Incidence Constraints in Hypergraph Partitioning on GPU

Hypergraph partitioning is a pervasive NP-hard problem, and accelerating its computation on GPU can both slice timeto-solution and raise quality of results. In this work, we implement a multi-level hypergraph partitioning algorithm on GPU targeting a specific set of problem constraints: bounded per-partition size and distinct inbound hyperedges. Manipulating hypergraphs requires long orders of nested iterations, and enforcing these constraints introduces further set operations amidst them. Hence, we design algorithms around our problem's specifics, materializing the hypergraph's incidence structure in memory and exploiting set sparsity. Our results show competitive speedups as high as 940× and 2-26% better results in connectivity over a sequential multi-level partitioner.