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A Model-Driven Approach to Job/Task Composition in Cluster Computing
IEEE International Parallel and Distributed Processing Symposium
  • Yogesh Kanitkar
  • Konstantin Läufer, Loyola University Chicago
  • Neeraj Mehta
  • George K. Thiruvathukal, Loyola University Chicago
Document Type
Conference Proceeding
Publication Date
Publisher Name
IEEE Computer Society
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.
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Creative Commons License
Creative Commons Attribution-Noncommercial-No Derivative Works 3.0
Citation Information
Neeraj Mehta, Yogesh Kanitkar, Konstantin Laufer, George K. Thiruvathukal, "A Model-Driven Approach to Job/Task Composition in Cluster Computing," ipdps, pp.233, 2007 IEEE International Parallel and Distributed Processing Symposium, 2007