Multi-run numerical simulations using supercomputers are increasingly used by physicists and engineers for dealing with input data and model uncertainties. Most of the time, the input parameters of a simulation are modeled as random variables, then simulations are run a (possibly large) number of times with input parameters varied according to a specific design of experiments. Uncertainty quantification for numerical simulations is a hard computational problem, currently bounded by the large size of the produced results. This book chapter is about using in situ techniques to enable large scale uncertainty quantification studies. We provide a comprehensive description of Melissa, a file avoiding, adaptive, fault-tolerant, and elastic framework that computes in transit statistical quantities of interest. Melissa currently implements the on-the-fly computation of the statistics necessary for the realization of large scale uncertainty quantification studies: moment-based statistics (mean, standard deviation, higher orders), quantiles, Sobol' indices, and threshold exceedance.
In situ processing proposes to reduce storage needs and I/O traffic by processing results of parallel simulations as soon as they are available in the memory of the compute processes. We focus here on computing in situ statistics on the results of N simulations from a parametric study. The classical approach consists in running various instances of the same simulation with different values of input parameters. Results are then saved to disks and statistics are computed post mortem, leading to very I/O intensive applications. Our solution is to develop Melissa, an in situ library running on staging nodes as a parallel server. When starting, simulations connect to Melissa and send the results of each time step to Melissa as soon as they are available. Melissa implements iterative versions of classical statistical operations, enabling to update results as soon as a new time step from a simulation is available. Once all statistics ar updated, the time step can be discarded. We also discuss two different approaches for scheduling simulation runs: the jobs-in-job and the multi-jobs approaches. Experiments run instances of the Computational Fluid Dynamics Open Source solver Code_Saturne. They confirm that our approach enables one to avoid storing simulation results to disk or in memory.
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