A Parallel Algorithm for Multilevel Graph Partitioning and Sparse Matrix Ordering

302Citations
Citations of this article
141Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In this paper we present a parallel formulation of the multilevel graph partitioning and sparse matrix ordering algorithm. A key feature of our parallel formulation (that distinguishes it from other proposed parallel formulations of multilevel algorithms) is that it partitions the vertices of the graph intopparts while distributing the overall adjacency matrix of the graph among allpprocessors. This mapping results in substantially smaller communication than one-dimensional distribution for graphs with relatively high degree, especially if the graph is randomly distributed among the processors. We also present a parallel algorithm for computing a minimal cover of a bipartite graph which is a key operation for obtaining a small vertex separator that is useful for computing the fill reducing ordering of sparse matrices. Our parallel algorithm achieves a speedup of up to 56 on 128 processors for moderate size problems, further reducing the already moderate serial run time of multilevel schemes. Furthermore, the quality of the produced partitions and orderings are comparable to those produced by the serial multilevel algorithm that has been shown to outperform both spectral partitioning and multiple minimum degree. © 1998 Academic Press.

Cite

CITATION STYLE

APA

Karypis, G., & Kumar, V. (1998). A Parallel Algorithm for Multilevel Graph Partitioning and Sparse Matrix Ordering. Journal of Parallel and Distributed Computing, 48(1), 71–95. https://doi.org/10.1006/jpdc.1997.1403

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free