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Title:Hypergraph-Based Combinatorial Optimization of Matrix -Vector Multiplication
Author(s):Wolf, Michael Maclean
Doctoral Committee Chair(s):Michael Heath
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
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.
Issue Date:2009
Description:131 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.
Other Identifier(s):(MiAaPQ)AAI3395539
Date Available in IDEALS:2015-09-25
Date Deposited:2009

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