Visualizing the Topology and Data Traffic of Multi-Dimensional Torus Interconnect Networks

Cheng, Shenghui; Zhong, Wen; Isaacs, Katherine E.; Mueller, Klaus

doi:10.1109/access.2018.2872344

Cited by 21 publications

(14 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a plain case, a simpler clockwise enumeration of ports is applied in [4]. For a particular case when d = 2 and k = 2, the p−invariants obtained (generated) on (5) are listed in (6).…”

Section: Analysis Of Hypertorus Grid Modelmentioning

confidence: 99%

“…H YPERCUBE Petri net models [1]− [5] are suited for parallel and distributed computer systems [6], [7], computing grids [7], and systems biology [8]. The nature of a cell is different, but the structures of triangular, square, and hexagonal grids on a plane or a hypercube for a greater number of dimensions, look alike.…”

Section: Introductionmentioning

confidence: 99%

“…Communication devices and cells have similar behavior: a computing or communication device transmits packets, while a bioplast cell transmits substances and impulses. In [6] it is stated that a torus network is the best topology for supercomputing and that using such networks decreases communication overhead considerably; the majority of the top supercomputers, including IBM Blue Gene/Q, use a multidimensional torus.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Verification of hypertorus communication grids by infinite petri nets and process algebra

Zaitsev

Shmeleva

Groote

2019

IEEE/CAA J. Autom. Sinica

View full text Add to dashboard Cite

DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

show abstract

“…For a plain case, a simpler clockwise enumeration of ports is applied in [4]. For a particular case when d = 2 and k = 2, the p−invariants obtained (generated) on (5) are listed in (6).…”

Section: Analysis Of Hypertorus Grid Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Verification of hypertorus communication grids by infinite petri nets and process algebra

Zaitsev

Shmeleva

Groote

2019

IEEE/CAA J. Autom. Sinica

View full text Add to dashboard Cite

show abstract

“…With the involvement of NNM, it is possible to present data structures as graphs. In a variety of research and application areas, data graph modeling and analysis (Kennedy et al 2017) are an important task to be accomplished, which makes graph visualization (Cheng et al 2018) necessary when developing visualization techniques.…”

Section: Density Concentration and Relations Generation Process In Thmentioning

confidence: 99%

DCMDS-RV: density-concentrated multi-dimensional scaling for relation visualization

Smith

Wilamowski

Nelms

2018

J Vis

View full text Add to dashboard Cite

This paper proposes a novel unsupervised multi-dimensional scaling (MDS) method to visualize high-dimensional data and their relations in a low-dimensional (e.g., 2D) space. Different from traditional MDS approaches where the main purpose is to embed high-dimensional data into a low-dimensional space, this study aims to both embed data into a low-dimensional space and reveal data relations, thus providing better visualization as graph. By taking into account the density relationships inherent in data, this paper proposes a new density-concentrated multi-dimensional scaling algorithm DCMDS-RV to perform visualization of high-dimensional data and their relations. One benefit of the proposed DCMDS-RV algorithm is the ability to embed data more accurately than traditional MDS techniques by using second-order gradient optimization instead of first-order gradient. A key advantage of the presented DCMDS-RV algorithm is the capability to show relations as categorical information. In the resulting embedding, data are compact in clusters. The results demonstrate that the proposed DCMDS-RV algorithm outperforms conventional MDS methods regarding Kruskal stress factor and ACC value. The relations between data as graph are clearly viewed as well.

show abstract

“…Accordingly, newer generation visualization tools are needed to assist with the analyses and interpretation of these increasingly high-dimensional and complex data sets. Several resources now offer a variety of techniques for visualizing metabolomics data structure and exploring the inter-relations between individual and groups of metabolites [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

A Single Visualization Technique for Displaying Multiple Metabolite–Phenotype Associations

et al. 2019

View full text Add to dashboard Cite

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel ‘rain plot’ approach to display the results of these analyses. The ‘rain plot’ combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.

show abstract

Visualizing the Topology and Data Traffic of Multi-Dimensional Torus Interconnect Networks

Cited by 21 publications

References 26 publications

Verification of hypertorus communication grids by infinite petri nets and process algebra

Verification of hypertorus communication grids by infinite petri nets and process algebra

DCMDS-RV: density-concentrated multi-dimensional scaling for relation visualization

A Single Visualization Technique for Displaying Multiple Metabolite–Phenotype Associations

Contact Info

Product

Resources

About