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Compression and Heuristic Caching for GPU Particle Tracing in Turbulent Vector Fields

  • Marc Treib
  • Kai Bürger
  • Jun WuEmail author
  • Rüdiger Westermann
Part of the Communications in Computer and Information Science book series (CCIS, volume 598)

Abstract

Particle tracing in fully resolved turbulent vector fields is challenging due to their extreme resolution. Since particles can move along arbitrary paths through large parts of the domain, particle integration requires access to the entire field in an unpredictable order. Thus, techniques for particle tracing in such fields require a careful design to reduce performance constraints caused by memory and communication bandwidth. One possibility to achieve this is data compression, but so far it has been considered rather hesitantly due to supposed accuracy issues. We shed light on the use of data compression for turbulent vector fields, motivated by the observation that particle traces are always afflicted with inaccuracy. We quantitatively analyze the additional inaccuracies caused by lossy compression. We propose an adaptive data compression scheme using the discrete wavelet transform and integrate it into a block-based particle tracing approach. Furthermore, we present a priority-based GPU caching scheme to reduce memory access operations. In some experiments we confirm that the compression has only minor impact on the accuracy of the trajectories, and that on a desktop system our technique can achieve comparable performance to previous approaches on supercomputers.

Keywords

Vector fields Turbulence Particle tracing Data compression Data streaming 

Notes

Acknowledgements

The work was partly funded by the European Union under the ERC Advanced Grant 291372: Safer-Vis - Uncertainty Visualization for Reliable Data Discovery. The authors want to thank Charles Meneveau from Johns Hopkins University and Tobias Pfaffelmoser from TUM for helpful discussions and constructive criticism.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marc Treib
    • 1
  • Kai Bürger
    • 1
  • Jun Wu
    • 1
    Email author
  • Rüdiger Westermann
    • 1
  1. 1.Department of Computer ScienceTechnische Universität MünchenMunichGermany

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