Parallel streaming graph partitioning utilizing multilevel framework

Abstract

Graph partitioning is widely used for e cient parallelization of a variety of applications. Streaming graph partitioning is a one pass partitioning solution provided to overcome high computation costs of o ine graph partitioners. Even though these streaming algorithms can be used for successively repartitioning, aiming at further improvements in partitioning qualities, quality improvements is limited to few passes that make o ine graph partitioning tools still a desirable solution for graph partitioning due to the generated high quality partitions. We propose a multilevel approach using streaming algorithms that can alleviate tradeo between quality and performance in graph partitioning problem. Moreover, our OpenMP based multi-threaded implementation, can generate fast and highly scalable solutions compared to mt-metis, a multi-threaded solution for METIS, the state-of-the-art o ine high quality graph partitioning tool. Our results show that our method can produce up to fteen times faster and more scalable results in large graph datasets. We also show that our method can improve quality of partitions signi cantly compared to state-of-the-art streaming graph partitioning algorithm LDG after repartitioning several times. On average we produce partitions with 29% better qualities than LDG algorithm.