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.

翻译：图划分广泛应用于欺诈检测、社交网络分析等现实场景，以支持大规模图的分布式图计算。然而，现有方法仅考虑复制因子而忽略实际数据中心中机器的差异（包括计算能力、网络带宽和内存大小），无法在具有不同性能的机器间平衡计算代价与通信代价。本文提出通用图划分算法WindGP，能够支持异构机器上的快速高质量边划分。WindGP设计新型预处理技术，根据图与机器特征简化度量指标并平衡计算代价；同时提出最佳优先搜索替代BFS与DFS以生成高内聚簇；此外，WindGP通过精细的局部搜索方法自适应调整划分结果。大量实验表明，在稠密与稀疏分布式图算法上，WindGP性能达到现有最优划分方法的1.35-27倍，且对图规模与机器数量具有良好的可扩展性。