  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.
