Scalable Edge Partitioning

TL;DR

This paper presents a scalable distributed algorithm for edge partitioning in large networks, improving parallelism in edge-centric computations on massive graphs.

Contribution

It introduces a high-quality, scalable distributed memory algorithm for edge partitioning that efficiently handles networks with billions of edges.

Findings

Algorithm scales to billions of edges

Runs efficiently on thousands of processing elements

Achieves high-quality partitions on real-world networks

Abstract

Edge-centric distributed computations have appeared as a recent technique to improve the shortcomings of think-like-a-vertex algorithms on large scale-free networks. In order to increase parallelism on this model, edge partitioning - partitioning edges into roughly equally sized blocks - has emerged as an alternative to traditional (node-based) graph partitioning. In this work, we give a distributed memory parallel algorithm to compute high-quality edge partitions in a scalable way. Our algorithm scales to networks with billions of edges, and runs efficiently on thousands of PEs. Our technique is based on a fast parallelization of split graph construction and a use of advanced node partitioning algorithms. Our extensive experiments show that our algorithm has high quality on large real-world networks and large hyperbolic random graphs, which have a power law degree distribution and are therefore specifically targeted by edge partitioning