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.

• Type: Conference
• Year: 2007
• Where: IPPS
• Authors: Neeraj Mehta, Yogesh Kanitkar, Konstantin Läufer, George K. Thiruvathukal
• Keywords: Activity Diagrams, CN Jobs, Distributed And Parallel Computing, IPPS 2007, Job/task Composition