  Computational Fluid Dynamics (CFD) is the simulation of fluid flow undertaken
with the use of computational hardware. The underlying equations are
computationally challenging to solve and necessitate high performance computing
(HPC) to resolve in a practical timeframe when a reasonable level of fidelity
is required. The simulations are memory intensive, having previously been
limited to central processing unit (CPU) solvers, as graphics processing unit
(GPU) video random access memory (VRAM) was insufficient. However, with recent
developments in GPU design and increases to VRAM, GPU acceleration of CPU
solved workflows is now possible. At HPC scale however, many operational
details are still unknown. This paper utilizes ANSYS Fluent, a leading
commercial code in CFD, to investigate the compute speed, power consumption and
service unit (SU) cost considerations for the GPU acceleration of CFD workflows
on HPC architectures. To provide a comprehensive analysis, different CPU
architectures, and GPUs have been assessed. It is seen that GPU compute speed
is faster, however, the initialisation speed, power and cost performance is
less clear cut. Whilst the larger A100 cards perform well with respect to power
consumption, this is not observed for the V100 cards. In situations where more
than one GPU is required, their adoption may not be beneficial from a power or
cost perspective.
