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dc.contributor.authorKayaaslan, E.en_US
dc.contributor.authorAykanat, C.en_US
dc.contributor.authorUçar, B.en_US
dc.date.accessioned2019-01-23T12:25:29Z
dc.date.available2019-01-23T12:25:29Z
dc.date.issued2018en_US
dc.identifier.issn1064-8275
dc.identifier.urihttp://hdl.handle.net/11693/48269
dc.description.abstractThere are three common parallel sparse matrix-vector multiply algorithms: 1D row-parallel, 1D column-parallel, and 2D row-column-parallel. The 1D parallel algorithms offer the advantage of having only one communication phase. On the other hand, the 2D parallel algorithm is more scalable, but it suffers from two communication phases. Here, we introduce a novel concept of heterogeneous messages where a heterogeneous message may contain both input-vector entries and partially computed output-vector entries. This concept not only leads to a decreased number of messages but also enables fusing the input- and output-communication phases into a single phase. These findings are exploited to propose a 1.5D parallel sparse matrix-vector multiply algorithm which is called local row-column-parallel. This proposed algorithm requires a constrained fine-grain partitioning in which each fine-grain task is assigned to the processor that contains either its input-vector entry, its output-vector entry, or both. We propose two methods to carry out the constrained fine-grain partitioning. We conduct our experiments on a large set of test matrices to evaluate the partitioning qualities and partitioning times of these proposed 1.5D methods.en_US
dc.language.isoEnglishen_US
dc.source.titleSIAM Journal on Scientific Computingen_US
dc.relation.isversionofhttps://doi.org/10.1137/16M1105591en_US
dc.subjectSparse matrix partitioningen_US
dc.subjectParallel sparse matrix-vector multiplicationen_US
dc.subjectDirected hypergraph modelen_US
dc.subjectBipartite vertex coveren_US
dc.subjectCombinatorial scientific computingen_US
dc.title1.5D parallel sparse matrix-vector multiplyen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spageC25en_US
dc.citation.epageC46en_US
dc.citation.volumeNumber40en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1137/16M1105591en_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.identifier.eissn1095-7197


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