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) |
|---|---|
| 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 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 9/5/17 → 9/8/17 |
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Keywords
- Dragonfly networks
- Performance analysis
- Visual analytics
- Visualization
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Signal Processing
Cite this
Visual Analytics Techniques for Exploring the Design Space of Large-Scale High-Radix Networks. / Li, Jianping Kelvin; Mubarak, Misbah; Ross, Robert B.; Carothers, Christopher D.; Ma, Kwan-Liu.
Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017. Vol. 2017-September Institute of Electrical and Electronics Engineers Inc., 2017. p. 193-203 8048931.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Visual Analytics Techniques for Exploring the Design Space of Large-Scale High-Radix Networks
AU - Li, Jianping Kelvin
AU - Mubarak, Misbah
AU - Ross, Robert B.
AU - Carothers, Christopher D.
AU - Ma, Kwan-Liu
PY - 2017/9/22
Y1 - 2017/9/22
N2 - 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.
AB - 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.
KW - Dragonfly networks
KW - Performance analysis
KW - Visual analytics
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85032644966&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032644966&partnerID=8YFLogxK
U2 - 10.1109/CLUSTER.2017.26
DO - 10.1109/CLUSTER.2017.26
M3 - Conference contribution
AN - SCOPUS:85032644966
VL - 2017-September
SP - 193
EP - 203
BT - Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -