Consistently GPU-Accelerated Graph Visualization

Abstract

Graph visualization is essential for the analysis of networks and relational data sets. Often, most of the effort is expended on computing sophisticated layouts of the visual representation of the graph. Even though this is increasingly accelerated by use of graphics processing units (GPUs), the rendering is often considered as circumstantial. In this paper, we present a coherent approach to graph visualization that utilizes all features of modern GPUs. We describe specialized data structures and our GPU-centric pipeline for computing and rendering a layout, while enabling steering and interaction. We evaluate technical aspects of our approach as well as its applicability to huge real-world graphs.