Down to Earth - How to Visualize Traffic on High-dimensional Torus Networks

Theisen, Lucas; Shah, Abad; Wolf, Felix

doi:10.1109/vpa.2014.6

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…The approach is demonstrated by analyzing network traffic for a simulation running on the IBM Blue architecture. Small multiples with links between them are used by [TSW14] to visualize torus networks.…”

Section: Applicationmentioning

confidence: 99%

State of the Art of Graph Visualization in non‐Euclidean Spaces

Miller,

Bhatia,

Kobourov

2024

Computer Graphics Forum

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Visualizing graphs and networks in non‐Euclidean space can have benefits such as natural focus+context in hyperbolic space and the familiarity of interactions in spherical space. Despite work on these topics going back to the mid 1990s, there is no survey, or a part of a survey for this area of research. In this paper we review and categorize over 60 relevant papers and analyze them by geometry, (e.g., spherical, hyperbolic, torus), by contribution (e.g., technique, evaluation, proof, application), and by graph class (e.g., tree, planar, complex).

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Section: Applicationmentioning

confidence: 99%

State of the Art of Graph Visualization in non‐Euclidean Spaces

Miller,

Bhatia,

Kobourov

2024

Computer Graphics Forum

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show abstract

“…The same hurdle can be found on the works of Isaacs et al (2012) andSchnorr et al (2010), which also attempt to create a whole new three-dimensional viewing tool (just for the sake of performance analysis). Finally, Theisen et al (2014) combined multiple axes onto two-dimensional views:…”

Section: Related Workmentioning

confidence: 99%

Peer Review #3 of "Further enhancing the in situ visualization of performance data in parallel CFD applications (v0.1)"

2021

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“…Predicting user estimates of the runtime of their jobs so as to ensure that they not killed prematurely due to underestimation has also been studied from the point of view of noise and variation [23,66]. Furthermore, several machine learning techniques to predict application performance and detect anomalies for both power and network related variations have been explored [5,9,16,32,43,44,70,73], and the visualization community has come up with novel mechanisms to analyze network traffic and performance data [10,24,31,39,45,69]. However, all the studies listed above have focused on a single objective such as network traffic, or a single constraint such as power.…”

Section: Related Workmentioning

confidence: 99%

Performance optimality or reproducibility

Patki

Thiagarajan

Ayala

et al. 2019

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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The era of extremely heterogeneous supercomputing brings with itself the devil of increased performance variation and reduced reproducibility. There is a lack of understanding in the HPC community on how the simultaneous consideration of network traffic, power limits, concurrency tuning, and interference from other jobs impacts application performance. In this paper, we design a methodology that allows both HPC users and system administrators to understand the trade-off space between optimal and reproducible performance. We present a firstof-its-kind dataset that simultaneously varies multiple system-and user-level parameters on a production cluster, and introduce a new metric, called the desirability score, which enables comparison across different system configurations. We develop a novel, model-agnostic machine learning methodology based on the graph signal theory for comparing the influence of parameters on application predictability, and using a new visualization technique, make practical suggestions for best practices for multi-objective HPC environments. CCS Concepts• Computing methodologies → Parallel computing methodologies, Machine learning, Model development and analysis;• General and reference → Performance.

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Down to Earth - How to Visualize Traffic on High-dimensional Torus Networks

Cited by 11 publications

References 9 publications

State of the Art of Graph Visualization in non‐Euclidean Spaces

State of the Art of Graph Visualization in non‐Euclidean Spaces

Peer Review #3 of "Further enhancing the in situ visualization of performance data in parallel CFD applications (v0.1)"

Performance optimality or reproducibility

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