ON two-dimensional sparse matrix partitioning: models, methods, and a recipe
Çatalyürek, U. V.
SIAM Journal on Scientific Computing
Society for Industrial and Applied Mathematics
656 - 683
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We 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.
KeywordsCombinatorial scientific computing
Parallel matrix-vector multiplication
Sparse matrix partitioning
Matrix vector multiplication
Published Version (Please cite this version)http://dx.doi.org/10.1137/080737770
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