High-Quality Shared-Memory Graph Partitioning

TL;DR

This paper introduces a multi-level shared-memory parallel graph partitioning method that achieves high speed and quality, effectively handling large, complex networks while maintaining balanced solutions across multiple cores.

Contribution

It presents a novel parallel graph partitioning approach that guarantees balanced solutions and outperforms existing methods in speed and quality on large graphs.

Findings

Achieves high speed-ups on large graphs with multiple cores.

Partitions large graphs into balanced blocks with fewer edge cuts.

Outperforms main competitors in edge cut quality at the same runtime.

Abstract

Partitioning graphs into blocks of roughly equal size such that few edges run between blocks is a frequently needed operation in processing graphs. Recently, size, variety, and structural complexity of these networks has grown dramatically. Unfortunately, previous approaches to parallel graph partitioning have problems in this context since they often show a negative trade-off between speed and quality. We present an approach to multi-level shared-memory parallel graph partitioning that guarantees balanced solutions, shows high speed-ups for a variety of large graphs and yields very good quality independently of the number of cores used. For example, on 31 cores, our algorithm partitions our largest test instance into 16 blocks cutting less than half the number of edges than our main competitor when both algorithms are given the same amount of time. Important ingredients include parallel label propagation for both coarsening and improvement, parallel initial partitioning, a simple yet effective approach to parallel localized local search, and fast locality preserving hash tables.