  High performance calculation is increasingly used within society. Previously
reserved for an elite, based on large computing and storage infrastructures, it
is now a core module for many companies. Indeed, high performance calculation
makes it possible to design and optimize many elements for a limited cost
,compared to the production of prototypes or tests in situ. It is also widely
used in big data and artificial intelligence.It seems essential to ask about
theenvironmental impact of these digital practices. A number of actions have
already been initiated in this community: GREEN500; European CoC
eco-responsibility label for Data centres... but these actions generally look
at specific or even idealised situations and/or software.The software
qualification process in the field of high performance calculation consists in
looking at the scalability of the software. The originality of this study is to
focus on energy scalability (calculation return time depending on the power
consumed), by considering several architectures (three TOP500 machines and a
laboratory cluster).The energy cost of an example calculation could be
estimated, which shows that the most efficient machine in terms of calculation
time is not necessarily the most energy efficient, and depending on the number
of cores/processes chosen, it is not always the same architecture that is the
most energy-efficient. It was therefore possible to show that the longer the
user is prepared to wait, the less energy is used by the calculation.
