ON two-dimensional sparse matrix partitioning: models, methods, and a recipe

Çatalyürek, U. V.Aykanat, CevdetUçar, A.2016-02-082016-02-0820101064-8275http://hdl.handle.net/11693/22351We consider two-dimensional partitioning of general sparse matrices for parallel sparse matrix-vector multiply operation. We present three hypergraph-partitioning-based methods, each having unique advantages. The first one treats the nonzeros of the matrix individually and hence produces fine-grain partitions. The other two produce coarser partitions, where one of them imposes a limit on the number of messages sent and received by a single processor, and the other trades that limit for a lower communication volume. We also present a thorough experimental evaluation of the proposed two-dimensional partitioning methods together with the hypergraph-based one-dimensional partitioning methods, using an extensive set of public domain matrices. Furthermore, for the users of these partitioning methods, we present a partitioning recipe that chooses one of the partitioning methods according to some matrix characteristics. © 2010 Society for Industrial and Applied Mathematics.EnglishCombinatorial scientific computingHypergraph partitioningParallel matrix-vector multiplicationSparse matrix partitioningTwo-dimensional partitioningExperimental evaluationHypergraphMatrix vector multiplicationOne-dimensional partitioningPartitioning methodsPublic domainsScientific computingSingle processorsSparse matricesMatrix algebraON two-dimensional sparse matrix partitioning: models, methods, and a recipeArticle10.1137/080737770