A Unified Algorithm for Load-balancing Adaptive Scientific Simulations

Kirk Schloegel, George Karypis, and Vipin Kumar
Supercomputing, 2000
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Adaptive scientific 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 tradeoffs among these objectives. This paper describes a new Unified Repartitioning Algorithm that can gracefully tradeoff one objective for the other dependent upon a user-defined parameter describing the relative costs of these objectives. We show that the Unified 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 experiments show that the Unified Repartitioning Algorithm is extremely fast and scalable to very large problems.
Research topics: Graph partitioning | Parallel processing | ParMETIS