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Parallel and Distributed Processing Symposium, International (2007)
Long Beach, CA, USA
Mar. 26, 2007 to Mar. 30, 2007
ISBN: 1-4244-0909-8
pp: 233
Yogesh Kanitkar , Emerging Technologies Laboratory, Department of Computer Science, Loyola University Chicago, 820 North Michigan Avenue, Chicago, IL 60611, USA
Neeraj Mehta , Emerging Technologies Laboratory, Department of Computer Science, Loyola University Chicago, 820 North Michigan Avenue, Chicago, IL 60611, USA
Konstantin Laufer , Emerging Technologies Laboratory, Department of Computer Science, Loyola University Chicago, 820 North Michigan Avenue, Chicago, IL 60611, USA. laufer@cs.luc.edu
George K. Thiruvathukal , Emerging Technologies Laboratory, Department of Computer Science, Loyola University Chicago, 820 North Michigan Avenue, Chicago, IL 60611, USA. gkt@cs.luc.edu
ABSTRACT
In the general area of high-performance computing, object-oriented methods have gone largely unnoticed. In contrast, the Computational Neighborhood (CN), a framework for parallel and distributed computing with a focus on cluster computing, was designed from ground up to be object-oriented. This paper describes how we have successfully used UML in the following model-driven, generative approach to job/task composition in CN. We model CN jobs using activity diagrams in any modeling tool with support for XMI, an XML-based external representation of UML models. We then export the activity diagrams and use our XSLT-based tool to transform the resulting XMI representation to CN job/task composition descriptors.
INDEX TERMS
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CITATION
Yogesh Kanitkar, Neeraj Mehta, Konstantin Laufer, George K. Thiruvathukal, "A Model-Driven Approach to Job/Task Composition in Cluster Computing", Parallel and Distributed Processing Symposium, International, vol. 00, no. , pp. 233, 2007, doi:10.1109/IPDPS.2007.370423
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