Korali: efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization. (English) Zbl 1507.62234
Summary: 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.
MSC:
| 62F15 | Bayesian inference |
| 90-04 | Software, source code, etc. for problems pertaining to operations research and mathematical programming |
| 90C15 | Stochastic programming |
Software:
muq; CMA-ES; APT-MCMC; UQLab; BCM; Adam; UQpy; GAMBIT; Aphros; Korali; EasyVVUQ; Mirheo; ABC-SysBio; Uncertainpy; Chaospy; Pi4U; QUESO; PSUADE; MultiNest; IMPALA; DAKOTA; Stan; TORC; GitHub; BioBayesReferences:
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