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

Yogesh Kanitkar, Konstantin Läufer, Neeraj Mehta, George K. Thiruvathukal

Research output: Contribution to journalArticlepeer-review

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.

Original languageAmerican English
JournalComputer Science: Faculty Publications and Other Works
DOIs
StatePublished - Jan 1 2007

Keywords

  • computational neighborhood (CN)
  • computer science

Disciplines

  • Computer Sciences

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