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Published Articles >> Table of Contents >> Abstract
18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 10
p. 199b
Autonomic Proactive Runtime Partitioning Strategies for SAMR Applications
Yeliang Zhang, University of Arizona
Jingmei Yang, University of Arizona
Salim Hariri, University of Arizona
Sumir Chandra, Rutgers University
Manish Parashar, Rutgers University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IPDPS.2004.1303223
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| Abstract |
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Dynamic structured adaptive mesh re.nement (SAMR) techniques along with the emergence of the computational Grid offer the potential for realistic scientific and engineering simulations of complex physical phenomena. However, the inherent dynamic nature of SAMR applications coupled with the heterogeneity and dynamism of the underlying Grid environment present significant research challenges. This paper presents proactive runtime partitioning strategies based on performance prediction functions that are experimentally formulated in terms of system parameters such as CPU load and available memory. These proactive partitioning strategies form a part of the GridARM autonomic framework which enables self-managing, self-adapting, and self-optimizing SAMR applications on the Grid. Experimental evaluation of the proactive schemes using the 3-D Richtmyer-Meshkov compressible fluid dynamics kernel for different system con.gurations and workloads demonstrates the improvement in overall runtime performance.
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 Additional Information
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Citation:
Yeliang Zhang, Jingmei Yang, Salim Hariri, Sumir Chandra, Manish Parashar,
"Autonomic Proactive Runtime Partitioning Strategies for SAMR Applications,"
ipdps,
p. 199b,
18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 10,
2004
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