Incidence Constraints in Hypergraph Partitioning on GPU
For practitioners needing fast, high-quality hypergraph partitioning, this GPU method offers dramatic speedups and improved results, though it is specific to certain constraints.
This work implements a multi-level hypergraph partitioning algorithm on GPU with bounded per-partition size and distinct inbound hyperedges, achieving speedups up to 940x and 2-26% better connectivity over a sequential partitioner.
Hypergraph partitioning is a pervasive NP-hard problem, and accelerating its computation on GPU can both slice time-to-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 940x and 2-26% better results in connectivity over a sequential multi-level partitioner.