Designing scalable b-Matching algorithms on distributed memory multiprocessors by approximation.

A b-Matching is a subset of edges M such that at most b(v) edges in M are incident on each vertex v, where b(v) is specified. We present a distributed-memory parallel algorithm, b-Suitor, that computes a b-Matching with more than half the maximum weight in a graph with weights on the edges. The approximation algorithm is designed to have high concurrency and low time complexity. We organize the implementation of the algorithm in terms of asynchronous supersteps that combine computation and communication, and balance the computational work and frequency of communication to obtain high performance. Since the performance of the b-Suitor algorithm is strongly influenced by communication, we present several strategies to reduce the communication volume. We implement the algorithm using a hybrid strategy where inter-node communication uses MPI and intra-node computation is done with OpenMP threads. We demonstrate strong and weak scaling of b-Suitor up to 16K processors on two supercomputers at NERSC. We compute a b-Matching in a graph with 2 billion edges in under 4 seconds using 16K processors.