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Binning Based Data Reduction for Vector Field Data of a Particle-In-Cell Fusion Simulation

  • James KressEmail author
  • Jong Choi
  • Scott Klasky
  • Michael Churchill
  • Hank Childs
  • David Pugmire
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11203)

Abstract

With this work, we explore the feasibility of using in situ data binning techniques to achieve significant data reductions for particle data, and study the associated errors for several post-hoc analysis techniques. We perform an application study in collaboration with fusion simulation scientists on data sets up to 489 GB per time step. We consider multiple ways to carry out the binning, and determine which techniques work the best for this simulation. With the best techniques we demonstrate reduction factors as large as 109x with low error percentage.

Keywords

In situ Data reduction Visualization 

Notes

Acknowledgements

This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • James Kress
    • 1
    • 2
    Email author
  • Jong Choi
    • 1
  • Scott Klasky
    • 1
  • Michael Churchill
    • 3
  • Hank Childs
    • 2
  • David Pugmire
    • 1
  1. 1.Oak Ridge National LaboratoryOak RidgeUSA
  2. 2.University of OregonEugeneUSA
  3. 3.Princeton Plasma Physics LaboratoryPrincetonUSA

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