论文标题
Korali：用于贝叶斯不确定性定量和随机优化的高效且可扩展的软件框架
Korali: Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization
论文作者
论文摘要
我们提出了Korali，这是一个用于大规模贝叶斯不确定性定量和随机优化的开源框架。该框架依赖于复杂多物理模型的非侵入性采样，并可以利用其优化和决策。此外，其分布式采样引擎可以有效利用大量并行架构，同时引入了新型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（基于CPU）和Mirheo（基于GPU）（基于GPU）的现有高性能软件进行连接来证明这些功能，并显示出多达512个CSCS PIZ DAINT SUPERCOUPTER的高效缩放。最后，我们提出的基准表明，科拉利的表现优于与最先进的软件框架相关。
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.
