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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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Loose capacity-constrained representatives for the qualitative visual analysis in molecular dynamics

Loose capacity-constrained representatives for the qualitati...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Steffen Frey Thomas Schlömer Sebastian Grottel Carsten Dachsbacher Oliver Deussen Thomas Ertl Visualization Research Center Universitat Stuttgart (VISUS) Germany University of Konstanz Germany Visualization Research Center Universitat Stuttgart (VISUS) Universität Stuttgart Germany University of Konstanz Karlsruhe Institute of Technology Germany

Molecular dynamics is a widely used simulation technique to investigate material properties and structural changes under external forces. The availability of more powerful clusters and algorithms continues to increase the spatial and temporal extents of the simulation domain. This poses a particular challenge for the visualization of the underlying processes which might consist of millions of particles and thousands of time steps. Some application domains have developed special visual metaphors to only represent the relevant information of such data sets but these approaches typically require detailed domain knowledge that might not always be available or applicable. We propose a general technique that replaces the huge amount of simulated particles by a smaller set of representatives that are used for the visualization instead. The representatives capture the characteristics of the underlying particle density and exhibit coherency over time. We introduce loose capacity-constrained Voronoi diagrams for the generation of these representatives by means of a GPU-friendly, parallel algorithm. This way we achieve visualizations that reflect the particle distribution and geometric structure of the original data very faithfully. We evaluate our approach using real-world data sets from the application domains of material science, thermodynamics and dynamical systems theory.

关键词： graphics processing unit Data visualization Measurement Density functional theory visualization Clustering algorithms parallel algorithms

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parallel clustering for visualizing large scientific line data

Parallel clustering for visualizing large scientific line da...

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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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Multi-dimensional transfer function design based on flexible dimension projection embedded in parallel coordinates

Multi-dimensional transfer function design based on flexible...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Hanqi Guo He Xiao Xiaoru Yuan Center of Computational Science and Engineering Peking University Beijing China Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Beijing China Peking University Beijing Beijing CN

In this paper, we present an effective transfer function (TF) design for multivariate volume, providing tightly coupled views of parallel coordinates plot (PCP), MDS-based dimension projection plots, and volume rendered image space. In our design, the PCP showing the data distribution of each variate dimension and the MDS showing reduced dimensional features are integrated seamlessly to provide flexible feature classification for the user without context switching between different data presentations. Our proposed interface enables users to identify interested clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, sketching directly on the volume rendered images has been implemented to probe and edit features. To achieve interactivity, octree partitioning with Gaussian Mixture Model (GMM), and other data reduction techniques are applied. Our experiments show that the proposed method is effective for multidimensional TF design and data exploration.

关键词： Rendering (computer graphics) Data visualization Octrees Algorithm design and analysis Transfer functions Strips Context

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GPGPU Acceleration Algorithm for Medical Image Reconstruction

GPGPU Acceleration Algorithm for Medical Image Reconstructio...

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International symposium on parallel and Distributed Processing with Applications, ISPA

作者： Virasin Archirapatkave Hendra Sumilo Simon Chong Wee See Tiranee Achalakul Dept. of Comput. Eng. King Mongkut's Univ. of Technol. Singapore Singapore Department of Mechanical & AeroSpace Engineering Nanyang Technological University Singapore Nvidia Corporation Singapore

Medical imaging techniques such as X-ray, Ultrasound, CT and MRI scan are widely used for diagnosis. The 2D medical images from these scans are difficult to interpret because they can only show cross section views of a human body. Interpreting these images requires experts or trained professionals. Reconstructing 2D images into 3D models can help with the interpretation process. However, such model reconstruction is normally time-consuming and costly. It requires high performance computation, such as grid or parallel computing. This research, thus, proposes a high performance 3D reconstruction method using the General-Purpose computation on graphics Processing Units (GPGPU). The GPGPU has a high computational performance. parallel computing method on GPU can thus regenerate a model for real time 3D visualization. In other words, the GPU computational speed sufficiently improves the visualization effectiveness of both images and models to the point where a real-time navigation of the data set is possible. In our work, the 3D reconstruction process reconstructs a set of 2D cross-section images and stacks them to generate a volume data, and then transform them into a 3D model. The generated models are then displayed on the user interface developed with OpenGL. Finally, the performance of the GPU acceleration is presented in this paper.

关键词： Three dimensional displays graphics processing unit Image reconstruction Solid modeling Pixel Image edge detection Surface reconstruction

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Accelerated polyhedral visual hulls using OpenCL

Accelerated polyhedral visual hulls using OpenCL

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ieee Annual International symposium Virtual Reality

作者： Tobias Duckworth David J. Roberts University of Sanford Salford UK

We present a method for reconstruction of the visual hull (VH) of an object in real-time from multiple video streams. A state of the art polyhedral reconstruction algorithm is accelerated by implementing it for parallel execution on a multi-core graphics processor (GPU). The time taken to reconstruct the VH is measured for both the accelerated and non-accelerated implementations of the algorithm, over a range of image resolutions and number of cameras. The results presented are of relevance to researchers in the field of 3D reconstruction at interactive frame rates (real-time), for applications such as telepresence.

关键词： Cameras Three dimensional displays Image edge detection graphics processing unit Solid modeling visualization Image resolution

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Optimized GPU histograms for multi-modal registration

Optimized GPU histograms for multi-modal registration

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ieee International symposium on Biomedical Imaging

作者： Christoph Vetter Rüdiger Westermann Siemens AG Corporate Research and Development Princeton USA Computer Graphics and Visualization Technische Universität München Germany

ISBN: (纸本)9781424441273

GPU-based systems are used more and more for medical image processing because of their parallel processing power and memory bandwidth. Impressive results have been achieved when registering large volume, however, one of the most-used similarity measures for multi-modal registration - mutual information - is not well suited for the streaming architecture because of its memory access pattern. We present two optimization approaches that improve the performance by a factor of four compared to state-of-the-art GPU algorithms in the latest research papers.

关键词： Biomedical imaging Histograms Streaming media

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A Study of parallel Particle Tracing for Steady-State and Time-Varying Flow Fields

A Study of Parallel Particle Tracing for Steady-State and Ti...

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Tom Peterka Robert Ross Boonthanome Nouanesengsy Teng-Yok Lee Han-Wei Shen Wesley Kendall Jian Huang Argonne National Laboratory Argonne IL USA Ohio State Uinversity Columbus OH USA University of Tennessee Knoxville Knoxville TN USA

Particle tracing for streamline and path line generation is a common method of visualizing vector fields in scientific data, but it is difficult to parallelize efficiently because of demanding and widely varying computational and communication loads. In this paper we scale parallel particle tracing for visualizing steady and unsteady flow fields well beyond previously published results. We configure the 4D domain decomposition into spatial and temporal blocks that combine in-core and out-of-core execution in a flexible way that favors faster run time or smaller memory. We also compare static and dynamic partitioning approaches. Strong and weak scaling curves are presented for tests conducted on an IBM Blue Gene/P machine at up to 32 K processes using a parallel flow visualization library that we are developing. Datasets are derived from computational fluid dynamics simulations of thermal hydraulics, liquid mixing, and combustion.

关键词： Partitioning algorithms Heuristic algorithms Data structures Libraries Load management Steady-state Rendering (computer graphics)

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Hierarchical Aggregation for Information visualization: Overview, Techniques, and Design Guidelines

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2010年第3期16卷 439-454页

作者： Elmqvist, Niklas Fekete, Jean-Daniel Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Univ Paris 11 LRI INRIA Saclay F-91405 Orsay France

We present a model for building, visualizing, and interacting with multiscale representations of information visualization techniques using hierarchical aggregation. The motivation for this work is to make visual representations more visually scalable and less cluttered. The model allows for augmenting existing techniques with multiscale functionality, as well as for designing new visualization and interaction techniques that conform to this new class of visual representations. We give some examples of how to use the model for standard information visualization techniques such as scatterplots, parallel coordinates, and node-link diagrams, and discuss existing techniques that are based on hierarchical aggregation. This yields a set of design guidelines for aggregated visualizations. We also present a basic vocabulary of interaction techniques suitable for navigating these multiscale visualizations.

关键词： Aggregation clustering clutter reduction massive data sets visual exploration visual analytics

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A Layer-Oriented Interface for Visualizing Time-Series Data from Oscilloscopes

A Layer-Oriented Interface for Visualizing Time-Series Data ...

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ieee Pacific visualization symposium

作者： Lopez-Hernandez, Roberto Guilmaine, David McGuffin, Michael J. Barford, Lee École de Technologie Supérieure Canada Agilent Laboratories

ISBN: (纸本)9781424466849

We present a prototype interface for visualizing and interacting with univariate time-dependent digital signals, i.e., the kind of signals that might be measured using an oscilloscope and then analyzed to find anomalies in edge timing, overshoot, or other measured characteristics. Our prototype visualizes the data using a standard eye diagram that wraps the signal in time, as well as a parallel coordinates view, and a novel 2.5D layer-based technique based on a drawer metaphor for spreading the eye diagram apart and de-occluding the traces within it. Once a "drawer" is opened, the traces within it can be sorted or grouped according to various attributes, and drilling-down into data can be achieved by opening sub-drawers. We also present a novel technique for browsing eye diagrams called temporal brushing. Initial user feedback from students in electrical engineering is reported. A video demonstrating our prototype can be downloaded at http://***/mmcguffin/

关键词： H.5.2 [information interfaces and presentation]: User interfaces - interaction styles I.3.6 [computer graphics]: Methodology and techniques - interaction techniques

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