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2013 3rd ieee symposium on Large-Scale Data Analysis and visualization, LDAV 2013

ISBN: (纸本)9781479916597

The proceedings contain 23 papers. The topics discussed include: distributed parallel particle advection using work requesting;a scalable algorithm for single-linkage hierarchical clustering on distributed-memory architectures;portable data-parallel visualization and analysis in distributed memory environments;efficient parallel volume rendering of large-scale adaptive mesh refinement data;visualizing large-scale atomistic simulations in ultra-resolution immersive environments;a provably-robust sampling method for generating colormaps of large data;trelliscope: a system for detailed visualization in the deep analysis of large complex data;filtering edge for exploration of large graphs;proper orthogonal decomposition based parallel compression for visualizing big data on the K Computer;and visual analysis on online display advertising data.

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Distributed parallel particle advection using work requesting

Distributed parallel particle advection using work requestin...

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2013 3rd ieee symposium on Large-Scale Data Analysis and visualization, LDAV 2013

作者： Muller, Cornelius Camp, David Hentschel, Bernd Garth, Christoph University of Kaiserslautern Germany Lawrence Berkeley National Laboratory United States RWTH Aachen University Germany

ISBN: (纸本)9781479916597

Particle advection is an important vector field visualization technique that is difficult to apply to very large data sets in a distributed setting due to scalability limitations in existing algorithms. In this paper, we report on several experiments using work requesting dynamic scheduling which achieves balanced work distribution on arbitrary problems with minimal communication overhead. We present a corresponding prototype implementation, provide and analyze benchmark results, and compare our results to an existing algorithm. © 2013 ieee.

关键词： Data visualization

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Proper orthogonal decomposition based parallel compression for visualizing big data on the K computer

Proper orthogonal decomposition based parallel compression f...

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2013 3rd ieee symposium on Large-Scale Data Analysis and visualization, LDAV 2013

作者： Bi, Chongke Ono, Kenji Ma, Kwan-Liu Wu, Haiyuan Imamura, Toshiyuki RIKEN Japan University of California Davis United States Wakayama University Japan

ISBN: (纸本)9781479916597

The development of supercomputers has greatly help us to carry on large-scale computing for dealing with various problems through simulating and analyzing them. visualization is an indispensable tool to understand the properties of the data from supercomputers. Especially, interactive visualization can help us to analyze data from various viewpoints and even to find out some local small but important features. However, it is still difficult to interactively visualize such kind of big data directly due to the slow file I/O problem and the limitation of memory size. For resolving these problems, we proposed a parallel compression method to reduce the data size with low computational cost. Furthermore, the fast linear decompression process is another merit for interactive visualization. Our method uses proper orthogonal decomposition (POD) to compress data because it can effectively extract important features from the data and the resulting compressed data can also be linearly decompressed. Our implementation achieves high parallel efficiency with a binary load-distributed approach, which is similar to the binary-swap image composition used in parallel volume rendering [2]. This approach allows us to effectively utilize all the processing nodes and reduce the interprocessor communication cost throughout the parallel compression calculations. Our test results on the K computer demonstrate superior performance of our design and implementation. © 2013 ieee.

关键词： Supercomputers

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

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

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2013 3rd ieee symposium on Large-Scale Data Analysis and visualization, LDAV 2013

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

ISBN: (纸本)9781479916597

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. © 2013 ieee.

关键词： Volume rendering

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Leveraging GPUs in Ab initio nuclear physics calculations

Leveraging GPUs in Ab initio nuclear physics calculations

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

作者： Oryspayev, Dossay Potter, Hugh Maris, Pieter Sosonkina, Masha Vary, James P. Binder, Sven Calci, Angelo Langhammer, Joachim Roth, Robert U.S. DOE Ames Laboratory Iowa State University Ames IA 50011 United States Department of Physics and Astronomy Iowa State University Ames IA 50011 United States Department of Modeling Simulation and Visualization Engineering Old Dominion University Norfolk VA 23529 United States Institut fur Kernphysik Technische Universiẗat Darmstadt 64289 Darmstadt Germany

ISBN: (纸本)9780769549798

This paper describes initial steps to leverage accelerators, such as GPUs, in ab initio nuclear physics calculations. Specifically, parallel nuclear structure calculations performed by the MFDn package are considered with selected stages adapted for GPUs. This paper outlines the necessary steps to make MFDnutilize GPUs in its matrix construction stage. The experiments are presented to compare the multithreaded CPU-based version of this phase with the newly designedGPU-based version. Four-to ten-fold speedups were observed for the code executing on GPUs. © 2013 ieee.

关键词： graphics processing unit

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Progressive high-quality response surfaces for visually guided sensitivity analysis

Progressive high-quality response surfaces for visually guid...

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15th Eurographics Conference on visualization, EuroVis 2013

作者： Demir, I. Westermann, R. Computer Graphics and Visualization Group Technische Universität München Germany

In this paper we present a technique which allows us to perform high quality and progressive response surface prediction from multidimensional input samples in an efficient manner. We utilize kriging interpolation to estimate a response surface which minimizes the expectation value and variance of the prediction error. High computational efficiency is achieved by employing parallel matrix and vector operations on the GPU. Our approach differs from previous kriging approaches in that it uses a novel progressive updating scheme for new samples based on blockwise matrix inversion. In this way we can handle very large sample sets to which new samples are continually added. Furthermore, we can monitor the incremental evolution of the surface, providing a means to early terminate the computation when no significant changes have occurred. When the generation of input samples is fast enough, our technique enables steering this generation process interactively to find relevant dependency relations. © 2013 The Author(s).

关键词： Computational efficiency

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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

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

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Guest Editor's Introduction: Special Section on the Eurographics symposium on parallel graphics and visualization (EGPGV)

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2012年第6期18卷 837-837页

作者： Pajarola, Renato Zhou, Kun Univ Zurich Visualizat & MultiMedia Lab CH-8006 Zurich Switzerland Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Zhejiang Peoples R China

The articles in this special section contain selected papers from the Eurographics symposium on parallel graphics and visualization (EGPGV).

关键词： Special Issues And Sections Meetings Computer graphics V Isualization

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