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A Fully Scalable Parallel Algorithm for Solving Elliptic Partial Differential Equations

机译:求解椭圆局部微分方程的完全可扩展并行算法

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A comparison is made between the probabilistic domain decomposition (DD) method and a certain deterministic DD method for solving linear elliptic boundary-value problems. Since in the deterministic approach the CPU time is affected by intercommunications among the processors, it turns out that the probabilistic method performs better, especially when the number of subdomains (hence, of processors) is increased. This fact is clearly illustrated by some examples. The probabilistic DD algorithm has been implemented in an MPI environment, in order to exploit distributed computer architectures. Scalability and fault-tolerance of the probabilistic DD algorithm are emphasized.
机译:概率域分解(DD)方法与用于求解线性椭圆边值问题的某种确定性DD方法进行比较。由于在确定性方法中,CPU时间受处理器之间的互通的影响,结果证明概率方法更好地执行,特别是当增加子域(因此,处理器)的数量时。一些例子清楚地说明了这一事实。概率DD算法已在MPI环境中实现,以便利用分布式计算机架构。强调了概率DD算法的可扩展性和容错。

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