GPU Hypergraph Partitioning Achieves 380x Speedup with New Algorithm

Hypergraph partitioning is a critical yet NP-hard problem in engineering, essential for tasks like VLSI design and network optimization. Traditional sequential solvers struggle at scale. In a new paper submitted to IEEE TPDS, researchers Marco Ronzani and Cristina Silvano present a GPU-oriented algorithm that tackles the problem under specific constraints: fixed partition sizes and distinct incident hyperedges per partition. Their approach exploits the GPU’s hierarchical parallelism to handle deeply nested traversals and concurrent decision-making. By materializing the hypergraph’s incidence structure in memory and batching node-pairing scores in shared memory, the algorithm achieves dramatic speedups. The refinement phase chains node moves into improving paths and cycles, validating moves via cumulative set size variations reduced in parallel.

Results are striking: an average 380x speedup over a sequential multi-level partitioner, along with a 1.2–2.0x reduction in connectivity. The method also generalizes to k-way balanced partitioning, running 5x faster than CPU methods with only about 5% quality loss for k=2. It outperforms an existing GPU partitioner at comparable runtime without measurable overhead from the added constraint-handling logic. This work marks a significant step toward practical, real-time hypergraph partitioning on commodity GPU hardware, opening the door for larger-scale engineering optimizations.