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dc.contributor.authorAkbudak K.en_US
dc.contributor.authorAykanat, Cevdeten_US
dc.date.accessioned2018-04-12T11:02:47Z
dc.date.available2018-04-12T11:02:47Z
dc.date.issued2017en_US
dc.identifier.issn1045-9219
dc.identifier.urihttp://hdl.handle.net/11693/37098
dc.description.abstractExploiting spatial and temporal localities is investigated for efficient row-by-row parallelization of general sparse matrix-matrix multiplication (SpGEMM) operation of the form C=A,B on many-core architectures. Hypergraph and bipartite graph models are proposed for 1D rowwise partitioning of matrix A to evenly partition the work across threads with the objective of reducing the number of B-matrix words to be transferred from the memory and between different caches. A hypergraph model is proposed for B-matrix column reordering to exploit spatial locality in accessing entries of thread-private temporary arrays, which are used to accumulate results for C-matrix rows. A similarity graph model is proposed for B-matrix row reordering to increase temporal reuse of these accumulation array entries. The proposed models and methods are tested on a wide range of sparse matrices from real applications and the experiments were carried on a 60-core Intel Xeon Phi processor, as well as a two-socket Xeon processor. Results show the validity of the models and methods proposed for enhancing the locality in parallel SpGEMM operations. © 1990-2012 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Parallel and Distributed Systemsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TPDS.2017.2656893en_US
dc.subjectBipartite graph modelen_US
dc.subjectComputational hypergraph modelen_US
dc.subjectIntel Xeon Phien_US
dc.subjectSpGEMMen_US
dc.subjectComputer architectureen_US
dc.subjectGraph theoryen_US
dc.subjectBipartite graphsen_US
dc.subjectData localityen_US
dc.subjectGraph clusteringen_US
dc.subjectGraph modelen_US
dc.subjectGraph partitioningen_US
dc.subjectHypergraph clusteringen_US
dc.subjectHypergraph modelen_US
dc.subjectHypergraph partitioningen_US
dc.subjectMany-core architectureen_US
dc.subjectSparse matricesen_US
dc.subjectSparse matrix-matrix multiplicationsen_US
dc.subjectMatrix algebraen_US
dc.titleExploiting locality in sparse matrix-matrix multiplication on many-core rchitecturesen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage2258en_US
dc.citation.epage2271en_US
dc.citation.volumeNumber28en_US
dc.citation.issueNumber8en_US
dc.identifier.doi10.1109/TPDS.2017.2656893en_US
dc.publisherIEEE Computer Societyen_US
dc.contributor.bilkentauthorAykanat, Cevdet


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