Practical Parallel Hypergraph Algorithms Julian Shun
[email protected] MIT CSAIL Abstract v0 While there has been significant work on parallel graph pro- e0 cessing, there has been very surprisingly little work on high- v0 v1 v1 performance hypergraph processing. This paper presents a e collection of efficient parallel algorithms for hypergraph pro- 1 v2 cessing, including algorithms for computing hypertrees, hy- v v 2 3 e perpaths, betweenness centrality, maximal independent sets, 2 v k-core decomposition, connected components, PageRank, 3 and single-source shortest paths. For these problems, we ei- (a) Hypergraph (b) Bipartite representation ther provide new parallel algorithms or more efficient imple- mentations than prior work. Furthermore, our algorithms are Figure 1. An example hypergraph representing the groups theoretically-efficient in terms of work and depth. To imple- fv0;v1;v2g, fv1;v2;v3g, and fv0;v3g, and its bipartite repre- ment our algorithms, we extend the Ligra graph processing sentation. framework to support hypergraphs, and our implementations benefit from graph optimizations including switching between improved compared to using a graph representation. Unfor- sparse and dense traversals based on the frontier size, edge- tunately, there is been little research on parallel hypergraph aware parallelization, using buckets to prioritize processing processing. of vertices, and compression. Our experiments on a 72-core The main contribution of this paper is a suite of efficient machine and show that our algorithms obtain excellent paral- parallel hypergraph algorithms, including algorithms for hy- lel speedups, and are significantly faster than algorithms in pertrees, hyperpaths, betweenness centrality, maximal inde- existing hypergraph processing frameworks.