一种多层k-way超图划分的并行算法

骈文研究 Pub Date : 2004-07-05 DOI:10.1109/ISPDC.2004.6
Aleksandar Trifunović, W. Knottenbelt
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引用次数: 30

摘要

针对k-way超图划分问题,提出了一种粗粒度并行多层级算法。该算法通过提高处理器利用率、减少同步开销和避免磁盘争用,在运行时间和可伸缩性行为方面显著改进了我们之前的工作。新算法也具有普遍适用性,不再需要输入超图的特定结构来实现良好的分区质量。我们给出的结果表明,该算法在具有/spl Theta/(10/sup 7/)顶点的超大型超图上具有良好的可扩展性,并且在分区质量方面始终优于由最先进的并行图分区工具产生的近似分区,最高可达27%。
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A parallel algorithm for multilevel k-way hypergraph partitioning
In this paper we present a coarse-grained parallel multi-level algorithm for the k-way hypergraph partitioning problem. The algorithm significantly improves on our previous work in terms of run time and scalability behaviour by improving processor utilisation, reducing synchronisation overhead and avoiding disk contention. The new algorithm is also generally applicable and no longer requires a particular structure of the input hypergraph to achieve a good partition quality. We present results which show that the algorithm has good scalability properties on very large hypergraphs with /spl Theta/(10/sup 7/) vertices and consistently outperforms the approximate partitions produced by a state-of-the-art parallel graph partitioning tool in terms of partition quality, by up to 27%.
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