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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aliaga, J.I., Pérez, J., Quintana-Ortí, E.S.: Systematic fusion of CUDA kernels for iterative sparse linear system solvers. In: Träff, J.L., Hunold, S., Versaci, F. (eds.) Euro-Par 2015. LNCS, vol. 9233, pp. 675–686. Springer, Heidelberg (2015). doi:10.1007/978-3-662-48096-0_52
Aliaga, J.I., Perez, J., Quintana-Orti, E.S., Anzt, H.: Reformulated conjugate gradient for the energy-aware solution of linear systems on GPUs. In: 2013 42nd International Conference on Parallel Processing (ICPP), pp. 320–329, October 2013
Anzt, H., Chow, E., Dongarra, J.: Iterative sparse triangular solves for preconditioning. In: Träff, J.L., Hunold, S., Versaci, F. (eds.) Euro-Par 2015. LNCS, vol. 9233, pp. 650–661. Springer, Heidelberg (2015). doi:10.1007/978-3-662-48096-0_50
Anzt, H., Tomov, S., Dongarra, J.: Energy efficiency and performance frontiers for sparse computations on GPU supercomputers. In: Proceedings of the Sixth International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2015, pp. 1–10. ACM, New York (2015)
Archambeau, F., Méchitoua, N., Sakiz, M.: Code Saturne: A Finite Volume Code for the computation of turbulent incompressible flows - Industrial Applications. Int. J. Finite 1(1) (2004)
Chow, E., Anzt, H., Dongarra, J.: Asynchronous iterative algorithm for computing incomplete factorizations on GPUs. In: Kunkel, J.M., Ludwig, T. (eds.) ISC High Performance 2015. LNCS, vol. 9137, pp. 1–16. Springer, Cham (2015). doi:10.1007/978-3-319-20119-1_1
Chow, E., Patel, A.: Fine-grained parallel incomplete LU factorization. SIAM J. Sci. Comput. 37, C169–C193 (2015)
MAGMA Web page. http://icl.cs.utk.edu/magma/index.html
NVIDIA Corporation. CUDA C best practices guide. http://docs.nvidia.com/cuda/cuda-c-best-practices-guide/
NVIDIA Corporation. CUDA Toolkit Documentation v7.5, September 2015
Rupp, K., Rudolf, F., Weinbub, J.: ViennaCL - a high level linear algebra library for GPUs and multi-core CPUs. In: International Workshop on GPUs and Scientific Applications, pp. 51–56 (2010)
Saad, Y.: Iterative Methods for Sparse Linear Systems. Society for Industrial and Applied Mathematics, Philadelphia (2003)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-61982-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61981-1
Online ISBN: 978-3-319-61982-8
eBook Packages: Computer ScienceComputer Science (R0)