.png){kind=icon}
Return
Partitioning rectangular and structurally unsymmetric sparse matrices for parallel processing
.png){kind=icon}
PRE
AI
DOI：10.1137/S1064827598341475.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.6
Papers:
5.1K
Citations:
1.8W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSave
ShareSaveShareSaveShareSave
View more