ON two-dimensional sparse matrix partitioning: models, methods, and a recipe
SIAM Journal on Scientific Computing
Society for Industrial and Applied Mathematics
656 - 683
Item Usage Stats
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
Showing items related by title, author, creator and subject.
Encapsulating multiple communication-cost metrics in partitioning sparse rectangular matrices for parallel matrix-vector multiplies Uçar, B.; Aykanat, Cevdet (SIAM, 2004)This paper addresses the problem of one-dimensional partitioning of structurally unsymmetric square and rectangular sparse matrices for parallel matrix-vector and matrix-transpose-vector multiplies. The objective is to ...
Improving performance of sparse matrix dense matrix multiplication on large-scale parallel systems Acer, S.; Selvitopi, O.; Aykanat, Cevdet (Elsevier BV, 2016)We propose a comprehensive and generic framework to minimize multiple and different volume-based communication cost metrics for sparse matrix dense matrix multiplication (SpMM). SpMM is an important kernel that finds ...
Demirci, Gündüz Vehbi (Bilkent University, 2019-08)The focus of this thesis is intelligent partitioning models and methods for scaling the performance of parallel graph computations on distributed-memory systems. Distributed databases utilize graph partitioning to provide ...