Partitioning rectangular and structurally unsymmetric sparse matrices for parallel processing

doi:10.1137/S1064827598341475

Partitioning rectangular and structurally unsymmetric sparse matrices for parallel processing

2000-01-01

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Tamara G. Kolda

DOI：10.1137/S1064827598341475

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Abstract

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A common operation in scientific computing is the multiplication of a sparse, rectangular, or structurally unsymmetric matrix and a vector. In many applications the matrix-transpose-vector product is also required. This paper addresses the efficient parallelization of these operations. We show that the problem can be expressed in terms of partitioning bipartite graphs. We then introduce several algorithms for this partitioning problem and compare their performance on a set of test matrices.

Keywords:

matrix partitioning

iterative method

parallel computing

rectangular matrix

structurally unsymmetric matrix

bipartite graph

Partitioning rectangular and structurally unsymmetric sparse matrices for parallel processing

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