单源最短路径问题的并行算法

Saeed Maleki, Donald Nguyen, Andrew Lenharth, M. Garzarán, D. Padua, K. Pingali
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引用次数: 29

摘要

单源最短路径(SSSP)问题包括寻找从一个顶点(源顶点)到图中所有其他顶点的最短路径。SSSP有许多应用。对于某些算法和应用,并行解决SSSP问题是有用的。这就是中间性中心性的例子,它解决了大型图中多个源顶点的SSSP问题。本文介绍了Dijkstra条带挖掘松弛(DSMR)算法,这是一种适用于共享和分布式存储系统的高效并行SSSP算法。我们还介绍了一组预处理优化技术,这些技术可以在不显著增加总工作量的情况下显著降低通信开销。我们的研究结果表明,DSMR比之前最好的并行Δ-Stepping算法快了7.38倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DSMR: A Parallel Algorithm for Single-Source Shortest Path Problem
The Single Source Shortest Path (SSSP) problem consists in finding the shortest paths from a vertex (the source vertex) to all other vertices in a graph. SSSP has numerous applications. For some algorithms and applications, it is useful to solve the SSSP problem in parallel. This is the case of Betweenness Centrality which solves the SSSP problem for multiple source vertices in large graphs. In this paper, we introduce the Dijkstra Strip Mined Relaxation (DSMR) algorithm, an efficient parallel SSSP algorithm for shared and distributed-memory systems. We also introduce a set of preprocessing optimization techniques that significantly reduce the communication overhead without increasing the total amount of work dramatically. Our results show that, DSMR is faster than the best previous algorithm, parallel Δ-Stepping, by up-to 7.38×.
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