37-Issue 3EuroVis 2018 - Conference Proceedingshttps://diglib.eg.org:443/handle/10.2312/26323942018-10-16T11:39:25Z2018-10-16T11:39:25ZInteractive Investigation of Traffic Congestion on Fat-Tree Networks Using TREESCOPEBhatia, HarshJain, NikhilBhatele, AbhinavLivnat, YardenDomke, JensPascucci, ValerioBremer, Peer-Timohttps://diglib.eg.org:443/handle/10.1111/cgf13442 2018-06-02T18:09:41Z2018-01-01T00:00:00ZInteractive Investigation of Traffic Congestion on Fat-Tree Networks Using TREESCOPE
Bhatia, Harsh; Jain, Nikhil; Bhatele, Abhinav; Livnat, Yarden; Domke, Jens; Pascucci, Valerio; Bremer, Peer-Timo
Jeffrey Heer and Heike Leitte and Timo Ropinski
Parallel simulation codes often suffer from performance bottlenecks due to network congestion, leaving millions of dollars of investments underutilized. Given a network topology, it is critical to understand how different applications, job placements, routing schemes, etc., are affected by and contribute to network congestion, especially for large and complex networks. Understanding and optimizing communication on large-scale networks is an active area of research. Domain experts often use exploratory tools to develop both intuitive and formal metrics for network health and performance. This paper presents TREESCOPE, an interactive, web-based visualization tool for exploring network traffic on large-scale fat-tree networks. TREESCOPE encodes the network topology using a tailored matrix-based representation and provides detailed visualization of all traffic in the network. We report on the design process of TREESCOPE, which has been received positively by network researchers as well as system administrators. Through case studies of real and simulated data, we demonstrate how TREESCOPE's visual design and interactive support for complex queries on network traffic can provide experts with new insights into the occurrences and causes of congestion in the network.
2018-01-01T00:00:00ZMultiscale Visualization and Exploration of Large Bipartite GraphsPezzotti, NicolaFekete, Jean-DanielHöllt, ThomasLelieveldt, Boudewijn P. F.Eisemann, ElmarVilanova, Annahttps://diglib.eg.org:443/handle/10.1111/cgf134412018-06-02T18:09:39Z2018-01-01T00:00:00ZMultiscale Visualization and Exploration of Large Bipartite Graphs
Pezzotti, Nicola; Fekete, Jean-Daniel; Höllt, Thomas; Lelieveldt, Boudewijn P. F.; Eisemann, Elmar; Vilanova, Anna
Jeffrey Heer and Heike Leitte and Timo Ropinski
A bipartite graph is a powerful abstraction for modeling relationships between two collections. Visualizations of bipartite graphs allow users to understand the mutual relationships between the elements in the two collections, e.g., by identifying clusters of similarly connected elements. However, commonly-used visual representations do not scale for the analysis of large bipartite graphs containing tens of millions of vertices, often resorting to an a-priori clustering of the sets. To address this issue, we present the Who's-Active-On-What-Visualization (WAOW-Vis) that allows for multiscale exploration of a bipartite socialnetwork without imposing an a-priori clustering. To this end, we propose to treat a bipartite graph as a high-dimensional space and we create the WAOW-Vis adapting the multiscale dimensionality-reduction technique HSNE. The application of HSNE for bipartite graph requires several modifications that form the contributions of this work. Given the nature of the problem, a set-based similarity is proposed. For efficient and scalable computations, we use compressed bitmaps to represent sets and we present a novel space partitioning tree to efficiently compute similarities; the Sets Intersection Tree. Finally, we validate WAOWVis on several datasets connecting Twitter-users and -streams in different domains: news, computer science and politics. We show how WAOW-Vis is particularly effective in identifying hierarchies of communities among social-media users.
2018-01-01T00:00:00ZSetCoLa: High-Level Constraints for Graph LayoutHoffswell, JaneBorning, AlanHeer, Jeffreyhttps://diglib.eg.org:443/handle/10.1111/cgf134402018-06-02T18:09:35Z2018-01-01T00:00:00ZSetCoLa: High-Level Constraints for Graph Layout
Hoffswell, Jane; Borning, Alan; Heer, Jeffrey
Jeffrey Heer and Heike Leitte and Timo Ropinski
Constraints enable flexible graph layout by combining the ease of automatic layout with customizations for a particular domain. However, constraint-based layout often requires many individual constraints defined over specific nodes and node pairs. In addition to the effort of writing and maintaining a large number of similar constraints, such constraints are specific to the particular graph and thus cannot generalize to other graphs in the same domain. To facilitate the specification of customized and generalizable constraint layouts, we contribute SetCoLa: a domain-specific language for specifying high-level constraints relative to properties of the backing data. Users identify node sets based on data or graph properties and apply high-level constraints within each set. Applying constraints to node sets rather than individual nodes reduces specification effort and facilitates reapplication of customized layouts across distinct graphs. We demonstrate the conciseness, generalizability, and expressiveness of SetCoLa on a series of real-world examples from ecological networks, biological systems, and social networks.
2018-01-01T00:00:00ZRendering and Extracting Extremal Features in 3D FieldsKindlmann, Gordon L.Chiw, ChariseeHuynh, TriGyulassy, AttilaReppy, JohnBremer, Peer-Timohttps://diglib.eg.org:443/handle/10.1111/cgf134392018-06-02T18:09:34Z2018-01-01T00:00:00ZRendering and Extracting Extremal Features in 3D Fields
Kindlmann, Gordon L.; Chiw, Charisee; Huynh, Tri; Gyulassy, Attila; Reppy, John; Bremer, Peer-Timo
Jeffrey Heer and Heike Leitte and Timo Ropinski
Visualizing and extracting three-dimensional features is important for many computational science applications, each with their own feature definitions and data types. While some are simple to state and implement (e.g. isosurfaces), others require more complicated mathematics (e.g. multiple derivatives, curvature, eigenvectors, etc.). Correctly implementing mathematical definitions is difficult, so experimenting with new features requires substantial investments. Furthermore, traditional interpolants rarely support the necessary derivatives, and approximations can reduce numerical stability. Our new approach directly translates mathematical notation into practical visualization and feature extraction, with minimal mental and implementation overhead. Using a mathematically expressive domain-specific language, Diderot, we compute direct volume renderings and particlebased feature samplings for a range of mathematical features. Non-expert users can experiment with feature definitions without any exposure to meshes, interpolants, derivative computation, etc. We demonstrate high-quality results on notoriously difficult features, such as ridges and vortex cores, using working code simple enough to be presented in its entirety.
2018-01-01T00:00:00Z