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    Interactive visual exploration of a trillion particles

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    Type
    Conference Paper
    Authors
    Schatz, Karsten
    Müller, Christoph
    Krone, Michael
    Schneider, Jens
    Reina, Guido
    Ertl, Thomas
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-03-10
    Online Publication Date
    2017-03-10
    Print Publication Date
    2016-10
    Permanent link to this record
    http://hdl.handle.net/10754/623212
    
    Metadata
    Show full item record
    Abstract
    We present a method for the interactive exploration of tera-scale particle data sets. Such data sets arise from molecular dynamics, particle-based fluid simulation, and astrophysics. Our visualization technique provides a focus+context view of the data that runs interactively on commodity hardware. The method is based on a hybrid multi-scale rendering architecture, which renders the context as a hierarchical density volume. Fine details in the focus are visualized using direct particle rendering. In addition, clusters like dark matter halos can be visualized as semi-transparent spheres enclosing the particles. Since the detail data is too large to be stored in main memory, our approach uses an out-of-core technique that streams data on demand. Our technique is designed to take advantage of a dual-GPU configuration, in which the workload is split between the GPUs based on the type of data. Structural features in the data are visually enhanced using advanced rendering and shading techniques. To allow users to easily identify interesting locations even in overviews, both the focus and context view use color tables to show data attributes on the respective scale. We demonstrate that our technique achieves interactive performance on a one trillionpar-ticle data set from the DarkSky simulation.
    Citation
    Schatz K, Muller C, Krone M, Schneider J, Reina G, et al. (2016) Interactive visual exploration of a trillion particles. 2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV). Available: http://dx.doi.org/10.1109/LDAV.2016.7874310.
    Sponsors
    This work was partially funded by Deutsche Forschungsgemeinschaft (DFG) as part of SFB 716. J. Schneider is funded by the Visual Computing Center, King Abdullah University of Science and Technology.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)
    DOI
    10.1109/LDAV.2016.7874310
    Additional Links
    http://ieeexplore.ieee.org/document/7874310/
    ae974a485f413a2113503eed53cd6c53
    10.1109/LDAV.2016.7874310
    Scopus Count
    Collections
    Conference Papers; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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