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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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Visualizing multiscale, multiphysics simulation data: Brain blood flow

Visualizing multiscale, multiphysics simulation data: Brain ...

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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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Evaluating the benefits of an extended memory hierarchy for parallel streamline algorithms

Evaluating the benefits of an extended memory hierarchy for ...

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

作者： Camp, David Childs, Hank Chourasia, Amit Garth, Christoph Joy, Kenneth I. Lawrence Berkeley National Laboratory Department of Computer Science University of California Davis CA United States San Diego Supercomputer Center University of California San Diego CA United States Department of Computer Science University of California Davis CA United States

ISBN: (纸本)9781467301541

The increasing cost of achieving sufficient I/O bandwidth for high end supercomputers is leading to architectural evolutions in the I/O subsystem space. Currently popular designs create a staging area on each compute node for data output via solid state drives (SSDs), local hard drives, or both. In this paper, we investigate whether these extensions to the memory hierarchy, primarily intended for computer simulations that produce data, can also benefit visualization and analysis programs that consume data. Some algorithms, such as those that read the data only once and store the data in primary memory, can not draw obvious benefit from the presence of a deeper memory hierarchy. However, algorithms that read data repeatedly from disk are excellent candidates, since the repeated reads can be accelerated by caching the first read of a block on the new resources (i.e. SSDs or hard drives). We study such an algorithm, streamline computation, and quantify the benefits it can derive. © 2011 ieee.

关键词： Supercomputers

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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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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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Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction

Uncertainty-aware exploration of continuous parameter spaces...

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13th Eurographics / ieee symposium on visualization, EuroVis 2011

作者： Berger, W. Piringer, H. Filzmoser, P. Gröller, E. VRVis Research Center Austria Department of Statistics and Probability Theory Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

Systems projecting a continuous n-dimensional parameter space to a continuous m-dimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this paper is an interactive approach to enable a continuous analysis of a sampled parameter space with respect to multiple target values. We employ methods from statistical learning to predict results in real-time at any user-defined point and its neighborhood. In particular, we describe techniques to guide the user to potentially interesting parameter regions, and we visualize the inherent uncertainty of predictions in 2D scatterplots and parallel coordinates. An evaluation describes a real-world scenario in the application context of car engine design and reports feedback of domain experts. The results indicate that our approach is suitable to accelerate a local sensitivity analysis of multiple target dimensions, and to determine a sufficient local sampling density for interesting parameter regions. © 2011 The Author(s) Journal compilation © 2011 The Eurographics Association and Blackwell Publishing Ltd. Published by Blackwell Publishing.

关键词： Forecasting

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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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STREAMIT: Dynamic visualization and interactive exploration of text streams

STREAMIT: Dynamic visualization and interactive exploration ...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Jamal Alsakran Yang Chen Ye Zhao Jing Yang Dongning Luo Kent University USA North Carolina State University Charlotte USA

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 Dynamics Animation Data visualization Computational modeling Layout graphics processing unit

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