The Friends-of-Friends (FoF) algorithm is a standard technique used in cosmological $N$-body simulations to identify structures. Its goal is to find clusters of particles (called groups) that are separated by at most a cut-off radius. $N$-body simulations typically use most of the memory present on a node, leaving very little free for a FoF algorithm to run on-the-fly. We propose a new method that utilises the common Union-Find data structure and a hybrid MPI+threads approach. The algorithm can also be expressed elegantly in a task-based formalism if such a framework is used in the rest of the application. We have implemented our algorithm in the open-source cosmological code, SWIFT. Our implementation displays excellent strong- and weak-scaling behaviour on realistic problems and compares favourably (speed-up of 18x) over other methods commonly used in the $N$-body community.

中文翻译：

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使用联合查找的粒子群查找的混合 MPI+线程方法

Friends-of-Friends (FoF) 算法是用于宇宙学 $N$-body 模拟以识别结构的标准技术。它的目标是找到最多间隔一个截止半径的粒子簇（称为组）。$N$-body 模拟通常使用节点上的大部分内存，留给 FoF 算法运行时的空闲空间很少。我们提出了一种新方法，它利用了通用的 Union-Find 数据结构和混合 MPI+线程方法。如果在应用程序的其余部分使用这样的框架，则该算法也可以用基于任务的形式来优雅地表达。我们已经在开源宇宙学代码 SWIFT 中实现了我们的算法。