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

James Kress, Jong Choi, Scott Klasky, Michael Churchill, Hank Childs, David Pugmire

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationHigh Performance Computing - ISC High Performance 2018 International Workshops, Revised Selected Papers
EditorsJohn Shalf, Sadaf Alam, Rio Yokota, Michèle Weiland
PublisherSpringer Verlag
Pages215-229
Number of pages15
ISBN (Print)9783030024642
DOIs
StatePublished - 2018
EventInternational Conference on High Performance Computing, ISC High Performance 2018 - Frankfurt, Germany
Duration: Jun 28 2018Jun 28 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11203 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on High Performance Computing, ISC High Performance 2018
Country/TerritoryGermany
CityFrankfurt
Period06/28/1806/28/18

Funding

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.

Keywords

  • Data reduction
  • In situ
  • Visualization

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