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by Kirk Schloegel, George Karypis, Vipin Kumar
http://www-users.cs.umn.edu/~karypis/publications/Papers/PDF/unified-adaptive.pdf
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Abstract:
Adaptive scienti c simulations require that periodic repartitioning occur dynamically throughout the course of the simulation. The computed repartitionings should minimize both the inter-processor communications incurred during the iterative mesh-based computation and the data redistribution costs required to balance the load. Recently developed schemes for computing repartitionings provide the user with only a limited control of the tradeo s among these objectives. This paper describes anew Uni ed Repartitioning Algorithm that can gracefully tradeo one objective for the other dependent upon a user-de ned parameter describing the relative costs of these objectives. We show that the Uni ed Repartitioning Algorithm is able to minimize the precise overheads associated with repartitioning as well as or better than other repartitioning schemes for a variety of problems, regardless of the relative costs of performing inter-processor communication and data redistribution. Our experimental results show that the Uni ed Repartitioning Algorithm is extremely fast and scalable to very large problems.
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