WindGP: Efficient Graph Partitioning on Heterogenous Machines

TL;DR

WindGP is a novel graph partitioning algorithm designed for heterogeneous machines, balancing computation and communication costs, and outperforming existing methods significantly in distributed graph processing tasks.

Contribution

The paper introduces WindGP, a new graph partitioning approach that accounts for machine heterogeneity and employs advanced techniques for high-quality, scalable partitioning.

Findings

Outperforms state-of-the-art methods by 1.35-27 times

Effective on both dense and sparse graphs

Scales well with graph size and number of machines

Abstract

Graph Partitioning is widely used in many real-world applications such as fraud detection and social network analysis, in order to enable the distributed graph computing on large graphs. However, existing works fail to balance the computation cost and communication cost on machines with different power (including computing capability, network bandwidth and memory size), as they only consider replication factor and neglect the difference of machines in realistic data centers. In this paper, we propose a general graph partitioning algorithm WindGP, which can support fast and high-quality edge partitioning on heterogeneous machines. WindGP designs novel preprocessing techniques to simplify the metric and balance the computation cost according to the characteristics of graphs and machines. Also, best-first search is proposed instead of BFS and DFS, in order to generate clusters with high cohesion. Furthermore, WindGP adaptively tunes the partition results by sophisticated local search methods. Extensive experiments show that WindGP outperforms all state-of-the-art partition methods by 1.35 - 27 times on both dense and sparse distributed graph algorithms, and has good scalability with graph size and machine number.