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Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods

机译:使用快速多极方法可扩展的力指向图布局算法

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We present an extension to ExaFMM, a Fast Multipole Method library, as a generalized approach for fast and scalable execution of the Force-Directed Graph Layout algorithm. The Force-Directed Graph Layout algorithm is a physics-based approach to graph layout that treats the vertices V as repelling charged particles with the edges E connecting them acting as springs. Traditionally, the amount of work required in applying the Force-Directed Graph Layout algorithm is O(|V|^2 + |E|) using direct calculations and O(|V| log |V| + |E|) using truncation, filtering, and/or multi-level techniques. Correct application of the Fast Multipole Method allows us to maintain a lower complexity of O(|V| + |E|) while regaining most of the precision lost in other techniques. Solving layout problems for truly large graphs with millions of vertices still requires a scalable algorithm and implementation. We have been able to leverage the scalability and architectural adaptability of the ExaFMM library to create a Force-Directed Graph Layout implementation that runs efficiently on distributed multicore and multi-GPU architectures.
机译:我们向EXAFMM提供了一个快速多极方法库的扩展,作为用于快速和可扩展的力的图形布局算法的广义方法。力向图布局算法是基于物理的方法来图形布局对待顶点V作为与边缘ë连接它们充当弹簧排斥带电粒子。传统上,使用截断的直接计算和O(| V | v | v | v | v | + |)应用力定向图布局算法所需的工作量是O(| v | ^ 2 + | e |),过滤和/或多级技术。正确的快速多极方法的应用允许我们保持o(| v | + | + |)的较低复杂性,同时重新恢复在其他技术中损失的大部分精度。解决具有数百万顶点的真正大图的布局问题仍需要可扩展的算法和实现。我们已经能够利用EXAFMM库的可扩展性和架构适应性,以创建有效地在分布式多核和多GPU架构上运行的力定向图布局实现。

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