Laurent Lefèvre’s scientific contributions

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Publications (1)

Figure 3 -Coût énergétique versus temps de retour Un point intéressant non prévu est également ressorti de ce travail. Entre janvier et mars 2019, il y a eu une importante régression de performances sur la machine KNL du CEA/TGCC (cf. figure 1, différence de 30 à 40 % entre janvier et mars). De nombreux échanges avec le CEA ont eu lieu, mais il n'a pas étépossibleétépossible d'identifier la raison de cette évolution des performances. Plusieurs pistes ont éteé́voquées (évolutions concernant la couche MPI, mises à jour de micro-codes matériels). Lorsque l'on a repris le travail en juin 2019, les performances de janvier ont étéretrouvéesétéretrouvées. Même si aucune conclusion franche ne peut être amenée, ce résultat montre la complexitédescomplexitédes infrastructures actuelles, ainsi que l'importance du logiciel, des librairies associées, des micro-codes et de leurs paramétrages. Ces points sont rarement considérés actuellement.
High performance computing and energy efficiency: focus on OpenFOAM
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December 2019

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Laurent Lefèvre

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Gabriel Moreau

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.

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