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ieee symposium on large data Analysis and visualization (LDAV)

作者： Silvio Rizzi Mark Hereld Joseph Insley Michael E. Papka Thomas Uram Venkatram Vishwanath Argonne National Laboratory Argonne National Laboratory Northern Illinois University

In this poster we propose to interconnect LAMMPS and vl3, our parallel framework for large-scale visualization and analysis. The goal is co-visualization and exploration at interactive frame rates of large-scale atomistic simulations. We describe the architecture of the proposed system, as well as a preliminary implementation that runs on GPU-based visualization clusters.

关键词： data visualization Computational modeling Rendering (computer graphics) Analytical models graphics processing units Libraries

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Flying edges: A high-performance scalable isocontouring algorithm

Flying edges: A high-performance scalable isocontouring algo...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： William Schroeder Rob Maynard Berk Geveci Kitware Inc.

Isocontouring remains one of the most widely used visualization techniques. While a plethora of important contouring algorithms have been developed over the last few decades, many were created prior to the advent of ubiquitous parallel computing systems. With the emergence of large data and parallel architectures, a rethinking of isocontouring and other visualization algorithms is necessary to take full advantage of modern computing hardware. To this end we have developed a high-performance isocontouring algorithm for structured data that is designed to be inherently scalable. Processing is performed completely independently along edges over multiple passes. This novel algorithm also employs computational trimming based on geometric reasoning to eliminate unnecessary computation, and removes the parallel bottleneck due to coincident point merging. As a result the algorithm performs well in serial or parallel execution, and supports heterogeneous parallel computation combining data parallel and shared memory approaches. Further it is capable of processing data too large to fit entirely inside GPU memory, does not suffer additional costs due to preprocessing and search structures, and is the fastest non-preprocessed isocontouring algorithm of which we are aware on shared memory, multi-core systems. The software is currently available under a permissive, open source licence in the VTK visualization system.

关键词： data visualization Algorithm design and analysis Hardware parallel processing graphics processing units Memory management Feature extraction

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Folding Methods for Event Timelines in Performance Analysis

Folding Methods for Event Timelines in Performance Analysis

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

作者： Matthias Weber Ronald Geisler Holger Brunst Wolfgang E. Nagel Center for Information Services and High Performance Computing Technische Universität Dresden Germany

ISBN: (纸本)9781467376853

The complexity of today's high performance computing systems, and their parallel software, requires performance analysis tools to fully understand application performance behavior. The visualization of event streams has proven to be a powerful approach for the detection of various types of performance problems. However, visualization of large numbers of process streams quickly hits the limits of available screen resolution. To alleviate this problem we propose folding strategies for event timelines that consider common questions during performance analysis. We demonstrate the effectiveness of our solution with code inefficiencies in two real-world applications, PIConGPU and COSMO-SPECS. Our methods facilitate visual scalability and provide powerful overviews of performance data at the same time. Furthermore, our folding strategies improve GPU stream visualization and allow easy evaluation of the GPU device utilization.

关键词： data visualization graphics processing units visualization Kernel Scalability Performance analysis Aggregates

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parallel unsteady flow line integral convolution for high-performance dense visualization

Parallel unsteady flow line integral convolution for high-pe...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Zi'ang Ding Zhanping Liu Yang Yu Wei Chen Department of Computer Science Purdue University Division of Computer Science Kentucky State University State Key Lab of CAD&CG Zhejiang University

ISBN: (纸本)9781467368803

This paper presents an accurate parallel implementation of unsteady flow line integral convolution (UFLIC) for high-performance visualization of large time-varying flows. Our approach differs from previous implementations by using a novel value scattering+gathering mechanism to parallelize UFLIC and designing a pathline reuse strategy to reduce the computational cost of pathline integration. By exploiting the massive parallelism of modern graphical processing units (GPU), the proposed method allows for real-time dense visualization of unsteady flows with high spatial-temporal coherence.

关键词： data visualization Convolution graphics processing units visualization Coherence Animation

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Ray tracing within a data parallel framework

Ray tracing within a data parallel framework

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Matthew Larsen Jeremy S. Meredith Paul A. Navrátil Hank Childs University of Oregon Oak Ridge National Laboratory Texas Advanced Computing Center University of Texas at Austin Lawrence Berkeley National Laboratory

Current architectural trends on supercomputers have dramatic increases in the number of cores and available computational power per die, but this power is increasingly difficult for programmers to harness effectively. High-level language constructs can simplify programming many-core devices, but this ease comes with a potential loss of processing power, particularly for cross-platform constructs. Recently, scientific visualization packages have embraced language constructs centering around data parallelism, with familiar operators such as map, reduce, gather, and scatter. Complete adoption of data parallelism will require that central visualization algorithms be revisited, and expressed in this new paradigm while preserving both functionality and performance. This investment has a large potential payoff: portable performance in software bases that can span over the many architectures that scientific visualization applications run on. With this work, we present a method for ray tracing consisting of entirely of data parallel primitives. Given the extreme computational power on nodes now prevalent on supercomputers, we believe that ray tracing can supplant rasterization as the work-horse graphics solution for scientific visualization. Our ray tracing method is relatively efficient, and we describe its performance with a series of tests, and also compare to leading-edge ray tracers that are optimized for specific platforms. We find that our data parallel approach leads to results that are acceptable for many scientific visualization use cases, with the key benefit of providing a single code base that can run on many architectures.

关键词： Ray tracing Computer architecture parallel processing graphics processing units data visualization DSL

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A Survey of Software Frameworks for Cluster-Based large High-Resolution Displays

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2014年第8期20卷 1158-1177页

作者： Chung, Haeyong Andrews, Christopher North, Chris Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24060 USA Middlebury Coll Dept Comp Sci Middlebury VT 05753 USA

large high-resolution displays (LHRD) enable visualization of extremely large-scale data sets with high resolution, large physical size, scalable rendering performance, advanced interaction methods, and collaboration. Despite the advantages, applications for LHRD can be developed only by a select group of researchers and programmers, since its software implementation requires design and development paradigms different from typical desktop environments. It is critical for developers to understand and take advantage of appropriate software tools and methods for developing their LHRD applications. In this paper, we present a survey of the state-of-the-art software frameworks and applications for cluster-based LHRD, highlighting a three-aspect taxonomy. This survey can aid LHRD application and framework developers in choosing more suitable development techniques and software environments for new LHRD applications, and guide LHRD researchers to open needs in LHRD software frameworks.

关键词： large high-resolution display tiled displays distributed rendering parallel rendering distributed applications graphics api large scale visualization programming models input devices and strategies

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parallel Processing and Immersive visualization of Sonar Point Clouds 4

Parallel Processing and Immersive Visualization of Sonar Poi...

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ieee symposium on large data Analysis and visualization

作者： Febretti, Alessandro Richmond, Kristof Doran, Peter Johnson, Andrew Univ Illinois Elect Visualizat Lab Chicago IL 60607 USA Frontier Astronaut LLC Chugwater WY USA Univ Illinois Dept Environm Sci Chicago IL USA

ISBN: (纸本)9781479952151

The investigation of underwater structures and natural features through Autonomous Underwater Vehicles (AUVs) is an expanding field with applications in archaeology, engineering, environmental sciences and astrobiology. Processing and analyzing the raw sonar data generated by automated surveys is challenging due to the presence of complex error sources like water chemistry, zero-depth variations, inertial navigation errors and multipath reflections. Furthermore, the complexity of the collected data makes it difficult to perform effective analysis on a standard display. Point clouds made up of hundreds of millions to billions of points are not uncommon. Highly interactive, immersive visualization is a desirable tool that researchers can use to improve the quality of a final sonar-based data product. In this paper we present a scalable toolkit for the processing and visualization of sonar point clouds on a cluster-based, large scale immersive visualization environment. The cluster is used simultaneously as a parallel processing platform that performs sonar beam-tracing of the source raw data, and as the rendering driver of the immersive display.

关键词： data visualisation geophysics computing hydrological techniques lakes parallel processing rendering (computer graphics) sonar signal processing automated surveys cluster-based immersive visualization environment immersive display rendering driver sonar beam-tracing sonar point clouds Acoustic beams Antarctica data visualization Lakes Sonar Sonar navigation Three-dimensional displays parallel PROCESSING (COMPUTERS) data visualisation sonar three-dimensional displays Acoustic beams lakes sonar signal processing geophysics computing hydrological techniques Antarctica Rendering (computer graphics) visualization

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ADR visualization: A Generalized Framework for Ranking large-Scale Scientific data using Analysis-Driven Refinement 4

ADR Visualization: A Generalized Framework for Ranking Large...

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ieee symposium on large data Analysis and visualization

作者： Nouanesengsy, Boonthanome Woodring, Jonathan Patchett, John Myers, Kary Ahrens, James Los Alamos Natl Lab Los Alamos NM 87544 USA

ISBN: (纸本)9781479952151

Prioritization of data is necessary for managing large-scale scientific data, as the scale of the data implies that there are only enough resources available to process a limited subset of the data. For example, data prioritization is used during in situ triage to scale with bandwidth bottlenecks, and used during focus+context visualization to save time during analysis by guiding the user to important information. In this paper, we present ADR visualization, a generalized analysis framework for ranking large-scale data using Analysis -Driven Refinement (ADR), which is inspired by Adaptive Mesh Refinement (AMR). A large-scale data set is partitioned in space, time, and variable, using user-defined importance measurements for prioritization. This process creates a prioritization tree over the data set. Using this tree, selection methods can generate sparse data products for analysis, such as focus+context visualizations or sparse data sets.

关键词： data triage focus plus context large-scale data big data scientific data prioritization adaptive mesh refinement I.3.1 [Computer graphics]: Hardware Architecture-parallel processing I.3.3 [Computer graphics]: Picture/Image Generation-Viewing algorithms

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ieee symposium on large data Analysis and visualization 2014, LDAV 2014 - proceedings

IEEE Symposium on Large Data Analysis and Visualization 2014...

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4th ieee symposium on large data Analysis and visualization, LDAV 2014

ISBN: (纸本)9781479952151

The proceedings contain 22 papers. The topics discussed include: data-parallel halo finding with variable linking lengths;multivariate volume visualization through dynamic projections;ADR visualization: a generalized framework for ranking large-scale scientific data using analysis-driven refinement;in-situ multi-resolution and temporal data compression for visual exploration of large-scale scientific simulations;space-time volumetric depth images for in-situ visualization;improved post hoc flow analysis via lagrangian representations;visual analysis of large dental imaging data in caries research;visual analytics of large-scale climate model data;out-of-core visualization of time-varying hybrid-grid volume data;in-situ processing and interactive visualization for large-scaled numerical simulations;and SeedMe: a cyber infrastructure for sharing results.

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An Edge-Bundling Layout for Interactive parallel Coordinates

An Edge-Bundling Layout for Interactive Parallel Coordinates

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ieee Pacific visualization symposium (PacificVis)

作者： Palmas, Gregorio Bachynskyi, Myroslav Oulasvirta, Antti Seidel, Hans-Peter Weinkauf, Tino MPI Informat Saarbrucken Germany Univ Saarland MPI Informat D-66123 Saarbrucken Germany

ISBN: (纸本)9781479928736

parallel Coordinates is an often used visualization method for multidimensional data sets. Its main challenges for large data sets are visual clutter and overplotting which hamper the recognition of patterns in the data. We present an edge-bundling method using density-based clustering for each dimension. This reduces clutter and provides a faster overview of clusters and trends. Moreover, it allows rendering the clustered lines using polygons, decreasing rendering time remarkably. In addition, we design interactions to support multidimensional clustering with this method. A user study shows improvements over the classic parallel coordinates plot in two user tasks: correlation estimation and subset tracing.

关键词： I. 3.3 [Computer graphics]: Picture/Image Generation-Line and curve generation

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