HyperG: Multilevel GPU-Accelerated k-way Hypergraph Partitioner

Wan-Luan Lee, Dian-Lun Lin, Cheng-Hsiang Chiu, Ulf Schlichtmann, Tsung-Wei Huang

Abstract: Hypergraph partitioning plays a critical role in computer-aided design (CAD) because it allows us to break down a large circuit into several manageable pieces that facilitate efficient CAD algorithm designs. However, as circuit designs continue to grow in size, hypergraph partitioning becomes increasingly time-consuming. Recent research has introduced parallel hypergraph partitioners using multi-core CPUs to reduce the long runtime. However, the speedup of existing CPU parallel hypergraph partitioners is typically limited to a few cores. To overcome these challenges, we propose HyperG, a GPU-accelerated multilevel k-way hypergraph partitioning algorithm. HyperG introduces an innovative balanced group coarsening and a sequence-based refinement algorithm to accelerate both the coarsening and uncoarsening stages. Experimental results show that HyperG outperforms both the state-of-the-art sequential and CPU-based parallel partitioners with an average speedup of 133× and 4.1× while achieving comparable partitioning quality.