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Description
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 |
Degree: | Ph.D. |
Genre: | Dissertation |
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 |
Type: | Text |
Language: | English |
Description: | 131 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. |
URI: | http://hdl.handle.net/2142/81868 |
Other Identifier(s): | (MiAaPQ)AAI3395539 |
Date Available in IDEALS: | 2015-09-25 |
Date Deposited: | 2009 |
This item appears in the following Collection(s)
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Dissertations and Theses - Computer Science
Dissertations and Theses from the Dept. of Computer Science -
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois