Partitioning graphs on message-passing machines by pairwise mincut

Document Type

Article

Publication Date

1-1-1998

Abstract

Realizing the potential of massively parallel machines requires good solutions to the problem of mapping computations among processors so that execution is load-balanced with low inter-processor communication resulting in low execution time. This problem is typically treated as a graph partitioning problem. We develop a parallel heuristic algorithm for partitioning the vertices of a graph into many clusters so that the number of inter-cluster edges is minimized. The algorithm is designed for message-passing machines such as hypercubes. This algorithm is suitable for use with runtime approaches that have been recently developed for parallelizing unstructured scientific computations. We present a parallelization of the Kernighan-Lin heuristic that starts with an initial random multiway partition and performs pairwise improvements through application of the mincut bisection heuristic, known as Partitioning by Pairwise Mincut (PPM). A novel parallel scheme providing nearly linear speedup is developed for PPM that is optimal in terms of communication. © 1998 Published by Elsevier Science Inc. All rights reserved.

Publication Source (Journal or Book title)

Information Sciences

First Page

223

Last Page

237

This document is currently not available here.

Share

COinS