  The energy consumption and the compute performance of a fluid dynamic code
have been investigated varying parallelization approach, arithmetic precision
and clock speed. The code is based on a Lattice Boltzmann approximation, is
written in Fortran and was executed on high-end GPUs of Leonardo Booster
supercomputer. Tests were conducted on single server nodes (up to 4 GPUs in
parallel). Performance metrics like the number of operations per second and
energy consumption are reported, to quantify how smart coding approach and
system adjustment can contribute to reduction of energy footprint while keeping
the scientific throughput almost unaltered or with acceptable level of
degradation. Results indicate that this application can be executed with 20% of
energy saving and reduced thermal stress, at the cost of 5% more computing
time. The paper presents preliminary conclusions, as it is a first step of a
larger study dedicated to energy efficiency at scale.
