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dc.contributor.authorLiu, Junhong
dc.contributor.authorHe, Xin
dc.contributor.authorLiu, Weifeng
dc.contributor.authorTan, Guangming
dc.date.accessioned2018-04-09T08:18:02Z
dc.date.available2018-04-09T08:18:02Z
dc.date.created2018-04-06T09:27:32Z
dc.date.issued2018
dc.identifier.citationACMSIGPLAN Symposium on Principles and Practice of Parallel Programming. 2018, .nb_NO
dc.identifier.issn1542-0205
dc.identifier.urihttp://hdl.handle.net/11250/2493148
dc.description.abstractGeneral sparse matrix-matrix multiplication (SpGEMM) is an essential building block in a number of applications. In our work, we fully utilize GPU registers and shared memory to implement an efficient and load balanced SpGEMM in comparison with the existing implementations.nb_NO
dc.language.isoengnb_NO
dc.publisherAssociation for Computing Machinery (ACM)nb_NO
dc.titleRegister-based Implementation of the Sparse General Matrix-matrix Multiplication on GPUsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber2nb_NO
dc.source.journalACMSIGPLAN Symposium on Principles and Practice of Parallel Programmingnb_NO
dc.identifier.doihttps://doi.org/10.1145/3178487.3178529
dc.identifier.cristin1577872
dc.relation.projectEC/H2020/752321nb_NO
dc.description.localcode© 2018 Copyright held by the owner/author(s).nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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