High-performance graph algorithms from parallel sparse matrices

Authors:
John R. Gilbert;Steve Reinhardt;Viral B. Shah
Affiliations:
University of California, Dept. of Computer Science, Santa Barbara, CA;Silicon Graphics Inc.;University of California, Dept. of Computer Science, Santa Barbara, CA
Venue:
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Year:
2006

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Abstract

Large-scale computation on graphs and other discrete structures is becoming increasingly important in many applications, including computational biology, web search, and knowledge discovery. High-performance combinatorial computing is an infant field, in sharp contrast with numerical scientific computing. We argue that many of the tools of high-performance numerical computing - in particular, parallel algorithms and data structures for computation with sparse matrices - can form the nucleus of a robust infrastructure for parallel computing on graphs. We demonstrate this with an implementation of a graph analysis benchmark using the sparse matrix infrastructure in Star-P, our parallel dialect of the MATLAB programming language.