检索结果-内蒙古大学图书馆

Toward a General I/O Layer for parallel-visualization Applications

ieee COMPUTER graphics AND APPLICATIONS 2011年第6期31卷 6-10页

作者： Kendall, Wesley Huang, Jian Peterka, Tom Latham, Robert Ross, Robert Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Univ Tennessee Seelab Knoxville TN USA Argonne Natl Lab Argonne IL 60439 USA

For large-scale visualization applications, the visualization community urgently needs general solutions for efficient parallel I/O. These parallel visualization solutions should center around design patterns and the related data-partitioning strategies, not file formats. From this respect, it's feasible to greatly alleviate I/O burdens without reinventing the wheel. For example, BIL (Block I/O Layer), which implements such a pattern, has greatly accelerated I/O performance for large-scale parallel particle tracing, a pervasive but challenging use case. [ABSTRACT FROM PUBLISHER]

关键词： BIL computer graphics parallel file systems parallel I/O parallel visualization particle tracing

来源：评论

学校读者我要写书评

暂无评论

Sequence Surveyor: Leveraging Overview for Scalable Genomic Alignment visualization

引用

ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2011年第12期17卷 2392-2401页

作者： Albers, Danielle Dewey, Colin Gleicher, Michael Univ Wisconsin Madison WI 53706 USA

In this paper, we introduce overview visualization tools for large-scale multiple genome alignment data. Genome alignment visualization and, more generally, sequence alignment visualization are an important tool for understanding genomic sequence data. As sequencing techniques improve and more data become available, greater demand is being placed on visualization tools to scale to the size of these new datasets. When viewing such large data, we necessarily cannot convey details, rather we specifically design overview tools to help elucidate large-scale patterns. Perceptual science, signal processing theory, and generality provide a framework for the design of such visualizations that can scale well beyond current approaches. We present Sequence Surveyor, a prototype that embodies these ideas for scalable multiple whole-genome alignment overview visualization. Sequence Surveyor visualizes sequences in parallel, displaying data using variable color, position, and aggregation encodings. We demonstrate how perceptual science can inform the design of visualization techniques that remain visually manageable at scale and how signal processing concepts can inform aggregation schemes that highlight global trends, outliers, and overall data distributions as the problem scales. These techniques allow us to visualize alignments with over 100 whole bacterial-sized genomes.

关键词： Bioinformatics visualization Perception Theory Scalability Issues Visual Design

来源：评论

学校读者我要写书评

暂无评论

Load-Balanced parallel Streamline Generation on large Scale Vector Fields

引用

ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2011年第12期17卷 1785-1794页

作者： Nouanesengsy, Boonthanome Lee, Teng-Yok Shen, Han-Wei Ohio State Univ Columbus OH 43210 USA

Because of the ever increasing size of output data from scientific simulations, supercomputers are increasingly relied upon to generate visualizations. One use of supercomputers is to generate field lines from large scale flow fields. When generating field lines in parallel, the vector field is generally decomposed into blocks, which are then assigned to processors. Since various regions of the vector field can have different flow complexity, processors will require varying amounts of computation time to trace their particles, causing load imbalance, and thus limiting the performance speedup. To achieve load-balanced streamline generation, we propose a workload-aware partitioning algorithm to decompose the vector field into partitions with near equal workloads. Since actual workloads are unknown beforehand, we propose a workload estimation algorithm to predict the workload in the local vector field. A graph-based representation of the vector field is employed to generate these estimates. Once the workloads have been estimated, our partitioning algorithm is hierarchically applied to distribute the workload to all partitions. We examine the performance of our workload estimation and workload-aware partitioning algorithm in several timings studies, which demonstrates that by employing these methods, better scalability can be achieved with little overhead.

关键词： Flow visualization parallel processing 3D vector field visualization Streamlines

来源：评论

学校读者我要写书评

暂无评论

parallel visualization on large clusters using MapReduce

Parallel visualization on large clusters using MapReduce

引用

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

来源：评论

学校读者我要写书评

暂无评论

Scalable multivariate volume visualization and analysis

Scalable multivariate volume visualization and analysis

引用

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

来源：评论

学校读者我要写书评

暂无评论

parallel Contour-Buildup algorithm for the molecular surface

Parallel Contour-Buildup algorithm for the molecular surface

引用

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

来源：评论

学校读者我要写书评

暂无评论

1st ieee symposium on large-Scale data Analysis and visualization 2011, LDAV 2011 - Proceedings

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Scalable parallel building blocks for custom data analysis

Scalable parallel building blocks for custom data analysis

引用

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

来源：评论

学校读者我要写书评

暂无评论

Message from the program chairs

Message from the program chairs

引用

1st ieee symposium on large-Scale data Analysis and visualization 2011, LDAV 2011

作者： Rogers, David Silva, Cláudio T. Sandia National Laboratories United States Polytechnic Institute of New York University United States

ISBN: (纸本)9781467301541

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

parallel clustering for visualizing large scientific line data

Parallel clustering for visualizing large scientific line da...

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：