Korali: Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization
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.
Cite
@article{arxiv.2005.13457,
title = {Korali: Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization},
author = {Sergio M. Martin and Daniel Wälchli and Georgios Arampatzis and Athena E. Economides and Petr Karnakov and Petros Koumoutsakos},
journal= {arXiv preprint arXiv:2005.13457},
year = {2022}
}
Comments
12 pages, 12 figures