  Thanks to the computational power of modern cluster machines, numerical
simulations can provide, with an unprecedented level of details, new insights
into fluid mechanics. However, taking full advantage of this hardware remains
challenging since data communication remains a significant bottleneck to
reaching peak performances. Reducing floating point precision is a simple and
effective way to reduce data movement and improve the computational speed of
most applications. Nevertheless, special care needs to be taken to ensure the
quality and convergence of computed solutions, especially when dealing with
complex fluid simulations. In this work, we analyse the impact of reduced
(single and mixed compared to double) precision on computational performance
and accuracy for computational fluid dynamics. Using the open source library
OpenFOAM, we consider incompressible, compressible, and multiphase fluid
solvers for testing on relevant benchmarks for flows in the laminar and
turbulent regime and in the presence of shock waves. Computational gain and
changes in the scalability of applications in reduced precision are also
discussed. In particular, an ad hoc theoretical model for the strong scaling
allows us to interpret and understand the observed behaviors, as a function of
floating point precision and hardware specifics. Finally, we show how reduced
precision can significantly speed up a hybrid CPU-GPU implementation, made
available to OpenFOAM end-users recently, that simply relies on a GPU linear
algebra solver developed by hardware vendors.
