FastSV：一种快速收敛的分布存储连通分量算法
FastSV: A Distributed-Memory Connected Component Algorithm with Fast Convergence
摘要：
本文提出了一种新的分布式内存算法FastSV，用于寻找无向图中的连通分支。我们的算法简化了经典的Shiloach-Vishkin算法，并采用了几种新颖而有效的挂钩策略，以加快收敛速度。我们将FastSV的不同步骤映射到线性代数运算，并在可扩展图形库的帮助下实现它们。FastSV使用稀疏操作来避免冗余工作，并优化MPI通信以避免瓶颈。由此产生的算法显示出高性能和可扩展性，因为它可以在30秒内使用262 K内核在Cray XC 40超级计算机上找到具有超过134 B边缘的超链接图的连接组件。FastSV优于最先进的算法，在各种真实世界的图形上的平均加速比为2.21倍（最大4.27倍）。
英文摘要：
This paper presents a new distributed-memory algorithm called FastSV for finding connected components in an undirected graph. Our algorithm simplifies the classic Shiloach-Vishkin algorithm and employs several novel and efficient hooking strategies for faster convergence. We map different steps of FastSV to linear algebraic operations and implement them with the help of scalable graph libraries. FastSV uses sparse operations to avoid redundant work and optimized MPI communication to avoid bottlenecks. The resultant algorithm shows high-performance and scalability as it can find the connected components of a hyperlink graph with over 134B edges in 30 seconds using 262K cores on a Cray XC40 supercomputer. FastSV outperforms the state-of-the-art algorithm by an average speedup of 2.21x (max 4.27x) on a variety of real-world graphs.
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