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检索条件"任意字段=Proceedings of the 1st Symposium on Applied perception in graphics and visualization"

共 101 条记录，以下是21-30 订阅

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Distributed terascale volume visualization using distributed shared virtual memory

Distributed terascale volume visualization using distributed...

引用

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

作者： Beyer, Johanna Hadwiger, Markus Schneider, Jens Jeong, Won-Ki Pfister, Hanspeter KAUST Saudi Arabia Harvard University United States

ISBN: (纸本)9781467301541

Table 1 illustrates the impact of different distribution unit sizes, different screen resolutions, and numbers of GPU nodes. We use two and four GPUs (NVIDIA Quadro 5000 with 2.5 GB memory) and a mouse cortex EM dataset (see Figure 2) of resolution 21,494 x 25,790 x 1,850 = 955GB. The size of the virtual distribution units significantly influences the data distribution between nodes. Small distribution units result in a high depth complexity for compositing. Large distribution units lead to a low utilization of GPUs, because in the worst case only a single distribution unit will be in view, which is rendered by only a single node. The choice of an optimal distribution unit size depends on three major factors: the output screen resolution, the block cache size on each node, and the number of nodes. Currently, we are working on optimizing the compositing step and network communication between nodes. © 2011 IEEE.

关键词： Program processors

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Visualizing multiscale, multiphysics simulation data: Brain blood flow

Visualizing multiscale, multiphysics simulation data: Brain ...

引用

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

作者： Insley, Joseph A. Grinberg, Leopold Papka, Michael E. Argonne National Laboratory United States Brown University United States

ISBN: (纸本)9781467301541

Accurately predicting many physical and biological systems requires modeling interactions of macroscopic and microscopic events. This results in large and heterogeneous data sets on vastly differing scales, both physical and temporal. The ability to use a single integrated tool for the visualization of multiscale simulation data is important to understanding the effects that events at one scale have on events in the other. In the case of blood flow, we examine how the large-scale flow patterns influence blood cell behavior. In this paper we describe the visualization tools that were developed for data from coupled continuum - atomistic simulations. Specifically, we overview a) a custom ParaView reader plug-in that processes macro-scale continuum data computed by a high-order spectral element solver;and b) an adaptive proper orthogonal decomposition-based technique for the visualization of nonstationary velocity fields from atomistic simulations. We also discuss how the ParaView parallel processing and rendering infrastructure was leveraged in the new tools. We apply our methods to visualize multiscale data from coupled continuum-atomistic simulations of blood flow in a patient-specific cerebrovasculature with a brain aneurysm. © 2011 IEEE.

关键词： visualization

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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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Atypical behavior identification in large-scale network traffic

Atypical behavior identification in large-scale network traf...

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

作者： Best, Daniel M. Hafen, Ryan P. Olsen, Bryan K. Pike, William A. Pacific Northwest National Laboratory United States

ISBN: (纸本)9781467301541

Cyber analysts are faced with the daunting challenge of identifying exploits and threats within potentially billions of daily records of network traffic. Enterprise-wide cyber traffic involves hundreds of millions of distinct IP addresses and results in data sets ranging from terabytes to petabytes of raw data. Creating behavioral models and identifying trends based on those models requires data intensive architectures and techniques that can scale as data volume increases. Analysts need scalable visualization methods that foster interactive exploration of data and enable identification of behavioral anomalies. Developers must carefully consider application design, storage, processing, and display to provide usability and interactivity with large-scale data. We present an application that highlights atypical behavior in enterprise network flow records. This is accomplished by utilizing data intensive architectures to store the data, aggregation techniques to optimize data access, statistical techniques to characterize behavior, and a visual analytic environment to render the behavioral trends, highlight atypical activity, and allow for exploration. © 2011 IEEE.

关键词： Data visualization

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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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HiTSEE: A visualization tool for hit selection and analysis in high-throughput screening experiments

HiTSEE: A visualization tool for hit selection and analysis ...

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1st IEEE symposium on Biological Data visualization, BioVis 2011

作者： Bertini, Enrico strobelt, Hendrik Braun, Joachim Deussen, Oliver Groth, Ulrich Mayer, Thomas U. Merhof, Dorit University of Konstanz Germany

ISBN: (纸本)9781467300025

We present HiTSEE (High-Throughput Screening Exploration Environment) a visualization tool for the analysis of large chemical screens for the analysis of biochemical processes. The tool supports the analysis of structure-activity relationships (SAR analysis) and, through a flexible interaction mechanism, the navigation of large chemical spaces. Our approach based on the projection of one or few molecules of interest and the expansion around their neighborhood allows for the exploration of large chemical libraries without the need to create an all encompassing overview of the whole library. We describe the requirements we collected during our collaboration with biologists and chemists, the design rationale behind the tool, and two case studies. © 2011 IEEE.

关键词： Throughput

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EVEVis: A multi-scale visualization system for dense evolutionary data

EVEVis: A multi-scale visualization system for dense evoluti...

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1st IEEE symposium on Biological Data visualization, BioVis 2011

作者： Miller, Robert Mozhayskiy, Vadim Tagkopoulos, Llias Ma, Kwan-Liu University of California Davis United States

ISBN: (纸本)9781467300025

Evolutionary simulations can produce datasets consisting of thousands or millions of separate entities, complete with their genealogical relationships. Biologists must examine this data to determine when and where these entities have changed, both on an individual basis and on a population-wide basis. Therefore, desirable features of a visualization system for evolutionary data are the capability of showing the status of the population at any given moment in time, good scalability, and smooth transition between high-level and low-level views. We propose a multi-scale visualization method, including a novel tree layout that both shows population status over time and can easily scale to very large populations. From this layout, the user can navigate to visualizations for moments in time or for individual entities. We demonstrate the effectiveness of the visualization on an existing evolutionary simulation called EVE: Evolution in Variable Environments. © 2011 IEEE.

关键词： visualization

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Parallel clustering for visualizing large scientific line data

Parallel clustering for visualizing large scientific line da...

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

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

ISBN: (纸本)9781467301541

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. © 2011 IEEE.

关键词： visualization

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Activity detection for scientific visualization

Activity detection for scientific visualization

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

作者： Ozer, Sedat Silver, Deborah Bemis, Karen Martin, Pino Takle, Jay Department of Electrical and Computer Engineering Rutgers University NJ 08854 United States Department of Earth and Planetary Sciences Rutgers University NJ 08854 United States Department of Aerospace Engineering University Maryland MD 20742 United States

ISBN: (纸本)9781467301541

Understanding the science behind ultra-scale simulations requires extracting meaning from data sets of hundreds of terabytes or more. At extreme scales, the data sets are so huge, there is not even enough time to view the data, let alone explore it with basic visualization methods. Automated tools are necessary for knowledge discovery to help sift through the information and isolate characteristic patterns, thereby enabling the scientist to study local interactions, the origin of features, and their evolution, i.e. activity detection in large volumes of 3D data. Defining and modelling such activities in 3D scientific data sets remains an open research problem, though it has been widely studied in the computer vision community. In this work we demonstrate how utilizing activity detection can help us model and detect complex events (activities) in large 3D scientific data sets. We employ Petri nets which support distributed and discrete graphical modelling of spatio-temporal patterns to model activities in time-varying 3D scientific data sets. We demonstrate the use of Petri nets on three different data sets. © 2011 IEEE.

关键词： Petri nets

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Visualizing virus population variability from next generation sequencing data

Visualizing virus population variability from next generatio...

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1st IEEE symposium on Biological Data visualization, BioVis 2011

作者： Correll, Michael Ghosh, Subhadip O'Connor, David Gleicher, Michael University of Wisconsin Madison United States Department of Computer Sciences United States Department of Pathology and Laboratory Medicine United States

ISBN: (纸本)9781467300025

Advances in genomic sequencing techniques allow for larger scale generation and usage of sequence data. While these techniques afford new types of analysis, they also generate new concerns with regards to data quality and data scale. We present a tool designed to assist in the exploration of the genetic variability of the population of viruses at multiple time points and in multiple individuals, a task that necessitates considering large amounts of sequence data and the quality issues inherent in obtaining such data in a practical manner. Our design affords the examination of the amount of variability and mutation at each position in the genome for many populations of viruses. Our design contains novel visualization techniques that support this specific class of analysis while addressing the issues of data aggregation, confidence visualization, and interaction support that arise when making use of large amounts of sequence data with variable uncertainty. These techniques generalize to a wide class of visualization problems where confidence is not known a priori, and aggregation in multiple directions is necessary. © 2011 IEEE.

关键词： visualization

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