DescriptionQuantiles being order statistics, the classical approach for their computation requires availability of the full sample before ranking it. This approach is not suitable at exascale. Large ensembles would need to gather a prohibitively large amount of data. We propose an iterative approach based on the stochastic quantile algorithm of Robbins-Monro. We rely on the Melissa framework, a file avoiding, adaptive, fault tolerant and elastic framework in order to compute in transit ubiquitous quantiles. Quantiles are updated on-the-fly as soon as the in transit parallel server receives results from one of the running simulations. We run 80,000 fluid dynamics parallel simulations of 6M hexahedra and 100 timespteps. They were executed on up to 4800 cores, avoiding 288 TB of file storage. We produce ubiquitous spatio-temporal maps of quantiles and inter-quantile based intervals.