Computational fluid dynamics (CFD) computations often require coupling of some values on one computational domain or portion thereof to another. This is the case both for multi-physics couplings, and for the implementation of various features, such as restarting on a different mesh, boundary condition couplings, and others. This usually involves locating points or elements relative to other elements, without a priori knowledge of neighbourhood or connectivity information. For highly refined meshes on parallel distributed memory systems, using programming paradigms such as MPI, it is practical to build mappings in situ, as a part of the computation. In this article, we describe different algorithms used in the context of the code_saturne CFD tool, and the associated Parallel Location and Exchange (PLE) library, based on their design goals, performance, ease of use and API stability. We also provide some elements on actual observed performance, and paths for improvements with increasingly massive parallelism.

中文翻译：

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非结构化网格的大规模并行定位和交换工具

计算流体动力学 (CFD) 计算通常需要将一个计算域或其一部分上的某些值耦合到另一个计算域。多物理场耦合和各种特征的实现都是这种情况，例如在不同的网格上重新启动、边界条件耦合等。这通常涉及相对于其他元素定位点或元素，而无需对邻域或连通性信息的先验知识。对于并行分布式内存系统上高度精细的网格，使用 MPI 等编程范式，作为计算的一部分，在原位构建映射是可行的。在本文中，我们描述了 code_saturne CFD 工具上下文中使用的不同算法，以及相关的并行位置和交换 (PLE) 库，基于它们的设计目标，性能、易用性和 API 稳定性。我们还提供了一些关于实际观察到的性能的元素，以及越来越多的大规模并行性的改进路径。