BatchLayout：一种共享存储器上的批并行力导向图布局算法
BatchLayout: A Batch-Parallel Force-Directed Graph Layout Algorithm in Shared Memory
摘要：
力导向算法被广泛用于生成美观的图形或网络布局，出现在许多科学学科中。为了实现大规模图形的可视化，文献中讨论了几种并行算法。然而，现有的并行算法不能有效地利用存储层次结构，并且通常提供有限的并行性。本文使用BatchLayout解决了这些限制，BatchLayout是一种将顶点分组为minibatch并并行处理它们的算法。BatchLayout还使用缓存阻塞技术来有效地利用存储层次结构。更多的并行性和改进的内存访问，再加上强制近似技术，更好的初始化和优化的学习率，使BatchLayout比其他最先进的算法（如ForceLayout 2和OpenOrd）快得多。BatchLayout中布局的可视化质量与类似的可视化工具相当或更好。我们所有的源代码、数据集链接、结果和日志文件都可以在https://github.com/khaled-rahman/BatchLayout上找到。
英文摘要：
Force-directed algorithms are widely used to generate aesthetically pleasing layouts of graphs or networks arisen in many scientific disciplines. To visualize large-scale graphs, several parallel algorithms have been discussed in the literature. However, existing parallel algorithms do not utilize memory hierarchy efficiently and often offer limited parallelism. This paper addresses these limitations with BatchLayout, an algorithm that groups vertices into minibatches and processes them in parallel. BatchLayout also employs cache blocking techniques to utilize memory hierarchy efficiently. More parallelism and improved memory accesses coupled with force approximating techniques, better initialization, and optimized learning rate make BatchLayout significantly faster than other state-of-the-art algorithms such as ForceAtlas2 and OpenOrd. The visualization quality of layouts from BatchLayout is comparable or better than similar visualization tools. All of our source code, links to datasets, results and log files are available at https://github.com/khaled-rahman/BatchLayout.
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。
