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SUQ$2$: Uncertainty Quantification Queries over Large Spatio-temporal Simulations

Noel Lemus 1 Fábio Porto 1 Yania Souto 1 Rafael Pereira 1 Ji Liu 2 Esther Pacitti 3 Patrick Valduriez 3
3 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The combination of high-performance computing towards Exascale power and numerical techniques enables exploring complex physical phenomena using large-scale spatio-temporal modeling and simulation. The improvements on the fidelity of phenomena simulation require more sophisticated uncertainty quantification analysis, leaving behind measurements restricted to low order statistical moments and moving towards more expressive probability density functions models of uncertainty. In this paper, we consider the problem of answering uncertainty quantification queries over large spatio-temporal simulation results. We propose the SU Q 2 method based on the Generalized Lambda Distribution (GLD) function. GLD fitting is an embarrassingly parallel process that scales linearly to the number of available cores on the number of simulation points. Furthermore, the answer of queries is entirely based on computed GLDs and the corresponding clusters, which enables trading the huge amount of simulation output data by 4 values in the GLD parametrization per simulation point. The methodology presented in this paper becomes an important ingredient in converging simulations improvements to the Exascale computational power.
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Submitted on : Friday, April 3, 2020 - 6:57:22 PM
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Noel Lemus, Fábio Porto, Yania Souto, Rafael Pereira, Ji Liu, et al.. SUQ$2$: Uncertainty Quantification Queries over Large Spatio-temporal Simulations. Bulletin of the Technical Committee on Data Engineering, IEEE Computer Society, 2020, 43 (1), pp.47-59. ⟨lirmm-02531748⟩

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