Abstract
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.
Original language | English (US) |
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Title of host publication | Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 193-203 |
Number of pages | 11 |
Volume | 2017-September |
ISBN (Electronic) | 9781538623268 |
DOIs | |
State | Published - Sep 22 2017 |
Event | 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 - Honolulu, United States Duration: Sep 5 2017 → Sep 8 2017 |
Other
Other | 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 |
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Country | United States |
City | Honolulu |
Period | 9/5/17 → 9/8/17 |
Keywords
- Dragonfly networks
- Performance analysis
- Visual analytics
- Visualization
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Signal Processing