Abstract
We explore a GPU implementation of a Krylov-accelerated algebraic multigrid (AMG) algorithm with flexible preconditioning. We demonstrate by means of two benchmarks from an industrial computational fluid dynamics (CFD) application that the acceleration with multiple graphics processing units (GPUs) speeds up the solution phase by a factor of up to 13. In order to achieve good performance for the whole AMG algorithm, we propose for the setup a substitution of the double-pairwise aggregation by a simpler aggregation scheme skipping the calculation of temporary grids and operators. The version with the revised setup reduces the total computing time on multiple GPUs by further 30% compared to the GPU implementation with the double-pairwise aggregation. We observe that the GPU implementation of the entire Krylov-accelerated AMG runs up to four times faster than the fastest central processing unit (CPU) implementation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Flynn, M.: Some computer organizations and their effectiveness. IEEE Transactons on Computers C-21, 948–960 (1972)
Emans, M.: Performance of parallel AMG-preconditioners in CFD-codes for weakly compressible flows. Parallel Computing 36, 326–338 (2010)
Haase, G., Liebmann, M., Douglas, C.C., Plank, G.: A Parallel Algebraic Multigrid Solver on Graphics Processing Units. In: Zhang, W., Chen, Z., Douglas, C.C., Tong, W. (eds.) HPCA 2009. LNCS, vol. 5938, pp. 38–47. Springer, Heidelberg (2010)
Notay, Y.: An aggregation-based algebraic multigrid method. Electronic Transactions on Numerical Analysis 37, 123–146 (2010)
Vaněk, P., Mandel, J., Brezina, M.: Algebraic multigrid by smoothed aggregation for second and fourth order elliptic problems. Computing 56, 179–196 (1996)
Emans, M.: Efficient parallel AMG methods for approximate solutions of linear systems in CFD applications. SIAM Journal on Scientific Computing 32, 2235–2254 (2010)
Falgout, R., Jones, J., Yang, U.: Conceptual interfaces in hypre. Future Generation Computer Systems 22, 239–251 (2006)
Emans, M.: AMG for Linear Systems in Engine Flow Simulations. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2009, Part II. LNCS, vol. 6068, pp. 350–359. Springer, Heidelberg (2010)
Hanjalic, K., Popovac, M., Hadziabdic, M.: A robust near-wall elliptic-relaxation eddy-viscosity turbulence model for cfd. International Journal of Heat and Fluid Flow 25, 1047–1051 (2004)
Patankar, S., Spalding, D.: A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows. International Journal Heat Mass Transfer 15, 1787–1806 (1972)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Emans, M., Liebmann, M. (2013). Efficient Setup of Aggregation AMG for CFD on GPUs. In: Manninen, P., Öster, P. (eds) Applied Parallel and Scientific Computing. PARA 2012. Lecture Notes in Computer Science, vol 7782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36803-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-36803-5_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36802-8
Online ISBN: 978-3-642-36803-5
eBook Packages: Computer ScienceComputer Science (R0)