GraphChallenge .org三角形计数性能
GraphChallenge.org Triangle Counting Performance
摘要：
图形分析系统的兴起需要新的方法来衡量和比较图形处理系统的能力。MIT/Amazon/IEEE Graph Challenge旨在提供一个定义明确的社区场所，用于激励研究并突出图形分析软件，硬件，算法和系统的创新。GraphChallenge.org提供了广泛的预解析图数据集、图生成器、数学定义的图算法、各种语言的示例串行实现以及用于测量性能的特定度量。GraphChallenge.org的三角形计数组件测试图形处理系统的性能，以计算图形中的所有三角形，并练习许多图形算法中的关键图形操作。在2017年，2018年和2019年，我们收到了来自广泛作者和组织的许多三角形计数提交。本文介绍了这些提交的最佳性能的性能分析。这些提交材料表明，他们最先进的三角形计数执行时间$T_{\rm tri}$是图中边数$N_e$的强函数，从2017年（$T_{\rm tri} \approx (N_e/10^8)^{4/3}$）到2018年（$T_{\rm tri} \approx N_e/10^9$）显著改善，从2018年到2019年保持可比性。Graph Challenge提供了当前图分析系统的清晰画面，并强调了在非常大的图上实现高性能的新创新的必要性。
英文摘要：
The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of pre-parsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. The triangle counting component of GraphChallenge.org tests the performance of graph processing systems to count all the triangles in a graph and exercises key graph operations found in many graph algorithms. In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art triangle counting execution time, $T_{\rm tri}$, is a strong function of the number of edges in the graph, $N_e$, which improved significantly from 2017 ($T_{\rm tri} \approx (N_e/10^8)^{4/3}$) to 2018 ($T_{\rm tri} \approx N_e/10^9$) and remained comparable from 2018 to 2019. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs.
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。
