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OpenMP-based parallel implementation of matrix-matrix multiplication on the Intel Knights Landing

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dc.contributor.authorLim, R.-
dc.contributor.authorLee, Y.-
dc.contributor.authorKim, R.-
dc.contributor.authorChoi, J.-
dc.date.available2019-04-10T09:56:17Z-
dc.date.created2018-09-12-
dc.date.issued2018-01-
dc.identifier.isbn9781450363471-
dc.identifier.urihttp://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/32428-
dc.description.abstractThe second generation Intel Xeon Phi processor codenamed Knights Landing (KNL) have emerged with 2D tile mesh architecture. Implementing of the general matrix-matrix multiplication on a new architecture is an important practice. To date, there has not been a sufficient description on a parallel implementation of the general matrix-matrix multiplication. In this study, we describe the parallel implementation of the double-precision general matrix-matrix multiplication (DGEMM) with OpenMP on the KNL. The implementation is based on the blocked matrix-matrix multiplication. We propose a method for choosing the cache block sizes and discuss the parallelism within the implementation of DGEMM. We show that the performance of DGEMM varies by the thread affinity environment variables. We conducted the performance experiments with the Intel Xeon Phi 7210 and 7250. The performance experiments validate our method. © 2018 Association for Computing Machinery.-
dc.language영어-
dc.language.isoen-
dc.publisherAssociation for Computing Machinery-
dc.relation.isPartOfACM International Conference Proceeding Series-
dc.titleOpenMP-based parallel implementation of matrix-matrix multiplication on the Intel Knights Landing-
dc.typeConference-
dc.identifier.doi10.1145/3176364.3176374-
dc.type.rimsCONF-
dc.identifier.bibliographicCitation2018 Workshop on High Performance Computing Asia, HPC Asia 2018, pp.63 - 66-
dc.description.journalClass2-
dc.identifier.scopusid2-s2.0-85043240112-
dc.citation.conferencePlaceUS-
dc.citation.endPage66-
dc.citation.startPage63-
dc.citation.title2018 Workshop on High Performance Computing Asia, HPC Asia 2018-
dc.contributor.affiliatedAuthorChoi, J.-
dc.type.docTypeConference Paper-
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