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.
在非结构化网格上进行动态网格自适应是一种有效地计算不稳定问题以解决感兴趣的解决方案功能的强大工具。不幸的是,这导致并行计算机上的处理器之间的负载不平衡。本文描述了四面体网格自适应方案和新的全局负载平衡方法的并行实现。提出了一种启发式重新映射算法,该算法将分区分配给处理器,从而使重新分配成本最小化。结果表明,网格自适应代码的并行性能取决于自适应区域的性质,并且当大约35%的网格被随机自适应时,在64个处理器上显示出35.5倍的加速。对于大规模科学计算,我们的负载平衡策略使求解器的执行时间比不平衡负载减少了近六倍。此外,我们的启发式重映射器产生的处理器分配比最佳解决方案少3%,但仅需要1%的计算时间。 P>
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