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parallel Contour-Buildup algorithm for the molecular surface

Parallel Contour-Buildup algorithm for the molecular surface

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1st ieee symposium on Biological Data visualization, BioVis 2011

作者： Krone, Michael Grottel, Sebastian Ertl, Thomas University of Stuttgart Germany University of Stuttgart Germany

ISBN: (纸本)9781467300025

Molecular Dynamics simulations are an essential tool for many applications. The simulation of large molecules like proteins over long trajectories is of high importance e. g. for pharmaceutical, biochemical and medical research. For analyzing these data sets interactive visualization plays a crucial role as details of the interactions of molecules are often affected by the spatial relations between these molecules. From the large range of visual representations for such data, molecule surface representations are of high importance as they clearly depict geometric interactions, such as docking or substrate channel accessibility. However, these surface visualizations are computationally demanding and thus pose a challenge for interactive visualization of time-dependent data sets. We propose an optimization of the Contour-Buildup algorithm for the Solvent Excluded Surface (SES) to remedy this issue. An optimized subdivision of calculation tasks of the original algorithm allows for full utilization of massive parallel processing hardware. Our approach is especially well suited for modern graphics hardware employing the CUDA programming language. As we do not rely on any pre-computations our method is intrinsically applicable to time-dependent data with arbitrarily long trajectories. We are able to visualize the SES for molecules with up to ten thousand atoms interactively on standard consumer graphics cards. © 2011 ieee.

关键词： Molecules

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parallel visualization on large clusters using MapReduce

Parallel visualization on large clusters using MapReduce

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1st ieee symposium on Large-Scale Data Analysis and visualization 2011, LDAV 2011

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

ISBN: (纸本)9781467301541

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 800002 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. © 2011 ieee.

关键词： MapReduce

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Scalable parallel building blocks for custom data analysis

Scalable parallel building blocks for custom data analysis

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1st ieee symposium on Large-Scale Data Analysis and visualization 2011, LDAV 2011

作者： Peterka, Tom Ross, Robert Gyulassy, Attila Pascucci, Valerio Kendall, Wesley Shen, Han-Wei Lee, Teng-Yok Chaudhuri, Abon Argonne National Laboratory United States University of Utah United States University of Tennessee Knoxville TN United States Ohio State University United States

ISBN: (纸本)9781467301541

We present a set of building blocks that provide scalable data movement capability to computational scientists and visualization researchers for writing their own parallel analysis. The set includes scalable tools for domain decomposition, process assignment, parallel I/O, global reduction, and local neighborhood communicationtasks that are common across many analysis applications. The global reduction is performed with a new algorithm, described in this paper, that efficiently merges blocks of analysis results into a smaller number of larger blocks. The merging is configurable in the number of blocks that are reduced in each round, the number of rounds, and the total number of resulting blocks. We highlight the use of our library in two analysis applications: parallel streamline generation and parallel Morse-Smale topological analysis. The first case uses an existing local neighborhood communication algorithm, whereas the latter uses the new merge algorithm. © 2011 ieee.

关键词： Data visualization

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Scalable multivariate volume visualization and analysis

Scalable multivariate volume visualization and analysis

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1st ieee symposium on Large-Scale Data Analysis and visualization 2011, LDAV 2011

作者： Guo, Hanqi Xiao, He Lu, Min Yuan, Xiaoru School of EECS Peking University Beijing China Center for Computational Science and Engineering Peking University Beijing China

ISBN: (纸本)9781467301541

In this work, we present an effective and scalable system for multivariate volume data visualization and analysis with a novel Transfer Function (TF) interface design that tightly couples parallel coordinates plots (PCP) and MDS-based dimension projection plots. In our system, the PCP visualizes the data distribution of each variate and the MDS plots project features. Together, they are integrated seamlessly to provide flexible feature classification without context switching between different data presentations during the user interaction. The proposed interface enables users to identify relevant correlation clusters and assign optical properties on them. To further support large scale multivariate volume data visualization and analysis, we develop three integrated parallel systems to accelerate the rendering of PCP, the layout of MDS, as well as parallel rendering of multivarite volume data. Our experiments show that the system is effective in multivariate volume data visualization and its performance is scalable for data sets with different sizes and number of variates. © 2011 ieee.

关键词： Transfer functions

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STREAMIT: Dynamic visualization and Interactive Exploration of Text Streams

STREAMIT: Dynamic Visualization and Interactive Exploration ...

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4th ieee Pacific visualization symposium

作者： Alsakran, Jamal Chen, Yang Zhao, Ye Yang, Jing Luo, Dongning Kent State Univ Kent OH 98032 USA Univ N Carolina Chapel Hill NC 27515 USA

ISBN: (纸本)9781612849324

Text streams demand an effective, interactive, and on-the-fly method to explore the dynamic and massive data sets, and meanwhile extract valuable information for visual analysis. In this paper, we propose such an interactive visualization system that enables users to explore streaming-in text documents without prior knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is "physically" achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword vectors to incorporate newly-introduced keywords from data streams. Furthermore, we propose a special keyword importance that makes it possible for users to adjust the similarity on-the-fly, and hence achieve their preferred visual effects in accordance to varying interests, which also helps to identify hot spots and outliers. We optimize the system performance through a similarity grid and with parallel implementation on graphics hardware (GPU), which achieves instantaneous animated visualization even for a very large data collection. Moreover, our system implements a powerful user interface enabling various user interactions for in-depth data analysis. Experiments and case studies are presented to illustrate our dynamic system for text stream exploration.

关键词： visualization system streams texts Dataset visualization Interactive System performance rivers user-computer interaction Consumers Visual analytics Dynamic systems Exploration graphic images User Interface Device Component

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1st ieee symposium on Large-Scale Data Analysis and visualization 2011, LDAV 2011 - Proceedings

1st IEEE Symposium on Large-Scale Data Analysis and Visualiz...

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1st ieee symposium on Large-Scale Data Analysis and visualization 2011, LDAV 2011

ISBN: (纸本)9781467301541

The proceedings contain 28 papers. The topics discussed include: visualizing multiscale, multiphysics simulation data: brain blood flow;toward simulation-time data analysis and I/O acceleration on leadership-class systems;atypical behavior identification in large-scale network traffic;revisiting wavelet compression for large-scale climate data using JPEG 2000 and ensuring data precision;histogram spectra for multivariate time-varying volume LOD selection;parallel clustering for visualizing large scientific line data;evaluating the benefits of an extended memory hierarchy for parallel streamline algorithms;parallel in situ indexing for data-intensive computing;incremental, approximate database queries and uncertainty for exploratory visualization;the ParaView coprocessing library: a scalable, general purpose in situ visualization library;and dax toolkit: a proposed framework for data analysis and visualization at extreme scale.

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ieee symposium on Biological Data visualization 2011, BioVis 2011 - Proceedings

IEEE Symposium on Biological Data Visualization 2011, BioVis...

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1st ieee symposium on Biological Data visualization, BioVis 2011

ISBN: (纸本)9781467300025

The proceedings contain 20 papers. The topics discussed include: Quick2Insight: a user-friendly framework for interactive rendering of biological image volumes;modeling and visualization of receptor clustering on the cellular membrane;parallel contour-buildup algorithm for the molecular surface;fiber stippling: an illustrative rendering for probabilistic diffusion tractography;visualization of anisotropic contact potentials within protein structures;the galaxy track browser: transforming the genome browser from visualization tool to analysis tool;visual analysis of next-generation sequencing data to detect overlapping genes in bacterial genomes;iHAT: interactive hierarchical aggregation table;metrics for comparing explicit representations of interconnected biological networks;RuleBender: integrated visualization for biochemical rule-based modeling;and MDMap: a system for data-driven layout and exploration of molecular dynamics simulations.

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GPGPU acceleration algorithm for medical image reconstruction

GPGPU acceleration algorithm for medical image reconstructio...

引用

9th ieee International symposium on parallel and Distributed Processing with Applications, ISPA 2011

作者： Archirapatkave, Virasin Sumilo, Hendra See, Simon Chong Wee Achalakul, Tiranee Department of Computer Engineering King Mongkut's University of Technology Thonburi Thailand Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore Nvidia Corporation Singapore Singapore

ISBN: (纸本)9780769544281

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. © 2011 ieee.

关键词： graphics processing unit

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ieee Pacific visualization symposium 2011, PacificVis 2011 - Proceedings

IEEE Pacific Visualization Symposium 2011, PacificVis 2011 -...

引用

4th ieee Pacific visualization symposium 2011, PacificVis 2011

ISBN: (纸本)9781612849324

The proceedings contain 26 papers. The topics discussed include: full-resolution interactive CPU volume rendering with coherent BVH traversal;context-aware volume navigation;multi-dimensional transfer function design based on flexible dimension projection embedded in parallel coordinates;static correlation visualization for large time-varying volume data;the neuron navigator: exploring the information pathway through the neural maze;CareCruiser: exploring and visualizing plans, events, and effects interactively;loose capacity-constrained representatives for the qualitative visual analysis in molecular dynamics;interactive seismic interpretation with piecewise global energy minimization;uncertain topology of 3D vector fields;edge maps: representing flow with bounded error;view point evaluation and streamline filtering for flow visualization;and dual space analysis of turbulent combustion particle data.

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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...

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1st ieee symposium on Large-Scale Data Analysis and visualization 2011, LDAV 2011

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

ISBN: (纸本)9781467301541

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. © 2011 ieee.

关键词： Data visualization

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