  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.
