A Parallel Dynamic Asynchronous Framework for Uncertainty Quantification by Hierarchical Monte Carlo Algorithms

Abstract

The necessity of dealing with uncertainties is growing in many different fields of science and engineering. Due to the constant development of computational capabilities, current solvers must satisfy both statistical accuracy and computational efficiency. The aim of this work is to introduce an asynchronous framework for Monte Carlo and Multilevel Monte Carlo methods to achieve such a result. The proposed approach presents the same reliability of state of the art techniques, and aims at improving the computational efficiency by adding a new level of parallelism with respect to existing algorithms: between batches, where each batch owns its hierarchy and is independent from the others. Two different numerical problems are considered and solved in a supercomputer to show the behavior of the proposed approach.

PyCOMPSs as an Instrument for Translational Computer Science

“…A critical paradigm to support was the relaxation of global synchronizations required between simulations belonging to different levels of the MLMC algorithm. 12 In addition, the support for MPI tasks was very naive at that time and required extensions to support more complex data layouts.…”

On the use of ensemble averaging techniques to accelerate the Uncertainty Quantification of CFD predictions in wind engineering

“…The computational efficiency of the joint use of XMC, Kratos and PyCOMPSs has already been demonstrated, and optimal strong scalability was ensured up to 128 nodes (6144 central processing units (CPUs)) (Tosi et al, 2021a).…”