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ieee symposium on large data Analysis and visualization (LDAV)

作者： Fang Chen Markus Flatken Ingrid Hotz Andreas Gerndt Simulation and Software Technology German Aerospace Center (DLR)

With the increasing power of the HPC hardware systems, numerical simulations are heading towards exa-scale computing. Early inspection and analysis of on-going large simulations enables domain experts to obtain first insight into their running simulation process and intermediate results. Compared to conventional post-processing, such in-situ processing has the advantage of keeping data in memory, avoiding to store the large amount of raw data to disk, providing on-the-fly analysis, and preventing early failures in the simulation process. In this poster we present a distributed and scalable software infrastructure, which provides distributed in-situ data processing, feature extraction and interactive exploration at user's front-end. We have integrated and extended our system to multiple simulation applications, ranging from Lattice-Boltzmann blood flow simulation to grid based simulation for propulsion systems. A user-interactive front-end is integrated to our system, allowing to directly interact with the visualization of running simulations, gain insight, and make decisions.

关键词： data models Rendering (computer graphics) Feature extraction data visualization Analytical models Computational modeling Software

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Out-of-core visualization of time-varying hybrid-grid volume data

Out-of-core visualization of time-varying hybrid-grid volume...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Min Shih Yubo Zhang Kwan-Liu Ma Jayanarayanan Sitaraman Dimitri Mavriplis University of California Davis University of Wyoming

Traditional computational fluid dynamics (CFD) solvers are usually written for a single gridding paradigm such as structured-Cartesian, structured-body-fitted, or unstructured grids. Each type of mesh paradigms has inherent advantages and disadvantages. Thus, the methods of coupling multiple mesh paradigms have been developed to facilitate the use of different solvers in different part of the computational domain. However, the complex hybrid gridding paradigm poses challenges to rendering calculations for visualizing the data. This paper describes a volume visualization system for time-varying adaptive moving-body CFD datasets, where the grid system consists of unstructured grids near the body surface, coupled with Structured Adaptive Mesh Refinement (SAMR) grid in the off-body domain. We present two approaches to the hybrid-grid volume ray casting: a KD-tree based single-pass algorithm, and a multi-pass algorithm using the depth peeling technique. The system has a three-level memory hierarchy: GPU memory, main memory, and a solid state drive (SSD). Through data caching and prefetching within the memory hierarchy, the latency of time-step swapping can be hidden. Experimental results show that our system allows interactive volume exploration on single-GPU commodity PCs.

关键词： Rendering (computer graphics) graphics processing units Casting data visualization Legged locomotion Prefetching Runtime

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Application of Stochastic Point-Based Rendering to Transparent visualization of large-Scale Laser-Scanned data of 3D Cultural Assets

Application of Stochastic Point-Based Rendering to Transpare...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Satoshi Tanaka Makoto Uemura Kyoko Hasegawa Takehiko Kitagawa Takahiro Yoshida Asuka Sugiyama Hiromi T. Tanaka Atsushi Okamoto Naohisa Sakamoto Koji Koyamada Ritsumeikan University Otemae University Kyoto University

We propose a new application of stochastic point-based rendering, which was recently proposed for implicit surfaces, to large-scale laser-scanned 3D point data. Specifically, we propose a scheme to apply the rendering to transparent and fused visualization of recent large and complex laser-scanned data from cultural assets. Our scheme uses 3D points that are directly acquired using a laser scanner as the rendering primitives. For laser-scanned data that consist of more than 107 or 108 3D points, the pre-processing stage takes only a few minutes, and the rendering stage is executable at interactive frame rates. We do not encounter rendering artifacts originating from the indefiniteness of depth-sorted orders of rendering primitives. Fused visualization with various visual assistants is also possible. We demonstrate the effectiveness of our scheme by visualizing a campus building and a culturally important festival float.

关键词： Rendering (computer graphics) Three-dimensional displays data visualization Lasers visualization Buildings Cultural differences

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Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2014年第8期20卷 1114-1126页

作者： Hoellt, Thomas Magdy, Ahmed Zhan, Peng Chen, Guoning Gopalakrishnan, Ganesh Hoteit, Ibrahim Hansen, Charles D. Hadwiger, Markus King Abdullah Univ Sci & Technol Geometr Modeling & Sci Visualizat Ctr Thuwal Saudi Arabia Wireless Stars Cairo Egypt King Abdullah Univ Sci & Technol Earth Sci & Engn Dept Thuwal Saudi Arabia Univ Houston Dept Comp Sci Houston TX 77004 USA Univ Calif San Diego Scripps Inst Oceanog San Diego CA 92103 USA Univ Utah SCI Inst Salt Lake City UT 84112 USA Univ Utah Sch Comp Salt Lake City UT 84112 USA

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis. The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures. Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.

关键词： Ensemble visualization ocean visualization ocean forecast risk estimation

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Visual analysis of large dental imaging data in caries research

Visual analysis of large dental imaging data in caries resea...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Guangchen Ruan Hui Zhang School of Informatics and Computing Indiana University Pervasive Technology Institute Indiana University

With dental imaging data acquired at unprecedented speed and resolution, traditional serial image processing and single-node storage need to be re-examined in a “Bigdata” context. Furthermore, most previous dental computing has focused on the actual imaging acquisition and image analysis tools, while much less research has focused on enabling caries assessment via visual analysis of large dental imaging data. In this paper we present DENVIS, an end-to-end solution for cariologists to manage, mine, visualize, and analyze large dental imaging data for investigative carious lesion studies. DENVIS consists of two main parts: data driven image analysis modules triggered by imaging data acquisition that exploit parallel MapReduce tasks and ingest visualization archive into a distributed NoSQL store, and user driven modules that allow investigative analysis at run time. DENVIS has seen early use by our collaborators in oral health research, where our system has been used to pose and answer domain-specific questions for quantitative assessment of dynamic carious lesion activities.

关键词： Lesions Dentistry visualization Computed tomography Image segmentation Teeth

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Evaluating trace aggregation for performance visualization of large distributed systems

Evaluating trace aggregation for performance visualization o...

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ieee International symposium on Performance Analysis of Systems and Software

作者： Robin Lamarche-Perrin Lucas Mello Schnorr Jean-Marc Vincent Yves Demazeau Laboratoire d'Informatique de Grenoble Université Grenoble Alpes France Laboratoire d'Informatique de Grenoble CNRS France

Performance analysis through visualization techniques usually suffers semantic limitations due to the size of parallel applications. Most performance visualization tools rely on data aggregation to work at scale, without any attempt to evaluate the loss of information caused by such aggregations. This paper proposes a technique to evaluate the quality of aggregated representations - using measures from information theory - and to optimize such measures in order to build consistent multiresolution representations of large execution traces.

关键词： data visualization Aggregates Complexity theory Loss measurement Organizations Partitioning algorithms High performance computing

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FiberScout: An Interactive Tool for Exploring and Analyzing Fiber Reinforced Polymers

FiberScout: An Interactive Tool for Exploring and Analyzing ...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Johannes Weissenböck Artem Amirkhanov Weimin Li Andreas Reh Alexander Amirkhanov Eduard Gröller Johann Kastner Christoph Heinzl University of Applied Sciences Upper Austria Ufa State Aviation Technical University Ufa Russia Vienna University of Technology Institute of Computer Graphics and Algorithms Vienna Austria

Advanced composites such as fiber reinforced polymers are promising candidate materials for future components as they allow integrating the continuously rising demands of industry regarding cost-effectiveness, function-orientation, integration and weight. The most important structures of fiber reinforced polymers are the individual fibers, as their characteristics (stiffness, strength, ductility, durability, etc.) to a large extent determine the properties of the final component. The main contribution of this paper is the introduction of a new system for interactive exploration and visual analysis of fiber properties in X-ray computed tomography data of fiber reinforced polymers. The presented tool uses parallel coordinates to define and configure initial fiber classes. Using a scatter plot matrix linked to the parallel coordinates the initial classification may be refined. This allows to analyze hidden relationships between individual fiber properties. 2D and 3D views depict the resulting fiber classifications. By using polar plots an intuitive rendering of the fiber orientation distribution is provided. In addition, two modules of higher abstraction are proposed: The Blob visualization creates a hull around fibers with similar characteristics. The fiber metadata visualization allows to calculate overlays for 2D and 3D views containing regional information of particular material characteristics. The proposed system has been evaluated by two groups of domain experts. Applying the presented concepts the user feedback shows that the domain experts are now able to efficiently perform tasks as classification of fibers, visualization of fiber lengths and orientations, and visualization of fiber regions. The insights gained can be forwarded to the design office as well as to material development and simulation, in order to speed up the development of novel composite components.

关键词： data visualization Three-dimensional displays visualization Image color analysis Plastics Pipelines

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data-parallel halo finding with variable linking lengths

Data-parallel halo finding with variable linking lengths

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Wathsala Widanagamaachchi Peer-Timo Bremer Christopher Sewell Li-Ta Lo James Ahrens Valerio Pascuccik SCI Institute University of Utah SCI Institute Lawrence Livermore National Laboratory Los Alamos National Laboratory University of Utah Salt Lake City UT US

State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.

关键词： Joining processes Pistons Vectors Computational modeling Shape Libraries Laboratories

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A parallel, Distributed, High-Performance Architecture for Simulating Particle-Based Models

A Parallel, Distributed, High-Performance Architecture for S...

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International symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Adrian Sabou Dorian Gorgan Computer Science Department Technical University of Cluj-Napoca Cluj-Napoca Romania

Particle-based models are widespread in the field of computer graphics and are mostly used in soft-body dynamics, for simulating surfaces such as cloth, fluids and biologic tissue. As model resolution and scenario complexity increases, the computation required for these particular applications becomes overwhelming for a single processing unit, especially when interactivity is required, thus parallelization must be employed in order to provide a fast, flexible and scalable simulation environment. High-performance computing architectures such as graphics clusters may provide the parallel computing and rendering power required, but the distributed and remote nature of the computation and rendering process introduce specific challenges that must be tackled. We propose a parallel, distributed, modular system architecture for a particle-based simulator on GPU clusters, encapsulating powerful parallel and distributed processing, distributed rendering and remote interaction techniques, for flexible, fast simulation of large models and complex scenarios. For validating and evaluating the proposed architecture, we perform a visual comparison of two largely used numeric integration methods, namely the explicit Velocity Verlet and implicit Euler integration techniques.

关键词： Computational modeling Numerical models graphics processing units Computer architecture data models visualization Rendering (computer graphics)

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A MULTILEVEL parallel AND SCALABLE SINGLE-HOST GPU CLUSTER FRAMEWORK FOR large-SCALE GEOSPATIAL data PROCESSING

A MULTILEVEL PARALLEL AND SCALABLE SINGLE-HOST GPU CLUSTER F...

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ieee International Geoscience and Remote Sensing symposium

作者： Grant J. Scott Kirk Backus Derek T. Anderson University of Missouri Center for Geospatial Intelligence Columbia Missouri USA Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS USA

Geospatial data exists in a variety of formats, including rasters, vector data, and large-scale geospatial databases. There exists an ever-growing number of sensors that are collecting this data, resulting in the explosive growth and scale of high-resolution remote sensing geospatial data collections. A particularly challenging domain of geospatial data processing involves mining information from high resolution remote sensing imagery. The prevalence of high-resolution raster geospatial data collections represents a significant data challenge, as a single remote sensing image is composed of hundreds of millions of pixels. We have developed a robust application framework which exploits graphics processing unit (GPU) clusters to perform high-throughput geospatial data processing. We process geospatial raster data concurrently across tiles of large geospatial data rasters, utilizing GPU co-processors driven by CPU threads to extract refined geospatial information. The framework can produce output rasters or perform image information mining to write data into a geospatial database.

关键词： graphics processing units Geospatial analysis data processing Instruction sets Remote sensing parallel processing data mining

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