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.

翻译：我们提出科拉利,这是大规模巴伊西亚不确定性量化和随机优化的开放源码框架,它依靠对复杂的多物理模型进行非侵入性抽样,能够利用这些模型进行优化和决策;此外,其分布式取样引擎有效利用了大规模平行建筑,同时引入了新的过失容忍度和负载平衡机制;我们通过将科拉利与现有的高性能软件,如Aphros、Lamps(基于CPU)和Mirheo(基于GPU)接口,展示了这些特征,并展示了可达512个CSCS Piz Daint超级计算机节点的有效扩展;最后,我们提出基准,表明科拉里比相关的最新软件框架要强。