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An effective algorithm for multiway hypergraph partitioning

机译:一种有效的多路超图分割算法

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In this paper, we propose an effective multiway hypergraph partitioning algorithm. We introduce the concept of net-gain and embed it in the selection of cell moves. Unlike traditional FM-based iterative improvement algorithms in which the selection of the next cell to move is only based on its cell gain, our algorithm selects a cell based on both its cell-gain and the sum of all net-gains for those nets incident to the cell. To escape from local optima and to search broader solution space, we propose a new perturbation mechanism. These two strategies significantly enhance the solution quality produced by our algorithm. Based on our experimental justification, we smoothly decrease the number of iterations from pass to pass to reduce the computational effort so that our algorithm can partition large benchmark circuits with reasonable run time. Compared with the recent multiway partitioning algorithms proposed by Dasdan and Aykanat, our algorithm significantly outperforms theirs in term of solution quality (cutsize) and run time, the average improvements in terms of average cutsize over their PLM3 and PFM3 are 47.64% and 36.76% with only 37.17% and 9.66% of their run time, respectively.
机译:在本文中,我们提出了一种有效的多路超图分割算法。我们介绍净收益的概念,并将其嵌入到单元移动的选择中。与传统的基于FM的迭代改进算法不同,后者仅根据其单元增益来选择要移动的下一个单元,而我们的算法会基于其单元增益和所有这些网络事件的所有净增益之和来选择一个单元到细胞。为了摆脱局部最优并寻找更广阔的解空间,我们提出了一种新的扰动机制。这两种策略显着提高了我们算法产生的解决方案质量。根据我们的实验依据,我们可以平滑地减少遍历之间的迭代次数,从而减少了计算量,因此我们的算法可以在合理的运行时间下划分大型基准电路。与Dasdan和Aykanat提出的最新多路分区算法相比,我们的算法在解决方案质量(剪切大小)和运行时间方面明显优于他们的算法,与PLM3和PFM3相比,平均剪切大小的平均改进为47.64%和36.76%,其中分别只有其运行时间的37.17%和9.66%。

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