GVE-Louvain：共享内存环境下用于社区检测的快速Louvain算法
GVE-Louvain: Fast Louvain Algorithm for Community Detection in Shared Memory Setting
AI总结：
GVE-Louvain提出一种共享内存下高效多核Louvain社区检测实现，在双路16核CPU上比现有工具快5.8至50倍，处理速率达5.6亿边/秒，且线程扩展性良好。
摘要：
社区检测是识别网络中自然划分的问题。在数据集规模已达到显著尺度的众多应用中，高效并行算法对于识别此类划分至关重要。本技术报告介绍了Louvain算法（一种高质量社区检测方法）最高效的多核实现之一。在配备双路16核Intel Xeon Gold 6226R处理器的服务器上，我们称之为GVE-Louvain的Louvain实现，其速度分别比Vite、Grappolo、NetworKit Louvain和cuGraph Louvain（在NVIDIA A100 GPU上运行）快50倍、22倍、20倍和5.8倍——在38亿条边的图上实现了每秒5.6亿条边的处理速率。此外，GVE-Louvain在线程数每翻倍时，性能平均提升1.6倍。
英文摘要：
Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This technical report presents one of the most efficient multicore implementations of the Louvain algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Louvain, which we term as GVE-Louvain, outperforms Vite, Grappolo, NetworKit Louvain, and cuGraph Louvain (running on NVIDIA A100 GPU) by 50x, 22x, 20x, and 5.8x faster respectively - achieving a processing rate of 560M edges/s on a 3.8B edge graph. In addition, GVE-Louvain improves performance at an average rate of 1.6x for every doubling of threads.
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。
