基于快速多极方法的可伸缩力有向图布局算法

E. Yunis, Rio Yokota, A. Ahmadia
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引用次数: 19

摘要

我们提出了ExaFMM的扩展,一个快速多极方法库,作为一种快速和可扩展的执行力定向图布局算法的通用方法。力导向图形布局算法是一种基于物理的图形布局方法,它将顶点V视为相互排斥的带电粒子,将它们连接起来的边E作为弹簧。传统上,应用力向图布局算法所需的工作量是使用直接计算的O(|V|2 + |E|)和使用截断、过滤和/或多级技术的O(|V| log |V| + |E|)。快速多极方法的正确应用使我们能够保持较低的复杂度O(|V| + |E|),同时恢复其他技术中丢失的大部分精度。解决具有数百万个顶点的真正大型图形的布局问题仍然需要可伸缩的算法和实现。我们已经能够利用ExaFMM库的可伸缩性和架构适应性来创建一个在分布式多核和多gpu架构上有效运行的强制定向图形布局实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods
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.
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