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Coarse Grained Parallel Maximum Independent Set in Convex Bipartite Graphs

机译:凸二部图中的粗粒平行最大独立集

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摘要

A bipartite graph G = (V, W, E) is convex if there exists an ordering of the vertices of W such that, for each v ∈ V, the neighbors of v are consecutive in W. Lipski and Preparata designed a sequential algorithm for computing a maximum matching in a convex bipartite graph. Bose et al. designed a BSP/CGM algorithm for the same problem. In this work we describe a sequential and a BSP/CGM algorithm for finding a maximum inde-pendent set in a convex bipartite graph. The input of our algorithms is a convex bipartite graph G and a maximum matching of G. Under certain assumptions, the sequential algorithm runs in time O(|V|) while, for p processors, the BSP/CGM algorithm runs in time O(|V|/p) using a constant number of communication rounds in which each processor sends and receives messages of size O(V|/p). The sequential algorithm is faster than the previously known algorithm. To the best of our knowledge, the parallel algorithm is the first in the BSP/CGM model for the problem.
机译:如果存在W的顶点的排序,则二分图G =(V,W,E)是凸的,这样对于每个v∈V,v的邻居在W中是连续的。Lipski和Preparata设计了一个用于计算凸二分图中的最大匹配。 Bose等。针对相同问题设计了BSP / CGM算法。在这项工作中,我们描述了一种顺序和BSP / CGM算法,用于在凸二分图中找到最大独立集。我们算法的输入是凸二分图G和G的最大匹配。在某些假设下,顺序算法在时间O(| V |)上运行,而对于p个处理器,BSP / CGM算法在时间O(| V)上运行| V | / p)使用固定数量的通信回合,其中每个处理器发送和接收大小为O(V | / p)的消息。顺序算法比以前已知的算法更快。据我们所知,并行算法是BSP / CGM模型中针对该问题的第一个算法。

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