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Embedded Systems for Real-Time Multimedia (ESTIMedia)

作者： Lars Schor Andreas Tretter Tobias Scherer Lothar Thiele ETH Zurich Computer Engineering and Networks Laboratory Zurich Switzerland

Programming heterogeneous systems has been greatly simplified by OpenCL, which provides a common low-level API for a large variety of compute devices. However, many low-level details, including data transfer, task scheduling, or synchronization, must still be managed by the application designer. Often, it is desirable to program heterogeneous systems in a higher-level language, making the developing process faster and less error-prone. In this paper, we introduce a framework to efficiently execute applications specified as synchronous dataflow graphs (SDF) on heterogeneous systems by means of OpenCL. In our approach, actors are embedded into OpenCL kernels and data channels are automatically instantiated to improve memory access latencies and end-to-end performance. The multi-level parallelism resulting from the hierarchical structure of heterogeneous systems is exploited by applying two techniques. Pipeline and task parallelism are used to distribute the application to the different compute devices and data-parallelism is used to concurrently process independent actor firings or even output tokens in a SIMD fashion. We demonstrate that the proposed framework can be used by application designers to efficiently exploit the parallelism of heterogeneous systems without writing low-level architecture dependent code.

关键词： parallel processing Kernel graphics processing units Hardware Programming Random access memory Ports (Computers)

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Visual data Analysis as an Integral Part of Environmental Management

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2012年第12期18卷 2088-2094页

作者： Meyer, Joerg Bethel, E. Wes Horsman, Jennifer L. Hubbard, Susan S. Krishnan, Harinarayan Romosan, Alexandru Keating, Elizabeth H. Monroe, Laura Strelitz, Richard Moore, Phil Taylor, Glenn Torkian, Ben Johnson, Timothy C. Gorton, Ian Lawrence Berkeley Natl Lab Berkeley CA USA Los Alamos Natl Lab Los Alamos NM 87545 USA Pacific NW Natl Lab Richland WA 99352 USA

The U.S. Department of Energy's (DOE) Office of Environmental Management (DOE/EM) currently supports an effort to understand and predict the fate of nuclear contaminants and their transport in natural and engineered systems. Geologists, hydrologists, physicists and computer scientists are working together to create models of existing nuclear waste sites, to simulate their behavior and to extrapolate it into the future. We use visualization as an integral part in each step of this process. In the first step, visualization is used to verify model setup and to estimate critical parameters. High-performance computing simulations of contaminant transport produces massive amounts of data, which is then analyzed using visualization software specifically designed for parallel processing of large amounts of structured and unstructured data. Finally, simulation results are validated by comparing simulation results to measured current and historical field data. We describe in this article how visual analysis is used as an integral part of the decision-making process in the planning of ongoing and future treatment options for the contaminated nuclear waste sites. Lessons learned from visually analyzing our large-scale simulation runs will also have an impact on deciding on treatment measures for other contaminated sites.

关键词： Visual analytics high-performance computing data management parallel rendering environmental management

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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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Hybrid parallelism for Volume Rendering on large-, Multi-, and Many- Core Systems

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2012年第1期18卷 17-29页

作者： Howison, Mark Bethel, E. Wes Childs, Hank Brown Univ Ctr Computat & Visualizat Providence RI 02912 USA Univ Calif Berkeley Lawrence Berkeley Lab Visualizat Grp Berkeley CA 94720 USA

With the computing industry trending toward multi- and many- core processors, we study how a standard visualization algorithm, raycasting volume rendering, can benefit from a hybrid parallelism approach. Hybrid parallelism provides the best of both worlds: using distributed-memory parallelism across a large numbers of nodes increases available FLOPs and memory, while exploiting shared-memory parallelism among the cores within each node ensures that each node performs its portion of the larger calculation as efficiently as possible. We demonstrate results from weak and strong scaling studies, at levels of concurrency ranging up to 216,000, and with data sets as large as 12.2 trillion cells. The greatest benefit from hybrid parallelism lies in the communication portion of the algorithm, the dominant cost at higher levels of concurrency. We show that reducing the number of participants with a hybrid approach significantly improves performance.

关键词： Volume visualization parallel processing

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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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Meta parallel coordinates for visualizing features in large, high-dimensional, time-varying data

Meta parallel coordinates for visualizing features in large,...

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

作者： Dasgupta, Aritra Kosara, Robert Gosink, Luke UNC Charlotte United States Pacific Northwest National Lab. United States

ISBN: (纸本)9781467347334

Managing computational complexity and designing effective visual representations are two important challenges for the visualization of large, complex, high-dimensional datasets. parallel coordinates are an effective technique for visualizing high-dimensional data, but do not scale well to very large datasets. The addition of the temporal dimension leads to more uncertainty due to clutter on screen. Consequently, this poses a significant challenge for visually finding trends and patterns that maximize insight about the underlying time-varying properties of the data. To address these problems, we present meta parallel coordinates, a parallel coordinates display that is guided by perceptually motivated visual metrics. These metrics describe the visual structures typically found in parallel coordinates and thus aid the user's analysis by providing meaningful views of the data. Since they are computed in screen space, our metrics are computationally more efficient than data-based metrics. Our choice of metrics is driven by the different analytical tasks that a user typically wants to perform with time-varying multivariate data. In particular, we have worked with domain scientists who performed simulations of bioremediation experiments, and use their data and results to demonstrate the usefulness of our approach. © 2012 ieee.

关键词： Bioremediation

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parallel stream surface computation for large data sets

Parallel stream surface computation for large data sets

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

作者： Camp, David Childs, Hank Garth, Christoph Pugmire, David Joy, Kenneth I. Lawrence Berkeley National Laboratory University of California Davis CA United States University of Kaiserslautern Germany Oak Ridge National Laboratory United States University of California Davis CA United States

ISBN: (纸本)9781467347334

parallel stream surface calculation, while highly related to other particle advection-based techniques such as streamlines, has its own unique characteristics that merit independent study. Specifically, stream surfaces require new integral curves to be added continuously during execution to ensure surface quality and accuracy;performance can be improved by specifically accounting for these additional particles. We present an algorithm for generating stream surfaces in a distributed-memory parallel setting. The algorithm incorporates multiple schemes for parallelizing particle advection and we study which schemes work best. Further, we explore speculative calculation and how it can improve overall performance. In total, this study informs the efficient calculation of stream surfaces in parallel for large data sets, based on existing integral curve functionality. © 2012 ieee.

关键词： parallel programming

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Query-driven parallel exploration of large datasets

Query-driven parallel exploration of large datasets

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

作者： Atanasov, Atanas Srinivasan, Madhusudhanan Weinzierl, Tobias Technische Universität München Germany King Abdullah University of Science and Technology Germany

ISBN: (纸本)9781467347334

Recent advances in supercomputing capabilities pose a multi-faceted data retrieval challenge to the exploration and visualisation of the obtained results: the bandwidth between visualisation devices and the high-performance computing (HPC) clusters neither scales with the simulation data nor with the compute power, the total memory footprint of the data on the supercomputer often exceeds the aggregate memory on the visualisation, and the data has to be distributed among several visualisation nodes working in parallel to render a visual. In the present paper, we introduce an on-demand data exploration paradigm that leverages HPC capabilities and distributed visualisation without requiring a large memory footprint on the visualisation cluster. Regions of interest within the data are specified by the user in the form of queries. These queries, augmented by node identifiers on the visualisation cluster, are automatically distributed among multiple compute nodes of the HPC cluster. The compute nodes work in parallel to assemble and merge data in response to the user query until the data distribution matches the visualisation cluster's topology. Query results are then simultaneously streamed to the right visualisation nodes. Our approach allows for interactive exploration of data residing on HPC resources, irrespective of memory footprint. The streaming of data to the visualisation nodes scales with the bandwidth of the interconnecting network and the HPC cluster's domain decomposition, while the latter is hidden from the visualisation and can change dynamically. We demonstrate the capability of our query-driven approach with a turbulent mixing dataset, and show that it supports interactive data exploration on HPC systems. © 2012 ieee.

关键词： visualization

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EG PGV 2012 - 12th Eurographics symposium on parallel graphics and visualization

EG PGV 2012 - 12th Eurographics Symposium on Parallel Graphi...

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12th Eurographics symposium on parallel graphics and visualization, EG PGV 2012

ISBN: (纸本)9783905674354

The proceedings contain 15 papers. The topics discussed include: HyperFlow: a heterogeneous dataflow architecture;PISTON: a portable cross-platform framework for data-parallel visualization operators;explicit cache management for volume ray-casting on parallel architectures;GLuRay: ray tracing in scientific visualization applications using OpenGL interception;a study of ray tracing large-scale scientific data in parallel visualization applications;dynamic scheduling for large-scale distributed-memory ray tracing;fast collision culling in large-scale environments using GPU mapping function;light propagation maps on parallel graphics architectures;polygonization of implicit surfaces on multi-core architectures with SIMD instructions;load-balanced multi-GPU ambient occlusion for direct volume rendering;parallel rendering on hybrid multi-GPU clusters;and auto splats: dynamic point cloud visualization on the GPU.

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A flexible Java GPU-enhanced visualization framework and its applications

A flexible Java GPU-enhanced visualization framework and its...

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25th International symposium on Computer-Based Medical Systems (CBMS)

作者： Cheng, Ruida Senseney, Justin Pandya, Nishith McCreedy, Evan McAuliffe, Matthew J. Bokinsky, Alexandra Natl Inst Hlth Imaging Sci Lab Ctr Informat Technol Bethesda MD 20892 USA Geometr Tools Inc Chapel Hill NC USA

ISBN: (纸本)9781467320511

A flexible biomedical visualization framework implemented with Java, OpenGL, and OpenCL performs efficient volume rendering with large, multi-modal datasets. The,framework takes advantage of the parallel processing power on modern graphics hardware with novel Open CL and GLSL shading language implementations. The Java and GPU environment provide portable advanced biomedical image visualization applications. Several applications built on top of the GPU framework are also presented to show the extensibility of the application. These include multi-surface rendering, stereoscopic rendering, image fusion, and diffusion tensor visualization.

关键词： graphics processing unit

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