor.author | Song, Junehwa | ko |
dc.date.accessioned | 2023-09-13T03:03:04Z | - |
dc.date.available | 2023-09-13T03:03:04Z | - |
dc.date.created | 2023-09-13 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | 31st International Conference on Parallel Architectures and Compilation Techniques, PACT 2022, pp.384 - 397 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312570 | - |
dc.description.abstract | Improving the communication performance of parallel programs is an important but difcult problem in a large-scale distributed memory-based cluster. Efforts to improve parallel scalability often face severe huddles in managing communication overheads. This paper proposes a framework of a space-fling curve(SFC)-based task-remapping for communication intensive parallel applications. An SFC-based mapping, when applied for task-mapping of parallel applications preserves locality in terms of communications and produce a less fragmented task-mapping, reducing communication overheads. The framework also provides tools for performance analysis to see if the proposed task-mapping is appropriate for a given application running on a target system. It further develops a binary classifer as a predictor to decide whether or not to apply the proposed mapping before run-time. We evaluate the framework with three communication intensive applications in Cartesian coordinates: P3DFFT solver and Channel code using 2D domain decomposition model, and Poisson solver using 3D domain decomposition. The evaluation is conducted on a large-scale cluster system of fat-tree topology with up to 1,024 compute nodes. The proposed task-mapping achieves the overall performance improvement ranging from ∼30% to ∼66% over the baseline approach depending on the workloads. Also, when used in combination with the binary classifer-based predictor, it achieves the expected performance gains from 4% to 8%. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Efficient task-mapping of parallel applications using a space-filling curve | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85147330600 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 384 | - |
dc.citation.endingpage | 397 | - |
dc.citation.publicationname | 31st International Conference on Parallel Architectures and Compilation Techniques, PACT 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Chicago, IL | - |
dc.identifier.doi | 10.1145/3559009.3569657 | - |
dc.contributor.localauthor | Song, Junehwa | - |
dc.contributor.nonIdAuthor | Kwon, Oh-Kyoung | - |
dc.contributor.nonIdAuthor | Kang, Ji-Hoon | - |
dc.contributor.nonIdAuthor | Lee, Seungchul | - |
dc.contributor.nonIdAuthor | Kim, Wonjung | - |
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