An accelerated procedure for hypergraph coarsening on the GPU

One of the obstacles in accelerating sparse graph applications using GPUs is load imbalance, which in certain cases causes threads to stall. We investigate a specific application known as hypergraph coarsening and explore a technique for addressing load imbalance. The hypergraph is a generalization of the graph where one edge may connect more than two nodes. Many problems of interest may be expressed in terms of optimal partitioning of hypergraphs where the edge cut is minimized. The most costly step in hypergraph partitioning is hypergraph coarsening, the process of grouping nodes with similar connectivity patterns into one node to yield a new hypergraph with fewer nodes. Hypergraph coarsening proves to be computationally challenging on GPUs because many hypergraphs exhibit an irregular distribution of connections. To address the resulting load imbalance, we explore a novel task allocation scheme to distribute work more evenly among GPU threads.