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A flexible Java GPU-enhanced visualization framework and its applications

A flexible Java GPU-enhanced visualization framework and its...

引用

Annual ieee symposium on Computer-Based Medical Systems

作者： Ruida Cheng Justin Senseney Nishith Pandya Evan McCreedy Matthew J. McAuliffe Alexandra Bokinsky Imaging Sciences Laboratory Center for Information Technology National Institutes of Health DHHS USA Geometric Tools Inc. Chapel Hill NC USA

A flexible biomedical visualization framework implemented with Java, OpenGL, and OpenCL performs efficient volume rendering with large, multi-modal datasets. The framework takes advantage of the parallel processing power on modern graphics hardware with novel OpenCL and GLSL shading language implementations. The Java and GPU environment provide portable advanced biomedical image visualization applications. Several applications built on top of the GPU framework are also presented to show the extensibility of the application. These include multi-surface rendering, stereoscopic rendering, image fusion, and diffusion tensor visualization.

关键词： Rendering (computer graphics) graphics processing unit Surface treatment Image color analysis Java visualization Pipelines

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Meta parallel coordinates for visualizing features in large, high-dimensional, time-varying data

Meta parallel coordinates for visualizing features in large,...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Aritra Dasgupta Robert Kosara Luke Gosink UNC-Charlotte USA Pacific Northwest National Laboratory USA

Managing computational complexity and designing effective visual representations are two important challenges for the visualization of large, complex, high-dimensional datasets. parallel coordinates are an effective technique for visualizing high-dimensional data, but do not scale well to very large datasets. The addition of the temporal dimension leads to more uncertainty due to clutter on screen. Consequently, this poses a significant challenge for visually finding trends and patterns that maximize insight about the underlying time-varying properties of the data. To address these problems, we present meta parallel coordinates, a parallel coordinates display that is guided by perceptually motivated visual metrics. These metrics describe the visual structures typically found in parallel coordinates and thus aid the user's analysis by providing meaningful views of the data. Since they are computed in screen space, our metrics are computationally more efficient than data-based metrics. Our choice of metrics is driven by the different analytical tasks that a user typically wants to perform with time-varying multivariate data. In particular, we have worked with domain scientists who performed simulations of bioremediation experiments, and use their data and results to demonstrate the usefulness of our approach.

关键词： Measurement Entropy data visualization visualization Kinetic theory Iron Image color analysis

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Progressive parallel coordinates

Progressive parallel coordinates

引用

Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： René Rosenbaum Jian Zhi Bernd Hamann Visual Computing and Computer Graphics group (VCG) Institute for Computer Science University of Rostock Rostock Germany Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California Davis CA USA Department of Industrial Engineering and Operations Research Columbia University NY USA

Progressive refinement is a methodology that makes it possible to elegantly integrate scalable data compression, access, and presentation into one approach. Specifically, this paper concerns the effective use of progressive parallel coordinates (PPCs), utilized routinely for high-dimensional data visualization. It discusses how the power of the typical stages of progressive data visualization can also be utilized fully for PPCs. Further, different implementations of the underlying methods and potential application domains are described. The paper also presents empirical results concerning the benefits of PPC with regard to efficient data management and improved presentation, indicating that the proposed approach is able to close the gap between data handling and visualization.

关键词： Lead visualization Logic gates

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Gaussian mixture model based volume visualization

Gaussian mixture model based volume visualization

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Shusen Liu Joshua A. Levine Peer-Timo Bremer Valerio Pascucci

Representing uncertainty when creating visualizations is becoming more indispensable to understand and analyze scientific data. Uncertainty may come from different sources, such as, ensembles of experiments or unavoidable information loss when performing data reduction. One natural model to represent uncertainty is to assume that each position in space instead of a single value may take on a distribution of values. In this paper we present a new volume rendering method using per voxel Gaussian mixture models (GMMs) as the input data representation. GMMs are an elegant and compact way to drastically reduce the amount of data stored while still enabling realtime data access and rendering on the GPU. Our renderer offers efficient sampling of the data distribution, generating renderings of the data that flicker at each frame to indicate high variance. We can accumulate samples as well to generate still frames of the data, which preserve additional details in the data as compared to either traditional scalar indicators (such as a mean or a single nearest neighbor down sample) or to fitting the data with only a single Gaussian per voxel. We demonstrate the effectiveness of our method using ensembles of climate simulations and MRI scans as well as the down sampling of large scalar fields as examples.

关键词： Uncertainty Rendering (computer graphics) data visualization data models Computational modeling Transfer functions graphics processing units

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parallel stream surface computation for large data sets

Parallel stream surface computation for large data sets

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： David Camp Hank Childs Christoph Garth David Pugmire Kenneth I. Joy Lawrence Berkeley National Laboratory USA University of California Davis USA University of Kaiserslautern Germany Oak Ridge National Laboratory USA

parallel stream surface calculation, while highly related to other particle advection-based techniques such as streamlines, has its own unique characteristics that merit independent study. Specifically, stream surfaces require new integral curves to be added continuously during execution to ensure surface quality and accuracy; performance can be improved by specifically accounting for these additional particles. We present an algorithm for generating stream surfaces in a distributed-memory parallel setting. The algorithm incorporates multiple schemes for parallelizing particle advection and we study which schemes work best. Further, we explore speculative calculation and how it can improve overall performance. In total, this study informs the efficient calculation of stream surfaces in parallel for large data sets, based on existing integral curve functionality.

关键词： Vectors Surface treatment Approximation methods Approximation algorithms Streaming media data visualization Piecewise linear approximation

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Virtual rheoscopic fluids for dense, large-scale fluid flow visualizations

Virtual rheoscopic fluids for dense, large-scale fluid flow ...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Paul A. Navrátil William L. Barth Hank Childs Texas Advanced Computing Center USA Lawrence Berkeley National Laboratory USA

Fluid flow visualizations typically employ a combination of isosur-facing and particle tracing, such as streamlines. These techniques capture surface flow well, but they can obscure deeper features from view. Further, these techniques present visualizations that are far removed from physical methods, which complicates their analysis. However, virtual rheoscopic fluids (VRF) provide a physically-based visualization that is similar to, and can be similarly analyzed as, physical rheoscopic fluid. However, we need a scalable VRF algorithm to apply the technique to large-scale data. In this paper, we present a scalable VRF algorithm based on ray casting implemented in a general visualization toolkit. We demonstrate the application of these techniques to large-scale flow problems beyond what would be physically feasible in the laboratory, and we show that our approach scales through 256 cores.

关键词： Rendering (computer graphics) data visualization Pipelines Streaming media Tensile stress Fluids visualization

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Efficient parallel extraction of crack-free isosurfaces from adaptive mesh refinement (AMR) data

Efficient parallel extraction of crack-free isosurfaces from...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Gunther H. Weber Hank Childs Jeremy S. Meredith Lawrence Berkeley National Laboratory University of California Davis USA Lawrence Berkeley National Laboratory University of California슠Davis USA Oak Ridge National Laboratory USA

We present a novel extraction scheme for crack-free isosurfaces from adaptive mesh refinement (AMR) data that builds on prior work utilizing dual grids and filling resulting gaps with stitch cells. We use a case-table-based approach to simplify the implementation of stitch cell generation. The most significant benefit of our new approach is that it uses ghost data to handle parallel isosurface extraction efficiently. We further present the results of applying this method to large scale data sets and analyze its computation time on parallel high-performance computing (HPC) platforms.

关键词： Isosurfaces data models data mining Indexes Algorithm design and analysis Computational modeling

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Towards real-time visualization of detailed neural tissue models: View frustum culling for parallel rendering

Towards real-time visualization of detailed neural tissue mo...

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ieee symposium on Biological data visualization (BioVis)

作者： Juan B. Hernando Felix Schürmann Luis Pastor CeSViMa Universidad Politécnica de Madrid Spain Blue Brain Project École Polytechnique Fédérale de Lausanne Switzerland GMRV Universidad Rey Juan Carlos de Madrid Spain

Recent efforts to model neural tissue in great anatomical detail play an important role in the shift towards simulation-based brain research. visualization of those detailed models is an important part of the scientific workflow, allowing domain scientists to inspect and analyze the models as a virtual tissue. However, these visualizations pose several computational and interface challenges, from issues such as data size and geometrical and visual complexity. Manual or query driven selection of visual attributes such as color, transparency, transfer functions, visibility are important components for the usability of those visualizations, yet this requires the rendering to be as responsive as possible. Given the data complexity (several million triangles) - despite the power of current GPUs - parallel rendering is the only choice to visualize the full circuits at interactive levels. In particular, sort-first is an appropriate choice to deal with transparency, however it requires efficient view frustum culling. This paper presents two novel techniques for view frustum culling based on a bounding volume skeleton which is created from the geometrical skeleton of the neurons. These skeletons are combined with spatial partitions and some GPU processing to extract the portions of the neurons that need to be rendered while still facilitating interactive changes in the attributes. Experimental results show that the proposed method yields better results than classical approaches such as OBB-trees and looks promising for sort-first environments.

关键词： Skeleton Neurons Rendering (computer graphics) Computational modeling Octrees Arrays graphics processing units

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Object-space ambient occlusion for molecular dynamics

Object-space ambient occlusion for molecular dynamics

引用

Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Sebastian Grottel Michael Krone Katrin Scharnowski Thomas Ertl Visualization Research Center University of Stuttgart Germany

In many different application fields particle-based simulation, like molecular dynamics, are used to study material properties and behavior. Nowadays, simulation data sets consist of millions of particles and thousands of time steps challenging interactive visualization. Direct glyph-based representations of the particle data are important for the visual analysis process and these rendering methods can be optimized to be able to work sufficiently fast with huge data sets. However, the perception of the implicit spatial structures formed by such data is often hindered by aliasing and visual clutter. Especially the depth of these structures can be grasped better if visual cues are applied, even in interactive representations. We hence present a method to apply object-space ambient occlusion, based on local neighborhood information, to large timedependent particle-based data sets without the need for any precomputations. Based on density information collected in real-time, glyph-based representations of the data sets can be visually enhanced without significant impact on the rendering performance allowing to visualize multi-million particle data sets interactively on commodity workstations.

关键词： Rendering (computer graphics) Geometry graphics processing unit visualization data visualization Equations Mathematical model

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Query-driven parallel exploration of large datasets

Query-driven parallel exploration of large datasets

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Atanas Atanasov Madhusudhanan Srinivasan Tobias Weinzierl Technische Universität München Germany King Abdullah University for Science and Technology Saudi Arabia

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.

关键词： data visualization Supercomputers Computational modeling data models Distributed databases Load modeling Topology

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