Partitioning Rectangular and Structurally Nonsymmetric Sparse Matrices for Parallel Processing | Zendy

Bruce Hendrickson | Zendy; Tamara G. Kolda | Zendy

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Partitioning Rectangular and Structurally Nonsymmetric Sparse Matrices for Parallel Processing

Author(s) -

Bruce Hendrickson,

Tamara G. Kolda

Publication year - 1998

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/1436

Subject(s) - bipartite graph , transpose , sparse matrix , matrix multiplication , multiplication (music) , computer science , parallel computing , matrix (chemical analysis) , set (abstract data type) , product (mathematics) , algorithm , theoretical computer science , mathematics , combinatorics , graph , eigenvalues and eigenvectors , physics , materials science , geometry , quantum mechanics , composite material , quantum , gaussian , programming language

A common operation in scientific computing is the multiplication of a sparse, rectangular or structurally nonsymmetric 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

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