Net Clusting Based Low Complexity Coarsening Algorithm In k-way Hypergraph Partitioning

Abstract

Abstract With the increasing scale of integrated circuits, hypergraph partitioning is usually applied to Very Large Scale Integration (VLSI) circuit layout and other applications to reduce the computational complexity. However, if without properly coarsening, the hypergraph partitioning problem will become intractable along with the increase of the number of vertices. In this paper, we propose a coarsening algorithm in k-way hypergraph partitioning based on net clustering, where net clustering is used to obtain the initial set of vertices with higher internal similarity. Due to the property of nets connecting through vertices, the proposal can cluster the net by local search and discard unimportant vertices from net clusters to achieve high-quality solutions. The experimental results show that our proposal achieves a high quality of coarsening with lower complexity. Even as the number of partitions rises, the computation time reduction will obviously increase. In the case of setting the number of partitions as 8, our algorithm can achieve almost the same partitioning quality as traditional hMetis, but with a time consumption reduction of 50%.