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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000517584Publication status
publishedExternal links
Journal / series
Computer Methods in Applied Mechanics and EngineeringVolume
Pages / Article No.
Publisher
ElsevierSubject
High-performance computing; Bayesian uncertainty quantification; OptimizationOrganisational unit
03499 - Koumoutsakos, Petros (ehemalig) / Koumoutsakos, Petros (former)
Funding
341117 - Fluid Mechanics in Collective Behaviour: Multiscale Modelling and Applications (EC)
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