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Dynamic partitioning in different distributed-memory environments

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Job Scheduling Strategies for Parallel Processing (JSSPP 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1162))

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Abstract

In this paper we present a detailed analysis of dynamic partitioning in different distributed-memory parallel environments based on experimental and analytical methods. We develop an experimental test-bed for the IBM SP2 and a network of workstations, and we apply a general analytic model of dynamic partitioning. This experimental and analytical framework is then used to explore a number of fundamental performance issues and tradeoffs concerning dynamic partitioning in different distributed-memory computing environments. Our results demonstrate and quantify how the performance benefits of dynamic partitioning are heavily dependent upon several system variables, including workload characteristics, system architecture, and system load.

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Authors and Affiliations

  1. IBM T. J. Watson Research Center, 10598, Yorktown Heights, NY, USA

    Nayeem Islam & Mark S. Squillante

  2. Columbia University, 10027, New York, NY, USA

    Andreas Prodromidis

Authors
  1. Nayeem Islam
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  2. Andreas Prodromidis
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  3. Mark S. Squillante
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Dror G. Feitelson Larry Rudolph

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© 1996 Springer-Verlag Berlin Heidelberg

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Islam, N., Prodromidis, A., Squillante, M.S. (1996). Dynamic partitioning in different distributed-memory environments. In: Feitelson, D.G., Rudolph, L. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 1996. Lecture Notes in Computer Science, vol 1162. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0022297

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  • DOI: https://doi.org/10.1007/BFb0022297

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61864-5

  • Online ISBN: 978-3-540-70710-3

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