WindGP：异构机器上的高效图划分
WindGP: Efficient Graph Partitioning on Heterogenous Machines
AI总结：
针对现有图划分方法无法适配异构机器、难以平衡计算与通信成本的问题，本文提出WindGP算法，通过预处理、最佳优先搜索和局部搜索优化划分，性能优于现有方法1.35-27倍且可扩展性良好。
摘要：
图划分广泛应用于欺诈检测、社交网络分析等诸多实际场景，目的是实现大规模图的分布式图计算。然而，现有方法无法在算力（包括计算能力、网络带宽和内存大小）不同的机器上平衡计算成本与通信成本，因为它们仅考虑复制因子，忽略了现实数据中心中的机器差异。本文提出一种通用图划分算法WindGP，可在异构机器上实现快速、高质量的边划分。WindGP设计了新颖的预处理技术，结合图与机器的特性简化度量指标并平衡计算成本；同时提出用最佳优先搜索替代BFS和DFS，以生成高内聚的簇；此外，WindGP还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，在稠密和稀疏分布式图算法上，WindGP的性能优于所有现有最先进划分方法1.35至27倍，且随图规模和机器数量增长具备良好的可扩展性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
