Distributed Deep Multilevel Graph Partitioning

Abstract

We describe the engineering of the distributed-memory multilevel graph partitioner dKaMinPar. It scales to (at least) 8192 cores while achieving partitioning quality comparable to widely used sequential and shared-memory graph partitioners. In comparison, previous distributed graph partitioners scale only in more restricted scenarios and often induce a considerable quality penalty compared to non-distributed partitioners. When partitioning into a large number of blocks, they even produce infeasible solution that violate the balancing constraint. dKaMinPar achieves its robustness by a scalable distributed implementation of the deep-multilevel scheme for graph partitioning. Crucially, this includes new algorithms for balancing during refinement and coarsening.