RT Journal Article
JF 2007 IEEE International Parallel and Distributed Processing Symposium
YR 2007
VO 00
IS
SP 233
TI A Model-Driven Approach to Job/Task Composition in Cluster Computing
A1 Yogesh Kanitkar,
A1 Neeraj Mehta,
A1 Konstantin Laufer,
A1 George K. Thiruvathukal,
K1 null
AB 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.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN
LA English
DO 10.1109/IPDPS.2007.370423
LK http://doi.ieeecomputersociety.org/10.1109/IPDPS.2007.370423