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arXiv · 2005.13457

Korali: Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization

Sergio M. Martin·Daniel Wälchli·Georgios Arampatzis·Athena E. Economides·Petr Karnakov·Petros Koumoutsakos
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Abstract

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.

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Sergio M. Martin, Daniel Wälchli, Georgios Arampatzis, Athena E. Economides, Petr Karnakov, Petros Koumoutsakos. 2020-05-27. Korali: Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization. https://doi.org/10.1016/j.cma.2021.114264

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