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2007
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A Model-Driven Approach to Job/Task Composition in Cluster Computing

9 years 10 months ago
A Model-Driven Approach to Job/Task Composition in Cluster Computing
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.
Neeraj Mehta, Yogesh Kanitkar, Konstantin Läu
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IPPS
Authors Neeraj Mehta, Yogesh Kanitkar, Konstantin Läufer, George K. Thiruvathukal
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