Abstract
This paper illustrates how GPU computing can be used to accelerate computational fluid dynamics (CFD) simulations. For sparse linear systems arising from finite volume discretization, we evaluate and optimize the performance of Conjugate Gradient (CG) routines designed for manycore accelerators and compare against an industrial CPU-based implementation. We also investigate how the recent advances in preconditioning, such as iterative Incomplete Cholesky (IC, as symmetric case of ILU) preconditioning, match the requirements for solving real world problems.
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Acknowledgements
This work was funded by the contract P02220 between Université Paris-Sud and EDF. We are grateful to Karl Rupp (TU Wien) for his support in using the ViennaCL library.
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Anzt, H. et al. (2017). Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations. In: Dutra, I., Camacho, R., Barbosa, J., Marques, O. (eds) High Performance Computing for Computational Science – VECPAR 2016. VECPAR 2016. Lecture Notes in Computer Science(), vol 10150. Springer, Cham. https://doi.org/10.1007/978-3-319-61982-8_5
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DOI: https://doi.org/10.1007/978-3-319-61982-8_5
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