State-of-the-art hypergraph partitioners utilize a multilevel paradigm to construct progressively coarser hypergraphs across multiple layers, guiding cut refinements at each level of the hierarchy. Traditionally, these partitioners employ heuristic methods for coarsening and do not consider the structural features of hypergraphs. In this work, we introduce a multilevel spectral framework, SHyPar, for partitioning large-scale hypergraphs by leveraging hyperedge effective resistances and flow-based community detection techniques. Inspired by the latest theoretical spectral clustering frameworks, such as HyperEF and HyperSF, SHyPar aims to decompose large hypergraphs into multiple subgraphs with few inter-partition hyperedges (cut size). A key component of SHyPar is a flow-based local clustering scheme for hypergraph coarsening, which incorporates a max-flow-based algorithm to produce clusters with substantially improved conductance. Additionally, SHyPar utilizes an effective resistance-based rating function for merging nodes that are strongly connected (coupled). Compared with existing state-of-the-art hypergraph partitioning methods, our extensive experimental results on real-world VLSI designs demonstrate that SHyPar can more effectively partition hypergraphs, achieving state-of-the-art solution quality.

翻译：当前最先进的超图划分器采用多层范式，在多个层级上构建逐渐粗化的超图，并在层次结构的每一级引导割集优化。传统上，这些划分器采用启发式方法进行粗化，且未考虑超图的结构特征。本研究提出一种多层谱框架SHyPar，通过利用超边有效电阻和基于流的社区检测技术来划分大规模超图。受HyperEF和HyperSF等最新理论谱聚类框架的启发，SHyPar旨在将大型超图分解为多个子图，并尽量减少子图间的超边数量（割集规模）。SHyPar的核心组件是一个基于流的超图粗化局部聚类方案，该方案采用基于最大流的算法生成具有显著改善电导率的聚类簇。此外，SHyPar利用基于有效电阻的评分函数来合并强连接（耦合）节点。通过在真实世界VLSI设计上的大量实验表明，与现有最先进的超图划分方法相比，SHyPar能够更有效地划分超图，达到业界领先的求解质量。