ConnectIt：一个静态和增量并行图连通性算法框架
ConnectIt: A Framework for Static and Incremental Parallel Graph Connectivity Algorithms
摘要：
连通分支是图应用中的一个基本核心。现有最快的并行多核连通性算法是基于某种形式的边缘采样和/或链接和压缩树。然而，这些设计选择的许多组合尚未被探索。在本文中，我们设计了ConnectIt框架，它提供了不同的采样策略以及各种树链接和压缩方案。ConnectIt使我们能够获得数百种新的连通性算法，其中大部分扩展到计算生成森林。除了静态图，我们还扩展了ConnectIt，以支持并发设置中的插入和连接查询的混合。我们提出了一个实验评估ConnectIt的72核机器上，我们认为这是最全面的评估并行连接算法的日期。与最先进的静态多核算法的集合相比，我们获得了12.4倍的平均加速比（每个图的最快现有实现的平均加速比为2.36倍）。使用ConnectIt，我们能够使用72核机器在10秒内计算最大的公共可用图（拥有超过35亿个顶点和1280亿条边）的连接性，在任何计算环境中，都可以提供3.1倍的加速比。对于我们的增量算法，我们表明，我们的算法可以摄取图形更新高达数十亿边每秒。为了指导用户在不同情况下选择ConnectIt中的最佳变体，我们提供了对不同策略的详细分析。最后，我们展示了如何在ConnectIt的技术可以用来加快两个重要的图形应用程序：近似最小生成森林和扫描聚类。
英文摘要：
Connected components is a fundamental kernel in graph applications. The fastest existing parallel multicore algorithms for connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.
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。
