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：图分割广泛应用于欺诈检测、社交网络分析等实际场景中，以实现大规模图的分布式计算。然而，现有工作仅考虑了复制因子，忽略了真实数据中心中机器性能差异（包括计算能力、网络带宽和内存大小），未能平衡异构机器的计算开销与通信开销。本文提出通用图分割算法WindGP，支持在异构机器上进行快速高质量的边分割。WindGP通过创新的预处理技术根据图与机器特征简化度量指标并平衡计算开销；同时，采用最佳优先搜索替代BFS和DFS生成高内聚簇；此外，通过精细化的局部搜索方法自适应调整分割结果。大量实验表明，在稠密与稀疏分布式图算法中，WindGP的性能优于所有最先进的分割方法1.35至27倍，且在图规模与机器数量方面具有良好的可扩展性。