  Numerical models of complex real-world phenomena often necessitate High
Performance Computing (HPC). Uncertainties increase problem dimensionality
further and pose even greater challenges.
  We present a parallelization strategy for multilevel Markov chain Monte
Carlo, a state-of-the-art, algorithmically scalable Uncertainty Quantification
(UQ) algorithm for Bayesian inverse problems, and a new software framework
allowing for large-scale parallelism across forward model evaluations and the
UQ algorithms themselves. The main scalability challenge presents itself in the
form of strong data dependencies introduced by the MLMCMC method, prohibiting
trivial parallelization.
  Our software is released as part of the modular and open-source MIT UQ
Library (MUQ), and can easily be coupled with arbitrary user codes. We
demonstrate it using the DUNE and the ExaHyPE Engine. The latter provides a
realistic, large-scale tsunami model in which identify the source of a tsunami
from buoy-elevation data.

Mathematical Software;Numerical Analysis;Numerical Analysis