Graph scaling: A technique for automating program construction and deployment in clusterGOP

Fan Chan, Jiannong Cao, Yudong Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Program development and resource management are critical issues in large-scaled parallel applications and they raise difficulties for the programmers. Automation tools can benefit the programmer by reducing the time and work required for programming, deploying, and managing parallel applications. In our previous work, we have developed a visual tool, VisualGOP, to help visual construction and automatic mapping of parallel programs to execute on the ClusterGOP platform, which provides a graph-oriented model and the environment for running the parallel applications on clusters. In VisualGOP, the programmer needs to manually build the task interaction graph. This may lead to scalability problem for large applications. In this paper, we propose a graph scaling approach that helps the programmer to develop and deploy a large-scale parallel application minimizing the effort of graph construction, task binding and program deployment. The graph scaling algorithms expand or reduce a task graph to match the specified scale of the program and the hardware architecture, e.g., the problem size, the number of processors and interconnection topology, so as to produce an automatic mapping. An example is used to illustrate the proposed approach and how programmer benefits in the automation tools.
Original languageEnglish
Pages (from-to)254-264
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2834
Publication statusPublished - 1 Dec 2003

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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