2017 IEEE International Conference on Cluster Computing (CLUSTER) (2017)
Honolulu, Hawaii, United States
Sept. 5, 2017 to Sept. 8, 2017
High-radix, low-diameter, hierarchical networks based on the Dragonfly topology are common picks for building next generation HPC systems. However, effective tools are lacking for analyzing the network performance and exploring the design choices for such emerging networks at scale. In this paper, we present visual analytics methods that couple data aggregation techniques with interactive visualizations for analyzing large-scale Dragonfly networks. We create an interactive visual analytics system based on these techniques. To facilitate effective analysis and exploration of network behaviors, our system provides intuitive, scalable visualizations that can be customized to show various traffic characteristics and correlate between different performance metrics. Using high-fidelity network simulation and HPC applications communication traces, we demonstrate the usefulness of our system with several case studies on exploring network behaviors at scale with different workloads, routing strategies, and job placement policies. Our simulations and visualizations provide valuable insights for mitigating network congestion and inter-job interference.
Data visualization, Visual analytics, Network topology, Analytical models, Space exploration, Data models, Tools
J. K. Li, M. Mubarak, R. B. Ross, C. D. Carothers and K. Ma, "Visual Analytics Techniques for Exploring the Design Space of Large-Scale High-Radix Networks," 2017 IEEE International Conference on Cluster Computing (CLUSTER), Honolulu, Hawaii, United States, 2017, pp. 193-203.