Interactive visual exploration of a trillion particles

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....

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Veröffentlicht in:2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) S. 56 - 64
Hauptverfasser: Schatz, Karsten, Muller, Christoph, Krone, Michael, Schneider, Jens, Reina, Guido, Ertl, Thomas
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2016
Schlagworte:
Computational modeling
Context
Data models
Data visualization
Hardware
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling - Object hierarchies
J.2 [Computer Applications]: Physical Sciences and Engineering - Astronomy
Rendering (computer graphics)
Visualization
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Zusammenfassung: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.
DOI:10.1109/LDAV.2016.7874310