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 language | English |
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Title of host publication | High Performance Computing - ISC High Performance 2018 International Workshops, Revised Selected Papers |
Editors | John Shalf, Sadaf Alam, Rio Yokota, Michèle Weiland |
Publisher | Springer Verlag |
Pages | 215-229 |
Number of pages | 15 |
ISBN (Print) | 9783030024642 |
DOIs | |
State | Published - 2018 |
Event | International Conference on High Performance Computing, ISC High Performance 2018 - Frankfurt, Germany Duration: Jun 28 2018 → Jun 28 2018 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11203 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on High Performance Computing, ISC High Performance 2018 |
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Country/Territory | Germany |
City | Frankfurt |
Period | 06/28/18 → 06/28/18 |
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