Multiphysics problems involve the couplings of different sets of partial differential equations. Partitioned methods consider the individual solutions of each set, which upon iterating, converge to the monolithic solution. The main drawback of partitioned methods is the additional iterative loop, which can be done a la Jacobi (parallel) or a la Gauss–Seidel (sequential). The latter method has worse algorithmic properties than the last method, but makes better use of the computational resources. We will assess both the algorithmic and computational performances of these couplings, in the context of multiphysics surface coupling. To enhance the computational performance of the Gauss–Seidel method, we will introduce an overloading strategy together with an MPI barrier using DLB library. This approach makes the Gauss–Seidel method almost as parallel efficient as the Jacobi method. Our methodology is based on simple performance models, and the solution of multiphysics problems to show the validity of the proposed approach.

中文翻译：

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并行多物理场耦合：算法和计算性能

多物理场问题涉及不同偏微分方程组的耦合。分区方法考虑每个集合的单独解决方案，在迭代时收敛到整体解决方案。分区方法的主要缺点是额外的迭代循环，可以通过雅可比（并行）或高斯-赛德尔（顺序）完成。后一种方法的算法性能比前一种方法差，但更好地利用了计算资源。我们将在多物理场表面耦合的背景下评估这些耦合的算法和计算性能。为了提高 Gauss-Seidel 方法的计算性能，我们将使用 DLB 库引入重载策略和 MPI 屏障。这种方法使 Gauss-Seidel 方法几乎与 Jacobi 方法并行有效。我们的方法基于简单的性能模型和多物理场问题的解决方案，以证明所提出方法的有效性。