HySpecPro: Scalable Hypergraph Partitioning via Spectral Projection Optimization
This paper introduces HySpecPro, a single-level hypergraph partitioner that performs end-to-end optimization in a spectral embedding space, delivering cut quality comparable to multilevel methods with linear scalability.
First spectral hypergraph partitioner to perform end-to-end optimization in a spectral embedding space
Before reading this…
Applications
- →VLSI designs
- →Parallel and hierarchical optimization
To understand this paper, make sure you know these concepts first:
- Understanding of hypergraphsfind papers →
- Spectral graph theoryfind papers →
Abstract
More Like ThisModern VLSI designs comprise tens of billions of components, making scalable hypergraph partitioning critical for parallel and hierarchical optimization. Although multilevel partitioning remains the dominant paradigm, its coarsening stage can distort structural information, especially in hypergraphs with many high-degree hyperedges, leading to increased refinement overhead and limited scalability. Recent approaches incorporate spectral information to guide coarsening, but only in a heuristic manner, without directly optimizing the partitioning objectives. We introduce HySpecPro, a single-level hypergraph partitioner that performs end-to-end optimization in a spectral embedding space. HySpecPro constructs embeddings from a bipartite Laplacian and performs efficient projection-based search, supported by a fully GPU-accelerated implementation. Experiments show that HySpecPro delivers cut quality comparable to state-of-the-art multilevel methods while scaling linearly with the total hyperedge degree.