Edmonds-Karp算法的并行化建模及其应用

A. Chaibou, Ousmane Moussa Tessa, Oumarou Sié
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引用次数: 1

摘要

许多优化问题可以简化为网络中的最大流问题。然而,最大流量问题相当于最小切割问题,Fulkerson和Ford (Fulkerson & Ford, 1956)指出。图的切割有几种算法,如Ford-Fulkerson算法(Ford & Fulkerson, 1962), Edmonds-Karp算法(Edmonds & Karp, 1972)或Goldberg-Tarjan算法(Goldberg & Tarjan, 1988)。本文研究了Ford-Fulkerson算法的优化版Edmonds-Karp算法的并行计算。实际上,当在大型图上执行时,该算法可能非常慢,复杂度为O|V|。|E|2(其中|V|表示顶点数,|E|表示图边数)。因此,我们为什么要研究它在OpenMp和GP-GPU等廉价并行平台上的实现。我们的工作也强调了该算法并行计算的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the Parallelization of the Edmonds-Karp Algorithm and Application
Many optimization problems can be reduced to the maximum flow problem in a network. However, the maximum flow problem is equivalent to the problem of the minimum cut, as shown by Fulkerson and Ford (Fulkerson & Ford, 1956). There are several algorithms of the graph’s cut, such as the Ford-Fulkerson algorithm (Ford & Fulkerson, 1962), the Edmonds-Karp algorithm (Edmonds & Karp, 1972) or the Goldberg-Tarjan algorithm (Goldberg & Tarjan, 1988). In this paper, we study the parallel computation of the Edmonds-Karp algorithm which is an optimized version of the Ford-Fulkerson algorithm. Indeed, this algorithm, when executed on large graphs, can be extremely slow, with a complexity of the order of O|V|.|E|2 (where |V| designates the number of vertices and |E| designates the number of the edges of the graph). So why we are studying its implementation on inexpensive parallel platforms such as OpenMp and GP-GPU. Our work also highlights the limits for parallel computing on this algorithm.
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