算法1003

T. Davis, W. Hager, Scott P. Kolodziej, S. Yeralan
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引用次数: 1

摘要

从并行计算到超大规模集成电路设计,再到稀疏矩阵算法,图分割在许多领域都是一种常见而有用的操作。本文介绍了多层混合图划分算法Mongoose及其库。在多层划分框架和组合方法的基础上,我们引入了新的减少失速和无失速粗化策略,以及利用(1)传统组合方法和(2)连续二次规划公式的高效混合算法。我们展示了这种新的混合算法如何在孤立的情况下优于任何一种策略,我们还比较了Mongoose和METIS,并展示了它在大型和社交网络(幂律)图上的有效性。
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Algorithm 1003
Partitioning graphs is a common and useful operation in many areas, from parallel computing to VLSI design to sparse matrix algorithms. In this article, we introduce Mongoose, a multilevel hybrid graph partitioning algorithm and library. Building on previous work in multilevel partitioning frameworks and combinatoric approaches, we introduce novel stall-reducing and stall-free coarsening strategies, as well as an efficient hybrid algorithm leveraging (1) traditional combinatoric methods and (2) continuous quadratic programming formulations. We demonstrate how this new hybrid algorithm outperforms either strategy in isolation, and we also compare Mongoose to METIS and demonstrate its effectiveness on large and social networking (power law) graphs.
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