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.
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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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Kress, J., Choi, J., Klasky, S., Churchill, M., Childs, H., Pugmire, D. (2018). Binning Based Data Reduction for Vector Field Data of a Particle-In-Cell Fusion Simulation. In: Yokota, R., Weiland, M., Shalf, J., Alam, S. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science(), vol 11203. Springer, Cham. https://doi.org/10.1007/978-3-030-02465-9_15
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