  We present Korali, an open-source framework for large-scale Bayesian
uncertainty quantification and stochastic optimization. The framework relies on
non-intrusive sampling of complex multiphysics models and enables their
exploitation for optimization and decision-making. In addition, its distributed
sampling engine makes efficient use of massively-parallel architectures while
introducing novel fault tolerance and load balancing mechanisms. We demonstrate
these features by interfacing Korali with existing high-performance software
such as Aphros, Lammps (CPU-based), and Mirheo (GPU-based) and show efficient
scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we
present benchmarks demonstrating that Korali outperforms related
state-of-the-art software frameworks.
