Partitioning Rectangular and Structurally Nonsymmetric Sparse Matrices for Parallel Processing
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.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Oak Ridge, TN
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 1436
- Report Number(s):
- ORNL/TM-13657; KJ0101010; ON: DE00001436
- Country of Publication:
- United States
- Language:
- English
Similar Records
Partitioning sparse rectangular matrices for parallel processing
Parallel hypergraph partitioning for scientific computing.
Permuting sparse rectangular matrices into block-diagonal form
Technical Report
·
Fri May 01 00:00:00 EDT 1998
·
OSTI ID:1436
Parallel hypergraph partitioning for scientific computing.
Conference
·
Fri Jul 01 00:00:00 EDT 2005
·
OSTI ID:1436
+3 more
Permuting sparse rectangular matrices into block-diagonal form
Journal Article
·
Mon Dec 09 00:00:00 EST 2002
· SIAM Journal on Scientific Computing
·
OSTI ID:1436