Treating uncertainties in models is essential in many fields of science and engineering. Uncertainty quantification (UQ) on complex and computationally costly numerical models necessitates a combination of efficient model solvers, advanced UQ methods and HPC-scale resources. The resulting technical complexities as well as lack of separation of concerns between UQ and model experts is holding back many interesting UQ applications. The aim of this paper is to close the gap between advanced UQ methods and advanced models by removing the hurdle of complex software stack integration, which in turn will offer a straightforward way to scale even prototype-grade UQ applications to high-performance resources. We achieve this goal by introducing a parallel software architecture based on UM-Bridge, a universal interface for linking UQ and models. We present three realistic applications from different areas of science and engineering, scaling from single machines to large clusters on the Google Cloud Platform.

翻译：在众多科学与工程领域中，处理模型中的不确定性至关重要。对于复杂且计算成本高昂的数值模型，不确定性量化（UQ）需要高效模型求解器、先进UQ方法以及高性能计算（HPC）级资源的结合。由此产生的技术复杂性以及UQ与模型专家之间缺乏关注点分离，阻碍了许多有趣UQ应用的发展。本文旨在通过消除复杂软件栈集成的障碍，弥合先进UQ方法与先进模型之间的差距，从而为将即使是原型级别的UQ应用扩展至高计算性能资源提供直接途径。我们通过引入基于UM-Bridge的并行软件架构来实现这一目标，UM-Bridge是一种用于连接UQ与模型的通用接口。我们展示了来自不同科学与工程领域的三个实际应用，这些应用从单台机器扩展到Google Cloud Platform上的大型集群。