EuroVisShort2015

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https://diglib.eg.org/handle/10.2312/12463

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Browsing EuroVisShort2015 by Subject "Distributed/network graphics"

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    Dynamic Scheduling for Progressive Large-Scale Visualization
    (The Eurographics Association, 2015) Flatken, Markus; Berres, Anne; Merkel, Jonas; Hotz, Ingrid; Gerndt, Andreas; Hagen, Hans; E. Bertini and J. Kennedy and E. Puppo
    The ever-increasing compute capacity of high-performance systems enables scientists to simulate physical phenomena with a high spatial and temporal accuracy. Thus, the simulation output can yield dataset sizes of many terabytes. An efficient analysis and visualization process becomes very difficult especially for explorative scenarios where users continuously change input parameters. Using a distributed rendering pipeline may relieve the visualization frontend considerably but is often not sufficient. Therefore, we additionally propose a progressive data streaming and rendering approach. The main contribution of our method is the importance-guided order of data processing for block structured datasets. This requires a dynamic scheduling of data chunks on the parallel post-processing system which has been implemented by using an R-Tree. In this paper, we demonstrate the efficiency of our implementation for view-dependent feature extraction with varying viewpoints.
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    Exploratory Performance Analysis and Tuning of Parallel Interactive Volume Visualization on Large Displays
    (The Eurographics Association, 2015) Panagiotidis, Alexandros; Frey, Steffen; Ertl, Thomas; E. Bertini and J. Kennedy and E. Puppo
    We present an exploratory approach to performance analysis and tuning of interactive parallel volume visualization for large displays. While traditional approaches target non-interactive applications and focus on separate specialized views for post-mortem performance analysis, we show metrics from the GPU and volume ray casting together with the volume visualization and allow users to interact with both of them simultaneously. With this, users can explore the data set together with the corresponding metrics to investigate both the visual and the performance impact of different parameter settings jointly, like camera position, sampling density, or acceleration technique. In particular, this supports parameter tuning by providing the user not only with timings and quality measures, but also internal metrics from the GPU and the ray caster that help to understand the connection between parameter settings and their induced outcome. We demonstrate the usage and utility of our approach for performance analysis and tuning at the example of distributed volume ray casting for a high-resolution powerwall with the goal to achieve interactive frame rates with the best possible image quality.