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检索条件"任意字段=IEEE Symposium on Parallel and Large-Data Visualization and Graphics"

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ieee visualization and graphics Technical Committee (VGTC)

IEEE Visualization and Graphics Technical Committee (VGTC)

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ieee symposium on large data Analysis and visualization (LDAV)

Provides a listing of current committee members.

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Dax Toolkit: A proposed framework for data analysis and visualization at Extreme Scale

Dax Toolkit: A proposed framework for data analysis and visu...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Kenneth Moreland Utkarsh Ayachit Berk Geveci Kwan-Liu Ma Sandia National Laboratories Kitware Inc. University of California at Davis

Experts agree that the exascale machine will comprise processors that contain many cores, which in turn will necessitate a much higher degree of concurrency. Software will require a minimum of a 1,000 times more concurrency. Most parallel analysis and visualization algorithms today work by partitioning data and running mostly serial algorithms concurrently on each data partition. Although this approach lends itself well to the concurrency of current high-performance computing, it does not exhibit the appropriate pervasive parallelism required for exascale computing. The data partitions are too small and the overhead of the threads is too large to make effective use of all the cores in an extreme-scale machine. This paper introduces a new visualization framework designed to exhibit the pervasive parallelism necessary for extreme scale machines. We demonstrate the use of this system on a GPU processor, which we feel is the best analog to an exascale node that we have available today.

关键词： Pipelines data visualization Concurrent computing graphics processing unit Instruction sets parallel processing Algorithm design and analysis

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parallel visualization on large clusters using MapReduce

Parallel visualization on large clusters using MapReduce

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Huy T. Vo Jonathan Bronson Brian Summa João L.D. Comba Juliana Freire Bill Howe Valerio Pascucci Cláudio T. Silva Polytechnic Institute of New York University USA SCI Institute University of Utah USA Instituto de Informática UFRGS Brazil EScience Institute University of Washington USA

large-scale visualization systems are typically designed to efficiently “push” datasets through the graphics hardware. However, exploratory visualization systems are increasingly expected to support scalable data manipulation, restructuring, and querying capabilities in addition to core visualization algorithms. We posit that new emerging abstractions for parallel data processing, in particular computing clouds, can be leveraged to support large-scale data exploration through visualization. In this paper, we take a first step in evaluating the suitability of the MapReduce framework to implement large-scale visualization techniques. MapReduce is a lightweight, scalable, general-purpose parallel data processing framework increasingly popular in the context of cloud computing. Specifically, we implement and evaluate a representative suite of visualization tasks (mesh rendering, isosurface extraction, and mesh simplification) as MapReduce programs, and report quantitative performance results applying these algorithms to realistic datasets. For example, we perform isosurface extraction of up to l6 isovalues for volumes composed of 27 billion voxels, simplification of meshes with 30GBs of data and subsequent rendering with image resolutions up to 80000 2 pixels. Our results indicate that the parallel scalability, ease of use, ease of access to computing resources, and fault-tolerance of MapReduce offer a promising foundation for a combined data manipulation and data visualization system deployed in a public cloud or a local commodity cluster.

关键词： visualization Runtime

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Progressive splatting of continuous scatterplots and parallel coordinates

Progressive splatting of continuous scatterplots and paralle...

引用

13th Eurographics / ieee symposium on visualization, EuroVis 2011

作者： Heinrich, J. Bachthaler, S. Weiskopf, D. University of Stuttgart Germany

Continuous scatterplots and parallel coordinates are used to visualize multivariate data defined on a continuous domain. With the existing techniques, rendering such plots becomes prohibitively slow, especially for large scientific datasets. This paper presents a scalable and progressive rendering algorithm for continuous data plots that allows exploratory analysis of large datasets at interactive framerates. The algorithm employs splatting to produce a series of plots that are combined using alpha blending to achieve a progressively improving image. For each individual frame, splats are obtained by transforming Gaussian density kernels from the 3-D domain of the input dataset to the respective data domain. A closed-form analytic description of the resulting splat footprints is derived to allow pre-computation of splat textures for efficient GPU rendering. The plotting method is versatile because it supports arbitrary reconstruction or interpolation schemes for the input data and the splatting technique is scalable because it chooses splat samples independently from the size of the input dataset. Finally, the effectiveness of the method is compared to existing techniques regarding rendering performance and quality. © 2011 The Author(s) Journal compilation © 2011 The Eurographics Association and Blackwell Publishing Ltd. Published by Blackwell Publishing.

关键词： Image enhancement

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Exploring large data over wide area networks

Exploring large data over wide area networks

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Mark Hereld Joseph A. Insley Eric C. Olson Michael E. Papka Venkatram Vishwanath Michael L. Norman Rick Wagner Argonne National Laboratory USA University of Chicago USA San Diego Supercomputer Center USA

Simulations running on the top supercomputers are routinely producing multi-terabyte data sets. Enabling scientists, at their home institutions, to analyze, visualize and interact with these data sets as they are produced is imperative to the scientific discovery process. We report on interactive visualizations of large simulations performed on Kraken at the National Institute for Computational Sciences using the parallel cosmology code Enzo, with grid sizes ranging from 1024 3 to 6400 3 . In addition to the asynchronous rendering of over 570 timesteps of a 4096 3 simulation (150 TB in total), we developed the ability to stream the rendering result to multipanel display walls, with full interactive control of the renderer(s).

关键词： Rendering (computer graphics) Computers Laboratories data visualization Streaming media data models Computational modeling

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Constructing limited Voronoi mesh in parallel based on multi-core environment and its implementation

Constructing limited Voronoi mesh in parallel based on multi...

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2011 4th International symposium on parallel Architectures, Algorithms and Programming, PAAP 2011

作者： Dong, Lanfang Wu, Yuan Li, Meng Vision Computing and Visualization Laboratory School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9780769545752

Voronoi mesh as the basic primitive in the field of computational geometry has been widely applied on solving fluid-related problem by finite volume method. By analyzing various limited conditions for reasonable point distributions and modifying parallel data structure (doubly-connected list) for complex boundaries features in Voronoi mesh generation, considering the rapid development of multi-core processors and high performance computing, we present an efficient and feasible parallel algorithm of limited Voronoi mesh generation in standalone multi-core CPU environment. The algorithm solves the practical application of Voronoi mesh generation of large-scale points set;the experiment results show that this method improved the efficiency of the algorithm and could be widely used in various applications. © 2011 ieee.

关键词： parallel algorithms

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parallel clustering for visualizing large scientific line data

Parallel clustering for visualizing large scientific line da...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Jishang Wei Hongfeng Yu Jacqueline H. Chen Kwan-Liu Ma University of California Davis USA Sandia National Laboratories USA

Scientists often need to extract, visualize and analyze lines from vast amounts of data to understand dynamic structures and interactions. The effectiveness of such a visual validation and analysis process mainly relies on a good strategy to categorize and visualize the lines. However, the sheer size of line data produced by state-of-the-art scientific simulations poses great challenges to preparing the data for visualization. In this paper, we present a parallelization design of regression model-based clustering to categorize large line data derived from detailed scientific simulations by leveraging the power of heterogeneous computers. This parallel clustering method employs the Expectation Maximization algorithm to iteratively approximate the optimal data partitioning. First, we use a sorted-balance algorithm to partition and distribute the lines with various lengths among multiple compute nodes. During the following iterative clustering process, regression model parameters are recovered based on the local lines on each individual node, with only a few inter-node message exchanges involved. Meanwhile, the workload of regression model computing is well balanced across the nodes. The experimental results demonstrate that our approach can effectively categorize large line data in a scalable manner to concisely convey dynamic structures and interactions, leading to a visualization that captures salient features and suppresses visual clutter to facilitate scientific exploration of large line data.

关键词： Mathematical model Vectors Clustering algorithms data visualization data models graphics processing unit Computational modeling

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Detecting Non-Transient Anomalies in Visual Information Using Neural Networks

Detecting Non-Transient Anomalies in Visual Information Usin...

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16th ieee symposium on Computers and Communications (lSCC)

作者： Kounavis, Michael E. Morrissette, Joel Srinivasan, Sadagopan Yavatkar, Raj Intel Corp Hillsboro OR 97124 USA

ISBN: (纸本)9781457706783

We address the problem of detecting non-transient anomalies in visual information. By non-transient anomalies we mean changes in the way environments look that are persistent across time. Such changes may include leaving unattended bags at airport corridors, putting graffiti in building walls or damaging public property. Detecting non-transient anomalies is critical to security and surveillance in indoor and outdoor environments. We argue that existing off-the-shelf solutions to computer vision problems (e. g., image recognition, gesture recognition, text recognition) are not the most efficient when applied to detecting non-transient anomalies due to their associated computational overhead. In this paper we present a neural network-based architecture that addresses some of the limitations of the state of the art. To speed up computations, our architecture supports the processing of a large number of neurons in parallel. To reduce computational overheads, our architecture omits some of the Gaussian kernel-based feature extraction tasks performed by other systems. To classify visual anomalies as non-transient, our architecture uses a codebook-based algorithm which builds a history profile for every image segment. We describe our architecture and present some performance analysis.

关键词： Biological neural networks Computer architecture Gaussian kernel-based feature extraction tasks History Neurons Surveillance Tiles visualization codebook-based algorithm computer vision computer vision problems image segment image segmentation neural nets neural network-based architecture nontransient anomalies security of data visual information

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CERA-TVR: A framework for interactive high-quality teravoxel volume visualization on standard PCs

CERA-TVR: A framework for interactive high-quality teravoxel...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Klaus Engel Siemens AG Germany

We present a volume visualization framework that allows high-quality interactive rendering of teravoxel scale volume data sets on standard PCs. It is based on a progressive multi-resolution out-of-core volume rendering approach. data loading is controlled by rays that are cast through the volume data set. The tree structure representing the multi-resolution hierarchy is traversed and updated directly by the GPU during ray casting. Consequently, occluded and empty data is never loaded or rendered. The framework is able to render dense, anisotropic, scalar volume data on regular grids with all common rendering modes.

关键词： Rendering (computer graphics) graphics processing unit Transfer functions data visualization Loading Image reconstruction Image resolution

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ARIES Systems Code Development, visualization and Application

ARIES Systems Code Development, Visualization and Applicatio...

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24th ieee/NPSS symposium on Fusion Engineering (SOFE)

作者： Carlson, Lane Tillack, Mark Najmabadi, Farrokh Kessel, Charles Univ Calif San Diego Energy Res Ctr La Jolla CA 92093 USA Princeton Plasma Phys Lab Princeton NJ USA

ISBN: (纸本)9781457706691

The ARIES research program has utilized its comprehensive ARIES systems code (ASC) and a new graphical user interface for visualizing the parameter space as important tools in its analysis of fusion power plant designs. Recently, the ASC has undergone modifications to accommodate different divertor designs, each having unique pumping powers, helium and liquid-metal pumps thermal heat recovery, and the latest material, fabrication, and costing algorithms. The modifications and changes made to the code have been documented and verified by members of the ARIES team to ensure accuracy of implementation and self-consistency of design. The code has also been modified to display a wider range of inputs/outputs, formulas, and algorithms for a greater degree of transparency and verification. After the changes to the code were completed and the version locked, the ASC has been employed to continue to scan the physics and technology operating space for relevant power plant designs. Four corners of aggressiveness and conservativeness in both physics and technology serve as the boundaries for the scans. The Visual ARIES Systems Scanning Tool (VASST) has been used in parallel with the ASC scans to visualize the tremendous amounts of data resulting from these detailed systems scans. Displaying the data in a colorful and intuitive visual environment and giving the user explorative and visual interaction has helped extract meaningful relationships and trends from the data. Initially, broad scans from the ASC and VASST indicated areas of interest where further detail was needed. Further scans with a higher degree of detail helped enhance and further refine the database. After the final scans, VASST facilitated in displaying and filtering the large database to choose two "strawmen" data points at two of the four corners of the aggressive/conservative operating space. These points then served as reference designs so more detailed design and calculations could be done. The results of th

关键词： visualization tool ARIES VASST systems code fusion power plant tokamak

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