Hypergraph-Based Combinatorial Optimization of Matrix -Vector Multiplication
Wolf, Michael Maclean
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Permalink
https://hdl.handle.net/2142/81868
Description
Title
Hypergraph-Based Combinatorial Optimization of Matrix -Vector Multiplication
Author(s)
Wolf, Michael Maclean
Issue Date
2009
Doctoral Committee Chair(s)
Michael Heath
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
The second problem we address is parallel matrix-vector multiplication for large sparse matrices. Parallel sparse matrix-vector multiplication is a particularly important numerical kernel in computational science. We have focused on optimizing the parallel performance of this operation by reducing the communication volume through smarter, two-dimensional matrix partitioning. We have developed and implemented a recursive algorithm based on nested dissection to partition structurally symmetric matrices. In general, this method has proven to be the best available for partitioning structurally symmetric matrices (when considering both volume and partitioning time) and has shown great promise for information retrieval matrices. We also developed a second, simpler method that is fast and works well for many symmetric matrices.
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