  The classical approach for quantiles computation requires availability of the
full sample before ranking it. In uncertainty quantification of numerical
simulation models, this approach is not suitable at exascale as large ensembles
of simulation runs would need to gather a prohibitively large amount of data.
This problem is solved thanks to an on-the-fly and iterative approach based on
the Robbins-Monro algorithm. This approach relies on Melissa, a file avoiding,
adaptive, fault-tolerant and elastic framework. On a validation case producing
11 TB of data, which consists in 3000 fluid dynamics parallel simulations on a
6M cell mesh, it allows on-line computation of spatio-temporal maps of
percentiles.
