Abstract
Based on in situ observations obtained in the context of multiphysics and multicomponent simulations of the Computational Fluid Dynamics community, parallel performances of code coupling is first discussed. Overloads due to coupling steps are then analyzed with a simple toy model. Many parameters can impact the communication times, such as the number of cores, the communication mode (synchronous or asynchronous), the global size of the exchanged fields or the amount of data per core. Results show that the respective partionning of the coupled codes as well as core distributions on the machine have an important role in exchange times and thus on the total CPU hours needed by an application. For the synchronous communications presented in this paper, two main outcomes independent from the coupler can be addressed by incorporating the knowledge of the coupling in the preprocessing step of the solvers with constraint and co-partitioning as well as process placement. Such conclusions can be directly extended to other field of applications such as climat science where coupling between ocean and atmosphere is of primary importance.
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Notes
- 1.
Projet d’Assimilation par Logiciel Multiméthodes.
- 2.
Coupling With Interpolation Parallel Interface.
- 3.
Titan: Oak Ridge National Laboratory. No. 1 system of Top500 in November 2012.
- 4.
Curie supercomputer, owned by GENCI and operated at the TGCC by CEA.
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Duchaine, F., Berger, S., Staffelbach, G., Gicquel, L. (2017). Partitioned High Performance Code Coupling Applied to CFD. In: Di Napoli, E., Hermanns, MA., Iliev, H., Lintermann, A., Peyser, A. (eds) High-Performance Scientific Computing. JHPCS 2016. Lecture Notes in Computer Science(), vol 10164. Springer, Cham. https://doi.org/10.1007/978-3-319-53862-4_1
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