Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY

TorusVisND: Unraveling High-Dimensional Torus Networks for Network Traffic Visualizations

Shenghui Cheng, Pradipta De, Shaofeng H.-C. Jiang, Klaus Mueller

Abstract: Torus networks are widely used in supercomputing. However, due to their complex topology and their large number of nodes, it is difficult for analysts to perceive the messages flow in these networks. We propose a visualization framework called TorusVisND that uses modern information visualization techniques to allow analysts to see the network and its communication patterns in a single display and control the amount of information shown via filtering in the temporal and the topology domains. For this purpose we provide three cooperating visual interfaces. The main interface is the network display. It uses two alternate graph numbering schemes – a sequential curve and a Hilbert curve – to unravel the 5D torus network into a single string of nodes. We then arrange these nodes onto a circle and add the communication links as line bundles in the circle interior. A node selector based on parallel coordinates and a time slicer based on ThemeRiver help users focus on certain processor groups and time slices in the network display. We demonstrate our approach via a small use case.

Teaser: Seen below is the complete interface of TorusVisND with its three components:

Teaser Image

In the above: (a) network display (b) node selector and (c) time slicer. The network display currently shows the traffic across the nodes chosen in the node selector when time=25 as selected in the time slicer. The time slicer shows the message flow across the nodes chosen in the red rectangle

Video: Watch it to get a quick overview:

Paper: S. Cheng, P. De, S. Jiang, K. Mueller, "TorusVisND: Unraveling High-Dimensional Torus Networks for Network Traffic Visualizations," Proc. First Workshop on Visual Performance Analysis (jointly with Supercomputing 2014), pp. 9-16, New Orleans, LA, November 2014. pdf ppt

Funding: NSF grant IIS-1117132