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2nd symposium on large-Scale data Analysis and visualization, LDAV 2012

作者： Atanasov, Atanas Srinivasan, Madhusudhanan Weinzierl, Tobias Technische Universität München Germany King Abdullah University of Science and Technology Germany

ISBN: (纸本)9781467347334

Recent advances in supercomputing capabilities pose a multi-faceted data retrieval challenge to the exploration and visualisation of the obtained results: the bandwidth between visualisation devices and the high-performance computing (HPC) clusters neither scales with the simulation data nor with the compute power, the total memory footprint of the data on the supercomputer often exceeds the aggregate memory on the visualisation, and the data has to be distributed among several visualisation nodes working in parallel to render a visual. In the present paper, we introduce an on-demand data exploration paradigm that leverages HPC capabilities and distributed visualisation without requiring a large memory footprint on the visualisation cluster. Regions of interest within the data are specified by the user in the form of queries. These queries, augmented by node identifiers on the visualisation cluster, are automatically distributed among multiple compute nodes of the HPC cluster. The compute nodes work in parallel to assemble and merge data in response to the user query until the data distribution matches the visualisation cluster's topology. Query results are then simultaneously streamed to the right visualisation nodes. Our approach allows for interactive exploration of data residing on HPC resources, irrespective of memory footprint. The streaming of data to the visualisation nodes scales with the bandwidth of the interconnecting network and the HPC cluster's domain decomposition, while the latter is hidden from the visualisation and can change dynamically. We demonstrate the capability of our query-driven approach with a turbulent mixing dataset, and show that it supports interactive data exploration on HPC systems. © 2012 ieee.

关键词： visualization

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Foveated Encoding for large High-Resolution Displays

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2021年第2期27卷 1850-1859页

作者： Friess, Florian Braun, Matthias Bruder, Valentin Frey, Steffen Reina, Guido Ertl, Thomas Univ Stuttgart Stuttgart Germany Univ Groningen Groningen Netherlands

Collaborative exploration of scientific data sets across large high-resolution displays requires both high visual detail as well as low-latency transfer of image data (oftentimes inducing the need to trade one for the other). In this work, we present a system that dynamically adapts the encoding quality in such systems in a way that reduces the required bandwidth without impacting the details perceived by one or more observers. Humans perceive sharp, colourful details, in the small foveal region around the centre of the field of view, while information in the periphery is perceived blurred and colourless. We account for this by tracking the gaze of observers, and respectively adapting the quality parameter of each macroblock used by the H.264 encoder, considering the so-called visual acuity fall-off. This allows to substantially reduce the required bandwidth with barely noticeable changes in visual quality, which is crucial for collaborative analysis across display walls at different locations. We demonstrate the reduced overall required bandwidth and the high quality inside the foveated regions using particle rendering and parallel coordinates.

关键词： visualization Bandwidth Encoding Streaming media Image coding Hardware Computational modeling large high-resolution displays Fovetaed Encoding Remote Visualisation

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visualization of large-scale customer satisfaction surveys using a parallel coordinate tree

Visualization of large-scale customer satisfaction surveys u...

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9th Annual ieee symposium on Information visualization (INFOVIS 2003)

作者： Brodbeck, D Girardin, L Macrofocus GmbH Germany

ISBN: (纸本)0780381548

Satisfaction surveys are an important measurement tool in fields such as market research or human resources management. Serious studies consist of numerous questions and contain answers from large population samples. Aggregation on both sides, the questions asked as well as the answers received, turns the multidimensional problem into a complex system of interleaved hierarchies. Traditional ways of presenting the results are limited to one-dimensional charts and cross-tables. We developed a visualization method called the parallel Coordinate Tree that combines multidimensional analysis with a tree structure representation. Distortion-oriented focus+context techniques are used to facilitate interaction with the visualization. In this paper we present a design study of a commercial application that we built, using this method to analyze and communicate results from large-scale customer satisfaction surveys.

关键词： parallel coordinates focus plus context hierarchical data satisfaction survey

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data parallel Hypersweeps for in Situ Topological Analysis 10

Data Parallel Hypersweeps for in Situ Topological Analysis

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10th ieee symposium on large data Analysis and visualization (LDAV)

作者： Hristov, Petar Weber, Gunther H. Carr, Hamish A. Rubel, Oliver Ahrens, James P. Univ Leeds Leeds W Yorkshire England Lawrence Berkeley Natl Lab Berkeley CA USA Univ Calif Davis Davis CA 95616 USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781728184685

The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze large data sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical data analysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.

关键词： branch decomposition contour tree geometric measures in situ scalar field

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Distributed Volumetric Neural Representation for in situ visualization and Analysis 12

Distributed Volumetric Neural Representation for in situ Vis...

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12th ieee symposium on large-Scale data Analysis and visualization (LDAV)

作者： Wu, Qi Insley, Joseph A. Mateevitsi, Victor A. Rizzi, Silvio Ma, Kwan-Liu Argonne Natl Lab Argonne IL 60439 USA Univ Calif Davis CA 95616 USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (数字)9781665491563

ISBN: (纸本)9781665491563

Volumetric grids have recently been used by many recent works for representing complex scenes implicitly. A volumetric neural representation can be several orders of magnitude smaller in size while still preserving most of high-frequency details. However, most volumes used in large-scale in situ visualization and analysis are partitioned and generated directly in parallel. Therefore, a compatible technique to create volumetric neural representations for these situations is much needed. In this project, we explore the possibility of constructing and optimizing such a representation for large-scale distributed volumes. We present our preliminary results in this poster. We also outline our plans to integrate our techniques with existing in situ visualization and analysis pipelines.

关键词： data analysis Pipelines data visualization

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The perceptual scalability of visualization

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2006年第5期12卷 837-844页

作者： Yost, Beth North, Chris IEEE

larger, higher resolution displays can be used to increase the scalability of information visualizations. But just how much can scalability increase using larger displays before hitting human perceptual or cognitive limits? Are the same visualization techniques that are. good on a single monitor also the techniques that are best when they are scaled up using large, high-resolution displays? To answer these questions we performed a controlled experiment on user performance time, accuracy, and subjective workload when scaling up data quantity with different space-time-attribute visualizations using a large, tiled display. Twelve college students used small multiples, embedded bar matrices, and embedded time-series graphs either on a 2 megapixel (Mp) display or with data scaled up using a 32 Mp tiled display. Participants performed various overview and detail tasks on geospatially-referenced multidimensional time-series data. Results showed that current designs are perceptually scalable because they result in a decrease in task completion time when normalized per number of data attributes along with no decrease in accuracy. It appears that, for the visualizations selected for this study, the relative comparison between designs is generally consistent between display sizes. However, results also suggest that encoding is more important on a smaller display while spatial grouping is more important on a larger display. Some suggestions for designers are provided based on our experience designing visualizations for large displays.

关键词： information visualization large displays empirical evaluation

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DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using data-parallel Primitives 8

DPP-PMRF: Rethinking Optimization for a Probabilistic Graphi...

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8th ieee symposium on large-Scale data Analysis and visualization (LDAV)

作者： Lessley, Brenton Perciano, Talita Heinemann, Colleen Camp, David Childs, Hank Bethel, E. Wes Univ Oregon Eugene OR 97403 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781538668733

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).

关键词： Mathematics of computing-Graph algorithms Markov networks Expectation maximization Computing methodologies-Image processing Shared memory

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Viz3D:: Effective exploratory visualization of large multidimensional data sets

<i>Viz3D</i>:: Effective exploratory visualization of large ...

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17th Brazilian symposium on Computer graphics and Image Processing/2nd Ibero-American symposium on Computer graphics

作者： Artero, AO de Oliveira, MCF Univ Sao Paulo Inst Ciencias Matemat & Computacao BR-13560970 Sao Carlos SP Brazil

ISBN: (纸本)0769522270

We propose a multidimensional visualization technique, named Viz3D, that creates a 3D representation of n-dimensional data that may be interactively manipulated by users to handle visual cluttering and object occlusion. The projection performed in Viz3D is comparable in quality with the 3D projections obtained with well-known dimensionality reduction techniques, at a lower complexity cost. nile a 3D projection conveys more information, giving the user more control of the visual representation and an additional dimension, as compared to 2D, visual cluttering and object occlusion are still a problem in handling large multidimensional data sets. To produce more effective visualizations, two strategies are introduced Dimensionality is handled with a similarity clustering of attributes prior to projection. data set size is handled with a new strategy of visualizing data densities, rather than individual data records. Both the direct and density Viz3D visualizations provide the basis for a user driven visual clustering approach applicable to high-dimensional data sets that is very simple, intuitive and effective.

关键词： visualization

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EGPGV 2022 - 22nd Eurographics symposium on parallel graphics and visualization

EGPGV 2022 - 22nd Eurographics Symposium on Parallel Graphic...

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22nd Eurographics symposium on parallel graphics and visualization, EGPGV 2022

ISBN: (纸本)9783038681755

The proceedings contain 8 papers. The topics discussed include: rainbow: a rendering-aware index for high-quality spatial scatterplots with result-size budgets & profiling and visualizing GPU memory access and cache behavior of ray tracers;iterative discrete element solver for efficient snow simulation;massively parallel large scale inundation modelling & a flexible data streaming design for interactive visualization of large-scale volume data;automatic in situ camera placement for isosurfaces of large-scale scientific simulations;design and evaluation of a GPU streaming framework for visualizing time-varying AMR data;and GraphWaGu: GPU powered large scale graph layout computation and rendering for the web.

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FacetMap: A scalable search and browse visualization

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2006年第5期12卷 797-804页

作者： Smith, Greg Czerwinski, Mary Meyers, Brian Robbins, Daniel Robertson, George Tan, Desney S. Microsoft Research. gregsmi@***

The dominant paradigm for searching and browsing large data stores is text-based: presenting a scrollable list of search results in response to textual search term input. While this works well for the Web, there is opportunity for improvement in the domain of personal information stores, which tend to have more heterogeneous data and richer metadata. In this paper, we introduce FacetMap, an interactive, query-driven visualization, generalizable to a wide range of metadata-rich data stores. FacetMap uses a visual metaphor for both input (selection of metadata facets as filters) and output. Results of a user study provide insight into tradeoffs between FacetMap's graphical approach and the traditional text-oriented approach.

关键词： graphical visualization interactive information retrieval faceted metadata

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