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A Graph Model for Minimizing the Storage Overhead of Distributing Data for the Parallel Solution of Two-Phase Flows

机译:用于最小化分布数据的分布数据的存储开销的图形模型,用于两相流的并联解

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We consider a finite element method for the parallel solution of two-phase flow problems using a level set approach. Here, two systems of equations result from the discretization of the governing partial differential equations. Rather than investigating the solution of these systems, we focus on finding a data distribution for their assembly. We formulate a new combinatorial problem that minimizes the overhead in storage requirement to represent the systems while, at the same time, balancing the computational effort to assemble these systems in parallel. We model this problem by introducing a weighted undirected graph. We then transform the problem to a (standard) graph partitioning problem in which a weighted sum of certain edges is minimized subject to balancing a weighted sum of all vertices. Numerical experiments are carried out illustrating the feasibility of the new approach for an application using up to 512 processes of a cluster of quad-core processors.
机译:我们考虑使用级别设定方法对两相流问题的并行解决方案进行有限元方法。这里,由控制局部微分方程的离散化导致两个方程系统。我们不是调查这些系统的解决方案,我们专注于寻找其组装的数据分配。我们制定了一个新的组合问题,最大限度地减少了存储要求中的开销来代表系统,同时平衡计算工作并行组装这些系统。我们通过引入加权无向图来模拟此问题。然后,我们将问题转换为(标准)图分区问题,其中某些边缘的加权和最小化以平衡所有顶点的加权和。执行数值实验,说明了使用多达512个跨越的四核处理器群集应用程序的新方法的可行性。

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