Distributed-memory fast maximal independent set

Thejaka Amila Kanewala, Marcin Zalewski, A. Lumsdaine
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引用次数: 1

Abstract

The Maximal Independent Set (MIS) graph problem arises in many applications such as computer vision, information theory, molecular biology, and process scheduling. The growing scale of MIS problems suggests the use of distributed-memory hardware as a cost-effective approach to providing necessary compute and memory resources. Luby proposed four randomized algorithms to solve the MIS problem. All those algorithms are designed focusing on shared-memory machines and are analyzed using the PRAM model. These algorithms do not have direct efficient distributed-memory implementations. In this paper, we extend two of Luby's seminal MIS algorithms, "Luby(A)" and "Luby(B)," to distributed-memory execution, and we evaluate their performance. We compare our results with the "Filtered MIS" implementation in the Combinatorial BLAS library for two types of synthetic graph inputs.
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分布式内存快速最大独立集
极大独立集(MIS)图问题在计算机视觉、信息论、分子生物学和过程调度等领域有广泛的应用。管理信息系统问题的规模日益扩大,这表明使用分布式内存硬件作为提供必要的计算和内存资源的一种经济有效的方法。Luby提出了四种随机算法来解决MIS问题。所有这些算法都是围绕共享内存机器设计的,并使用PRAM模型进行了分析。这些算法没有直接高效的分布式内存实现。在本文中,我们将Luby的两个开创性的MIS算法“Luby(A)”和“Luby(B)”扩展到分布式内存执行,并评估了它们的性能。对于两种类型的合成图输入,我们将我们的结果与组合BLAS库中的“过滤MIS”实现进行比较。
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