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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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parallel in situ indexing for data-intensive computing

Parallel in situ indexing for data-intensive computing

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

作者： Kim, Jinoh Abbasi, Hasan Chacón, Luis Docan, Ciprian Klasky, Scott Liu, Qing Podhorszki, Norbert Shoshani, Arie Wu, Kesheng Lawrence Berkeley National Lab. United States University of Tennessee Knoxville TX United States Oak Ridge National Lab. United States Rutgers University United States

ISBN: (纸本)9781467301541

As computing power increases exponentially, vast amount of data is created by many scientific research activities. However, the bandwidth for storing the data to disks and reading the data from disks has been improving at a much slower pace. These two trends produce an ever-widening data access gap. Our work brings together two distinct technologies to address this data access issue: indexing and in situ processing. From decades of database research literature, we know that indexing is an effective way to address the data access issue, particularly for accessing relatively small fraction of data records. As data sets increase in sizes, more and more analysts need to use selective data access, which makes indexing an even more important for improving data access. The challenge is that most implementations of indexing technology are embedded in large database management systems (DBMS), but most scientific datasets are not managed by any DBMS. In this work, we choose to include indexes with the scientific data instead of requiring the data to be loaded into a *** use compressed bitmap indexes from the FastBit software which are known to be highly effective for query-intensive workloads common to scientific data analysis. To use the indexes, we need to build them first. The index building procedure needs to access the whole data set and may also require a significant amount of compute time. In this work, we adapt the in situ processing technology to generate the indexes, thus removing the need of reading data from disks and to build indexes in parallel. The in situ data processing system used is ADIOS, a middleware for high-performance I/O. Our experimental results show that the indexes can improve the data access time up to 200 times depending on the fraction of data selected, and using in situ data processing system can effectively reduce the time needed to create the indexes, up to 10 times with our in situ technique when using identical parallel settings. © 2011

关键词： Middleware

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EuroVis 2011 - Eurographics / ieee symposium on visualization 2011

EuroVis 2011 - Eurographics / IEEE Symposium on Visualizatio...

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13th Eurographics / ieee symposium on visualization, EuroVis 2011

The proceedings contain 52 papers. The topics discussed include: curve density estimates;visual coherence for large-scale line-plot visualizations;progressive splatting of continuous scatterplots and parallel coordinates;topology-based visualization of transformation pathways in complex chemical systems;interactive exploration of protein cavities;illustrative molecular visualization with continuous abstraction;a visual analytics approach for peak-preserving prediction of large seasonal time series;visual exploration of time-series data with shape space projections;interactive visual analysis of temporal cluster structures;exploring collections of tagged text for literary scholarship;Lagrangian coherent structures with guaranteed material separation;energy-scale aware feature extraction for flow visualization;and evaluation of the visibility of vessel movement features in trajectory visualizations.

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VisIO: Enabling interactive visualization of ultra-scale, time series data via high-bandwidth distributed I/O systems

VisIO: Enabling interactive visualization of ultra-scale, ti...

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25th ieee International parallel and Distributed Processing symposium, IPDPS 2011

作者： Mitchell, Christopher Ahrens, James Wang, Jun Department of Electrical Engineering and Computer Science University of Central Florida Orlando FL 32816-2450 United States Computer Computational and Statistical Sciences Los Alamos National Laboratory Los Alamos NM 87545 United States

ISBN: (纸本)9780769543857

Petascale simulations compute at resolutions ranging into billions of cells and write terabytes of data for visualization and analysis. Interactive visualization of this time series is a desired step before starting a new run. The I/O subsystem and associated network often are a significant impediment to interactive visualization of time-varying data, as they are not configured or provisioned to provide necessary I/O read rates. In this paper, we propose a new I/O library for visualization applications: VisIO. visualization applications commonly use N-to-N reads within their parallel enabled readers which provides an incentive for a shared-nothing approach to I/O, similar to other data-intensive approaches such as Hadoop. However, unlike other data-intensive applications, visualization requires: (1) interactive performance for large data volumes, (2) compatibility with MPI and POSIX file system semantics for compatibility with existing infrastructure, and (3) use of existing file formats and their stipulated data partitioning rules. VisIO, provides a mechanism for using a non-POSIX distributed file system to provide linear scaling of I/O bandwidth. In addition, we introduce a novel scheduling algorithm that helps to co-locate visualization processes on nodes with the requested data. Testing using VisIO integrated into Para View was conducted using the Hadoop Distributed File System (HDFS) on TACC's Longhorn cluster. A representative dataset, VPIC, across 128 nodes showed a 64.4% read performance improvement compared to the provided Lustre installation. Also tested, was a dataset representing a global ocean salinity simulation that showed a 51.4% improvement in read performance over Lustre when using our VisIO system. VisIO, provides powerful high-performance I/O services to visualization applications, allowing for interactive performance with ultra-scale, time-series data. © 2011 ieee.

关键词： data visualization

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Constructing limited Voronoi mesh in parallel based on multi-core environment and its implementation

Constructing limited Voronoi mesh in parallel based on multi...

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2011 4th International symposium on parallel Architectures, Algorithms and Programming, PAAP 2011

作者： Dong, Lanfang Wu, Yuan Li, Meng Vision Computing and Visualization Laboratory School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9780769545752

Voronoi mesh as the basic primitive in the field of computational geometry has been widely applied on solving fluid-related problem by finite volume method. By analyzing various limited conditions for reasonable point distributions and modifying parallel data structure (doubly-connected list) for complex boundaries features in Voronoi mesh generation, considering the rapid development of multi-core processors and high performance computing, we present an efficient and feasible parallel algorithm of limited Voronoi mesh generation in standalone multi-core CPU environment. The algorithm solves the practical application of Voronoi mesh generation of large-scale points set;the experiment results show that this method improved the efficiency of the algorithm and could be widely used in various applications. © 2011 ieee.

关键词： parallel algorithms

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parallel Contour-Buildup algorithm for the molecular surface

Parallel Contour-Buildup algorithm for the molecular surface

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ieee symposium on Biological data visualization (BioVis)

作者： Michael Krone Sebastian Grottel Thomas Ertl Visualization Research Center (VISUS) University of Stuttgart Germany Visualization and Interactive Systems Institute (VIS) University of Stuttgart Germany

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.

关键词： Probes graphics processing unit data visualization Synthetic aperture sonar Atomic measurements Solvents Rendering (computer graphics)

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Scalable multivariate volume visualization and analysis

Scalable multivariate volume visualization and analysis

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ieee symposium on large data Analysis and visualization (LDAV)

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

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.

关键词： data visualization Rendering (computer graphics) Shock waves Graphical user interfaces Numerical models Entropy Educational institutions

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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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ieee symposium on large data Analysis and visualization (LDAV)

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

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.

关键词： Pipelines data visualization Concurrent computing graphics processing unit Instruction sets parallel processing Algorithm design and analysis

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parallel visualization on large clusters using MapReduce

Parallel visualization on large clusters using MapReduce

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ieee symposium on large data Analysis and visualization (LDAV)

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

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 80000 2 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.

关键词： visualization Runtime

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Exploring large data over wide area networks

Exploring large data over wide area networks

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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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