Predicting the cost and benefit of adapting data parallel applications in clusters

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11 Scopus citations


This paper examines the problem of adapting data parallel applications in a shared dynamic environment of PC or workstation clusters. We developed an analytic framework to compare and contrast a wide range of adaptation strategies: dynamic load balancing, migration, processor addition and removal. These strategies have been evaluated with respect to the cost and benefit they provide for three representative parallel applications: an iterative jacobi solver for Laplace's equation, gaussian elimination with partial pivoting, and a gene sequence comparison application. We found that the cost and benefit of each method can be predicted with high accuracy (within 10%) for all applications and show that the framework is applicable to a wide variety of parallel applications. We then show that accurate prediction allows the most appropriate method to be selected dynamically. Performance improvement for the three applications ranged from 25% to 45% using our adaptation library. In addition, we dispel the conventional wisdom that migration is too expensive, and show that it can be beneficial even for running parallel applications with non-trivial communication.

Original languageEnglish (US)
Pages (from-to)1248-1271
Number of pages24
JournalJournal of Parallel and Distributed Computing
Issue number8
StatePublished - 2002

Bibliographical note

Funding Information:
This work was sponsored in part by the Army High Performance Computing Research Center (AHPCRC) under the auspices of the Department of the Army, Army Research Laboratory cooperative agreement DAAD19-01-2-0014.


  • Cluster computing
  • Distributed computing
  • Parallel processing

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