Global load balancing with parallel mesh adaption on distributed-memory systems

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Abstract
Dynamic mesh adaption on unstructured grids is a powerful tool for efficiently computing unsteady problems to resolve solution features of interest. Unfortunately, this causes load imbalance among processors on a parallel machine. This paper describes the parallel implementation of a tetrahedral mesh adaption scheme and a new global load balancing method. A huristic remapping algorithm is presented that assigns partitions to processors such that the redistribution cost is minimized. Results indicate that the paralel performance of the mesh adaption code depends on the nature of the adaption region and show a 35.5X speedup on 64 processors when about 35% of the mesh is randomly adapted. For large-scale scientific computations, our load balancing strategy gives almost a sixfold reduction in solver execution times over non-balanced loads. Furthermore, our heuristic remapper yields processor assignments that are less than 3% off the optimal solutions but requries only 1% of the computational time.

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