Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization

SM Martin and D Waelchli and G Arampatzis and AE Economides and P Karnakov and P Koumoutsakos, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 389, 114264 (2022).

DOI: 10.1016/j.cma.2021.114264

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. (C)& nbsp;2021 Elsevier B.V. All rights reserved.

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