Improving graph partitioning for modern graphs and architectures

Abstract

Graph partitioning is an important preprocessing step in applications dealing with sparse-irregular data. As such, the ability to efficiently partition a graph in parallel is crucial to the performance of these applications. The number of compute cores in a compute node continues to increase, demanding ever more scalability from shared-memory graph partitioners. In this paper we present algorithmic improvements to the multithreaded graph partitioner mt-Metis. We experimentally evaluate our methods on a 36 core machine, using 20 different graphs from a variety of domains. Our improvements decrease the runtime by 1.5-11.7X and improve strong scaling by 82%.