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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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Progressive high-quality response surfaces for visually guided sensitivity analysis

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

引用

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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Interactive rendering and efficient querying for large multivariate seismic volumes on consumer level PCs

Interactive rendering and efficient querying for large multi...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Liang Zhou Charles Hansen SCI Institute and the School of Computing University of Utah

ISBN: (纸本)9781479924363

We present a volume visualization method that allows interactive rendering and efficient querying of large multivariate seismic volume data on consumer level PCs. The volume rendering pipeline utilizes a virtual memory structure that supports out-of-core multivariate multi-resolution data and a GPU-based ray caster that allows interactive multivariate transfer function design. A Gaussian mixture model representation is precomputed and nearly interactive querying is achieved by testing the Gaussian functions against user defined transfer functions on the GPU in the runtime. Finally, the method has been tested on a multivariate 3D seismic dataset which is larger than the size of the main memory of the testing machine.

关键词： data visualization Transfer functions graphics processing units Runtime Educational institutions Rendering (computer graphics) Three-dimensional displays

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Visualizing large-scale atomistic simulations in ultra-resolution immersive environments

Visualizing large-scale atomistic simulations in ultra-resol...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Khairi Reda Aaron Knoll Ken-ichi Nomura Michael E. Papka Andrew E. Johnson Jason Leigh University of Illinois at Chicago Texas Advanced Computing Center University of Southern California Argonne National Laboratory

ISBN: (纸本)9781479924363

Molecular Dynamics is becoming a principle methodology in the study of nanoscale systems, paving the way for innovations in battery design and alternative fuel applications. With the increasing availability of computational power and advances in modeling, atomistic simulations are rapidly growing in scale and complexity. Despite the plethora of molecular visualization techniques, visualizing and exploring large-scale atomistic simulations remain difficult. Existing molecular representations are not perceptually scalable and often adopt a rigid definition of surfaces, making them inappropriate for nanostructured materials where boundaries are inherently ill-defined. In this paper, we present an application for the interactive visualization and exploration of large-scale atomistic simulations in ultra-resolution immersive environments. We employ a hybrid representation which combines solid ball-and-stick glyphs with volumetric surfaces to visually convey the uncertainty in molecular boundaries at the nanoscale. We also describe a scalable, distributed GPU ray-casting implementation capable of rendering complex atomistic simulations with millions of atoms in real-time.

关键词： Rendering (computer graphics) visualization Solid modeling Macrocell networks Computational modeling graphics processing units Transfer functions

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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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ieee symposium on large data Analysis and visualization (LDAV)

作者： Chongke Bi Kenji Ono Kwan-Liu Ma Haiyuan Wu Toshiyuki Imamura RIKEN Rikagaku Kenkyujo Yokohama Campus Yokohama Kanagawa JP University of California Davis Wakayama University

ISBN: (纸本)9781479924363

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.

关键词： Computational efficiency data compression data visualization Computers Image coding Binary trees Standards

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Comparative case study between D3 & Highcharts on Lustre metadata visualization

Comparative case study between D3 & Highcharts on Lustre met...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Omar ElTayeby Dwayne John Pragnesh Patel Scott Simmerman Clark Atlanta University National Institute for Computational Sciences

ISBN: (纸本)9781479924363

One of the challenging tasks in visual analytics is to target clustered time-series data sets, since it is important for data analysts to discover patterns changing over time while keeping their focus on particular subsets. A web-based application to monitor the Lustre file system for system administrators and operation teams has been developed using D3 and Highcharts. This application is a use case to compare those JavaScript libraries demonstrating the differences in capabilities of using them. The goal of this application is to provide time-series visuals of the Remote Procedure Calls (RPCs) and storage patterns of users on Kraken, a University of Tennessee High Performance Computing (HPC) resource in Oak Ridge National Laboratory (ORNL).

关键词： data visualization Libraries Monitoring Correlation Rendering (computer graphics) Educational institutions Indexes

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Distributed parallel particle advection using work requesting

Distributed parallel particle advection using work requestin...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Cornelius Müller David Camp Bernd Hentschel Christoph Garth University of Kaiserslautern Lawrence Berkeley National Laboratory RWTH Aachen University

ISBN: (纸本)9781479924363

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.

关键词： Runtime Scalability Load management Dynamic scheduling Instruction sets data visualization Electronic mail

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Trelliscope: A system for detailed visualization in the deep analysis of large complex data

Trelliscope: A system for detailed visualization in the deep...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Ryan Hafen Luke Gosink Jason McDermott Karin Rodland Kerstin Kleese-Van Dam William S. Cleveland Pacific Northwest National Lab Purdue University

ISBN: (纸本)9781479924363

Trelliscope emanates from the Trellis Display framework for visualization and the Divide and Recombine (D&R) approach to analyzing large complex data. In Trellis, the data are broken up into subsets, a visualization method is applied to each subset, and the display result is an array of panels, one per subset. This is a powerful framework for visualization of data, both small and large. In D&R, the data are broken up into subsets, and any analytic method from statistics and machine learning is applied to each subset independently. Then the outputs are recombined. This provides not only a powerful framework for analysis, but also feasible and practical computations using distributed computational facilities. It enables deep analysis of the data: study of both data summaries as well as the detailed data at their finest granularity. This is critical to full understanding of the data. It also enables the analyst to program using an interactive high-level language for data analysis such as R, which allows the analyst to focus more on the data and less on code. In this paper we introduce Trelliscope, a system that scales Trellis to large complex data. It provides a way to create displays with a very large number of panels and an interactive viewer that allows the analyst to sort, filter, and sample the panels in a meaningful way. We discuss the underlying principles, design, and scalable architecture of Trelliscope, and illustrate its use on three analysis projects in proteomics, high intensity physics, and power systems engineering.

关键词： data visualization Measurement visualization data analysis Distributed databases Workstations Rendering (computer graphics)

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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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ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Matthieu Dorier Gabriel Antoniu Luc Bouge IRISA Rennes Bretagne FR INRIA Rennes Bretagne-Atlantique Rennes France ENS Cachan Brittany Cachan France

ISBN: (纸本)9781479913725

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.

关键词： data visualization Scalability data models Analytical models Multicore processing Adaptation models Libraries

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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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ieee symposium on large data Analysis and visualization (LDAV)

作者： Christopher Sewell Li-ta Lo James Ahrens CCS-7 Los Alamos National Laboratory

ISBN: (纸本)9781479924363

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.

关键词： Program processors Vectors Algorithm design and analysis Indexes Isosurfaces Computer architecture parallel processing

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