A Model-Driven Approach to Job/Task Composition in Cluster ComputingIEEE International Parallel and Distributed Processing Symposium
Document TypeConference Proceeding
AbstractIn 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.
Creative Commons LicenseCreative Commons Attribution-Noncommercial-No Derivative Works 3.0
Copyright StatementCopyright © 2007 Neeraj Mehta, Yogesh Kanitkar, Konstantin Läufer, George K. Thiruvathukal
Citation InformationNeeraj 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