Abstract
One of the great prospects of exascale computing is to simulate challenging highly complex multi-physics scenarios with different length and time scales. A modular approach re-using existing software for the single-physics model parts has great advantages regarding flexibility and software development costs. At the same time, it poses challenges in terms of numerical stability and parallel scalability. The coupling library preCICE provides communication, data mapping, and coupling numerics for surface-coupled multi-physics applications in a highly modular way. We recapitulate the numerical methods but focus particularly on their parallel implementation. The numerical results for an artificial coupling interface show a very small runtime of the coupling compared to typical solver runtimes and a good parallel scalability on a number of cores corresponding to a massively parallel simulation for an actual, coupled simulation. Further results for actual application scenarios from the field of fluid–structure–acoustic interactions are presented in the next chapter.
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Notes
- 1.
preCICE is licensed under LGPL3.
- 2.
- 3.
- 4.
We always refer to the number of processors per participant.
- 5.
For more details: https://www.lrz.de/services/compute/supermuc/systemdescription/.
- 6.
Optimizations of the memory requirements of IMVJ and RBF are possible and work in progress.
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Acknowledgements
The financial support of the priority program 1648 Software for Exascale Computing (www.sppexa.de) of the German Research Foundation and of the Institute for Advanced Study (www.tum-ias.de) of the Technical University of Munich as well as provided computing time on the SuperMUC at the Leibniz Supercomputing Centre, are thankfully acknowledged.
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Bungartz, HJ., Lindner, F., Mehl, M., Scheufele, K., Shukaev, A., Uekermann, B. (2016). Partitioned Fluid–Structure–Acoustics Interaction on Distributed Data: Coupling via preCICE. In: Bungartz, HJ., Neumann, P., Nagel, W. (eds) Software for Exascale Computing - SPPEXA 2013-2015. Lecture Notes in Computational Science and Engineering, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-40528-5_11
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