@article{844efb04706246ff9f6acdb886247c50,
title = "Visualizing the Topology and Data Traffic of Multi-Dimensional Torus Interconnect Networks",
abstract = "Torus networks are an attractive topology in supercomputing, balancing the tradeoff between network diameter and hardware costs. The nodes in a torus network are connected in a k -dimensional wrap-around mesh where each node has 2k neighbors. Effectively utilizing these networks can significantly decrease parallel communication overhead and in turn the time necessary to run large parallel scientific and data analysis applications. The potential gains are considerable - 5-D torus networks are used in the majority of the top 10 machines in the November 2017 Graph 500 list. However, the multi-dimensionality of these networks makes it difficult for analysts to diagnose ill-formed communication patterns and poor network utilization since human spatial understanding is by and large limited to 3-Ds. We propose a method based on a space-filling Hilbert curve to linearize and embed the network into a ring structure, visualizing the data traffic as flowlines in the ring interior. We compare our method with traditional 2-D embedding techniques designed for high-dimensional data, such as MDS and RadViz, and show that they are inferior to ours in this application. As a demonstration of our approach, we visualize the data flow of a massively parallel scientific code on a 5-D torus network.",
keywords = "Torus, multi-dimensional data, networks, supercomputing",
author = "Shenghui Cheng and Wen Zhong and Isaacs, {Katherine E.} and Klaus Mueller",
note = "Funding Information: This work was supported in part by the Ministry of Science and ICT (MSIT), South Korea, under the ICT Consilience Creative Program supervised by the Institute for Information & Communications Technology Promotion (IITP) under Grant IITP-2017-R0346-16-1007, in part by NSF under Grant IIS 1274 1527200, in part by the Shenzhen Peacock Plan under Grant KQTD2015033114415450, in part by the Shenzhen Fundamental Research Fund under Project ZDSYS201707251409055, and in part by The Pearl River Talent Recruitment Program Innovative and Entrepreneurial Teams in 2017 under Grant 2017ZT07X152. Funding Information: in computer science and the B.A. degree in math-ematics from San Jose State University, the B.S. degree in physics from the California Institute of Technology, and the Ph.D. degree in computer science from the University of California at Davis, Davis, CA, USA. She is currently an Assistant Pro-fessor with The University of Arizona, researching information visualization techniques for perfor-mance analysis. In 2012, she received the Department of Energy Office of Science Graduate Fellowship. Funding Information: KLAUS MUELLER received the Ph.D. degree in computer science from The Ohio State Uni-versity. He is currently a Professor of computer science at Stony Brook University and a Senior Adjunct Scientist at the Brookhaven National Lab. He has authored over 170 papers which were cited over 8100 times. His current research inter-ests include visualization, visual analytics, data science, and medical imaging. He received the U.S. National Science Foundation Early CAREER Award in 2001, the SUNY Chancellor{\textquoteright}s Award for Excellence in Scholarship and Creative Activity in 2011, and the IEEE CS Meritorious Service Certificate in 2016. He is currently the Associate Editor-in-Chief of the IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS.",
year = "2018",
doi = "10.1109/ACCESS.2018.2872344",
language = "English (US)",
volume = "6",
pages = "57191--57204",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}