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.

我们提出了 Korali，这是一个用于大规模贝叶斯不确定性量化和随机优化的开源框架。该框架依赖于复杂多物理场模型的非侵入式采样，并使其能够用于优化和决策。此外，其分布式采样引擎有效利用了大规模并行架构，同时引入了新颖的容错和负载平衡机制。我们通过将 Korali 与现有的高性能软件（例如Aphros、 LAMMPS（基于 CPU）和Mirheo）连接来展示这些功能（基于 GPU）并显示 CSCS Piz Daint 超级计算机多达 512 个节点的高效扩展。最后，我们展示了一些基准测试，证明 Korali 的性能优于相关的最先进的软件框架。