论文标题
batchlayout：共享内存中的批处理定向图表布局算法
BatchLayout: A Batch-Parallel Force-Directed Graph Layout Algorithm in Shared Memory
论文作者
论文摘要
实力定向算法被广泛用于生成许多科学学科中出现的图形或网络的美学布局。为了可视化大规模图，文献中已经讨论了几种平行算法。但是，现有的并行算法不会有效利用内存层次结构，并且经常提供有限的并行性。本文通过batchlayout解决了这些限制，该算法将顶点分为小匹配并并行处理。 Batchlayout还采用缓存阻塞技术有效地利用内存层次结构。更多的并行性和改进的内存访问，再加上力近似技术，更好的初始化和优化的学习率，比其他最先进的算法（例如Forceatlas2和Openord）更快。与类似的可视化工具相比，BatchLayout的布局可视化质量可比或更好。我们的所有源代码，数据集，结果和日志文件的链接均可在https://github.com/khaled-rahman/batchlayout上找到。
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.
