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检索条件"任意字段=Proceedings of the IEEE 2001 symposium on parallel and large-data visualization and graphics"

共 388 条记录，以下是71-80 订阅

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visualization of multivariate dark matter halos in cosmology simulations

Visualization of multivariate dark matter halos in cosmology...

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2013 3rd ieee symposium on large-Scale data Analysis and visualization, LDAV 2013

作者： Takle, Jay Silver, Deborah Kovacs, Eve Heitmann, Katrin Dept. of Electrical and Computer Engineering Rutgers University United States High Energy Physics Division Argonne National Laboratory United States

ISBN: (纸本)9781479916597

In this poster, we present a new approach to visualize multivariate dark matter halos representing the spheroid part of galaxies, the disk part of galaxies, black holes and the halo itself. The data being visualized here is an end result of tracking the evolution of cosmic structures called dark matter halos in cosmological simulation and evaluating the formation and evolution of galaxies within. Cosmologists have traditionally visualized individual galaxies in the form of two dimensional density maps, graphs and parallel coordinates. We introduce a new way of mapping multiple parameters of dark matter halos to a halo-icon. This allows the scientist to view all of the parameters associated with the dark matter halos in one visualization. © 2013 ieee.

关键词： Galaxies

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Portable data-parallel visualization and analysis in distributed memory environments

Portable data-parallel visualization and analysis in distrib...

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2013 3rd ieee symposium on large-Scale data Analysis and visualization, LDAV 2013

作者： Sewell, Christopher Lo, Li-Ta Ahrens, James CCS-7 Los Alamos National Laboratory United States

ISBN: (纸本)9781479916597

data-parallelism is a programming model that maps well to architectures with a high degree of concurrency. Algorithms written using data-parallel primitives can be easily ported to any architecture for which an implementation of these primitives exists, making efficient use of the available parallelism on each. We have previously published results demonstrating our ability to compile the same data-parallel code for several visualization algorithms onto different on-node parallel architectures (GPUs and multi-core CPUs) using our extension of NVIDIA's Thrust library. In this paper, we discuss our extension of Thrust to support concurrency in distributed memory environments across multiple nodes. This enables the application developer to write data-parallel algorithms while viewing the data as single, long vectors, essentially without needing to explicitly take into consideration whether the values are actually distributed across nodes. Our distributed wrapper for Thrust handles the communication in the backend using MPI, while still using the standard Thrust library to take advantage of available on-node parallelism. We describe the details of our distributed implementations of several key data-parallel primitives, including scan, scatter/gather, sort, reduce, and upper/lower bound. We also present two higher-level distributed algorithms developed using these primitives: isosurface and KD-tree construction. Finally, we provide timing results demonstrating the ability of these algorithms to take advantage of available parallelism on nodes and across multiple nodes, and discuss scaling limitations for communication-intensive algorithms such as KD-tree construction. © 2013 ieee.

关键词： parallel architectures

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DRepl: Optimizing Access to Application data for Analysis and visualization

DRepl: Optimizing Access to Application Data for Analysis an...

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29th ieee symposium on Massive Storage Systems and Technologies (MSST) - Research Track

作者： Ionkov, Latchesar Lang, Michael Maltzahn, Carlos Los Alamos Natl Lab Los Alamos NM 87545 USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9781479902170;9781479902187

Until recently most scientific applications produced data that is saved, analyzed and visualized at later time. In recent years, with the large increase in the amount of data and computational power available there is demand for applications to support data access in-situ, or close-to simulation to provide application steering, analytics and visualization. data access patterns required for these activities are usually different than the data layout produced by the application. In most of the large HPC clusters scientific data is stored in parallel file systems instead of locally on the cluster nodes. To increase reliability, the data is replicated, using standard RAID schemes. parallel file server nodes usually have more processing power than they need, so it is feasible to offload some of the data intensive processing to them. DRepl replaces the standard methods of data replication with replicas having different layouts, optimized for the most commonly used access patterns. Replicas can be complete (i.e. any other replica can be reconstructed from it), or incomplete. DRepl consists of a language to describe the dataset and the necessary data layouts and tools to create a user-space file server that provides and keeps the data consistent and up to date in all optimized layouts. DRepl decouples the data producers and consumers and the data layouts they use from the way the data is stored on the storage system. DRepl has shown up to 2x for cumulative performance when data is accessed using optimized replicas.

关键词： data storage data replication fault tolerance exascale DISC

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parallel data acquisition for visualization of very large sparse matrices

Parallel data acquisition for visualization of very large sp...

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15th International symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2013

作者： Langr, Daniel Šimeček, Ivan Tvrdík, Pavel Dytrych, Tomáš Czech Technical University in Prague Faculty of Information Technology Thákurova 9 160 00 Praha Czech Republic Louisiana State University Department of Physics and Astronomy Baton Rouge LA 70803 United States

ISBN: (纸本)9781479930357

The problem of visualization of very large sparse matrices emerging on massively parallel computer systems is identified and a new method along with an accompanying algorithm for parallel acquisition of visualization data for such matrices are presented. The proposed method is based on downsampling a matrix into blocks for which the desired visualization data are saved into a file. This file is then supposed to be downloaded and processed into a final image on a personal computer. Experimental results for the evaluation of the performance and scalability of the proposed algorithm are further provided and discussed. © 2013 ieee.

关键词： data acquisition

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Efficient I/O using dedicated cores in large-scale HPC simulations

Efficient I/O using dedicated cores in large-scale HPC simul...

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2013 ieee 37th Annual Computer Software and Applications Conference, COMPSAC 2013

作者： Dorier, Matthieu ENS Cachan Brittany IRISA Rennes France

ISBN: (纸本)9780769549798

large-scale HPC simulations running on cutting-edge machines raise new challenges when it comes to efficiently write, store, analyse and visualize large amounts of data. This PhD research focuses on the Damaris approaches, which leverages dedicated cores on multicore SMP nodes to achieve scalable I/O and data management. We here present past results demonstrating the efficiency of the approach in scaling I/O performance and hiding I/O variability. We then present current work and future directions related to in-situ data analysis and visualization. © 2013 ieee.

关键词： data visualization

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EuroVis 2013 - Eurographics Conference on visualization 2013

EuroVis 2013 - Eurographics Conference on Visualization 2013

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15th Eurographics Conference on visualization, EuroVis 2013

The proceedings contain 49 papers. The topics discussed include: evaluating isosurfaces with level-set-based information maps;constructing isosurfaces with sharp edges and corners using cube merging;progressive high-quality response surfaces for visually guided sensitivity analysis;evolutionary visual exploration: evaluation with expert users;evaluation of attention-guiding video visualization;visual analysis of set relations in a graph;maximum entropy summary trees;an interactive analysis and exploration tool for epigenomic data;towards high-dimensional data analysis in air quality research;HiFiVE: a Hilbert space embedding of fiber variability estimates for uncertainty modeling and visualization;nonparametric models for uncertainty visualization;visualizing large-scale parallel communication traces using a particle animation technique;and TAMRESH - tensor approximation multiresolution hierarchy for interactive volume visualization.

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I/O containers: Managing the data analytics and visualization pipelines of high end codes

I/O containers: Managing the data analytics and visualizatio...

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2013 ieee 37th Annual Computer Software and Applications Conference, COMPSAC 2013

作者： Dayal, Jai Cao, Jianting Eisenhauer, Greg Schwan, Karsten Wolf, Matthew Zheng, Fang Abbasi, Hasan Klasky, Scott Podhorszki, Norbert Lofstead, Jay Georgia Institute of Technology United States Oak Ridge National Labs United States

ISBN: (纸本)9780769549798

Lack of I/O scalability is known to cause measurable slowdowns for large-scale scientific applications running on high end machines. This is prompting researchers to devise 'I/O staging' methods in which outputs are processed via online analysis and visualization methods to support desired science outcomes. Organized as online workflows and carried out in I/O pipelines, these analysis components run concurrently with science simulations, often using a smaller set of nodes on the high end machine termed 'staging areas'. This paper presents a new approach to dealing with several challenges arising for such online analytics, including: how to efficiently run multiple analytics components on staging area resources providing them with the levels of end-to-end performance they need and how to manage staging resources when analytics actions change due to user or data-dependent behavior. Our approach designs and implements middleware constructs that delineate and manage I/O pipeline resources called 'I/O Containers'. Experimental evaluations of containers with realistic scientific applications demonstrate the feasibility and utility of the approach. © 2013 ieee.

关键词： visualization

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Efficient parallel volume rendering of large-scale adaptive mesh refinement data

Efficient parallel volume rendering of large-scale adaptive ...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Nick Leaf Venkatram Vishwanath Joseph Insley Mark Hereld Michael E. Papka Kwan-Liu Ma University of California Davis Argonne National Laboratory

ISBN: (纸本)9781479924363

Adaptive Mesh Refinement is a popular approach for allocating scarce computing resources to the most important portions of the simulation domain. This approach implies spatial compression and the large simulation sizes which necessitate it. We present a novel, cluster- and GPU-parallel rendering scheme for AMR data, which is built on previous work in the GPU ray casting of AMR data. Our approach utilizes the existing AMR structure to subdivide the problem into convexly-bounded chunks and perform static load-balancing. We take advantage of data locality within chunks to interpolate directly between blocks without the need to store ghost cells on the interior boundaries. We also present a novel block decomposition method, and analyze its performance against two alternative methods. Finally, we examine the interactivity of our renderer for multiple datasets, and consider its scalability across a large number of GPUs.

关键词： graphics processing units Interpolation Rendering (computer graphics) Octrees Adaptation models Computational modeling Casting

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ieee symposium on large data Analysis and visualization 2012, LDAV 2012 - proceedings

IEEE Symposium on Large Data Analysis and Visualization 2012...

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2nd symposium on large-Scale data Analysis and visualization, LDAV 2012

ISBN: (纸本)9781467347334

The proceedings contain 18 papers. The topics discussed include: panning and zooming the observable universe with subsequence-matching trees;interactive exploration of large-scale time-varying data using dynamic tracking graphs;interactive transfer function design on large multiresolution volumes;query-driven parallel exploration of large datasets;efficient parallel extraction of crack-free isosurfaces from adaptive mesh refinement (AMR) data;parallel stream surface computation for large data sets;salient time steps selection from large scale time-varying data sets with dynamic time warping;Gaussian mixture model based volume visualization;virtual rheoscopic fluids for dense, large-scale fluid flow visualizations;meta parallel coordinates for visualizing features in large, high-dimensional, time-varying data;redirecting research in large-format displays for visualization;flow-guided file layout for out-of-core pathline computation;and scalable computation of distributions from large scale data sets.

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Efficient parallel extraction of crack-free isosurfaces from adaptive mesh refinement (AMR) data

Efficient parallel extraction of crack-free isosurfaces from...

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2nd symposium on large-Scale data Analysis and visualization, LDAV 2012

作者： Weber, Gunther H. Childs, Hank Meredith, Jeremy S. Lawrence Berkeley National Laboratory University of California Davis CA United States Oak Ridge National Laboratory United States

ISBN: (纸本)9781467347334

We present a novel extraction scheme for crack-free isosurfaces from adaptive mesh refinement (AMR) data that builds on prior work utilizing dual grids and filling resulting gaps with stitch cells. We use a case-table-based approach to simplify the implementation of stitch cell generation. The most significant benefit of our new approach is that it uses ghost data to handle parallel isosurface extraction efficiently. We further present the results of applying this method to large scale data sets and analyze its computation time on parallel high-performance computing (HPC) platforms. © 2012 ieee.

关键词： Mesh generation

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