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Predicting the Cost and Benefit of Adapting Data Parallel Applications in Clusters

By Jon B. Weissman


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

Year: 2002
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