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Classical sensitivity analysis consists in running different instances of a numerical simulation with different sets of input parameters, store the results to disk, to later read them back from disk to compute the required statistics. A simulation can be multi-dimensional, multivariate, and multivalued, and a global sensitivity analysis often requires thousands of runs. The amount of storage needed can quickly become overwhelming, with the associated long read time that makes statistic computing time consuming. To avoid this pitfall, scientists usualy reduce their study size by running low resolution simulations or down-sampling output data in space and time.
Melissa bypass this limitation by avoiding intermediate file storage. Melissa processes the data in transit, enabling very large scale sensitivity analysis. Melissa is built around two key concepts: iterative statistics algorithms and asynchronous client/server model for data transfer. Simulation outputs are never stored on disc. They are sent by the simulations to a parallel server, which aggregate them to the statistic fields in an iterative fashion, and then throw them away. This allows to compute statistics maps on every mesh element for every timestep on a full scale study (ubiquitous statistics).
Melissa is a file avoiding, adaptive, fault tolerant and elastic framework, enabling very efficient executions on large scale supercomputers.
Melissa comes with iterative algorithms for computing the average, variance and co-variance, skewness, kurtosis, max, min, threshold exceedance, quantiles and Sobol' indices, and can easily be extended with new algorithms.