SUQ2 : Uncertainty Quantification Queries over Large Spatio-temporal Simulations

SUQ2 : Uncertainty Quantification Queries over Large Spatio-temporal Simulations

Noel Moreno Lemus, Fabio Porto, Yania M. Souto, Rafael S. Pereira, Ji Liu, Esther Pacciti, and Patrick Valduriez


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 SUQ2 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.

View Publication

Dexl Members

Fabio Porto
Rafael Silva Pereira
Yania Molina Souto