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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 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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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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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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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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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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ieee Pacific visualization symposium 2011, PacificVis 2011 - proceedings

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

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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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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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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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