Abstract
In this paper, an adjoint solver for the multigrid-in-time software library XBraid is presented. XBraid provides a non-intrusive approach for simulating unsteady dynamics on multiple processors while parallelizing not only in space but also in the time domain (XBraid: Parallel multigrid in time, http://llnl.gov/casc/xbraid). It applies an iterative multigrid reduction in time algorithm to existing spatially parallel classical time propagators and computes the unsteady solution parallel in time. Techniques from Automatic Differentiation are used to develop a consistent discrete adjoint solver which provides sensitivity information of output quantities with respect to design parameter changes. The adjoint code runs backwards through the primal XBraid actions and accumulates gradient information parallel in time. It is highly non-intrusive as existing adjoint time propagators can easily be integrated through the adjoint interface. The adjoint code is validated on advection-dominated flow with periodic upstream boundary condition. It provides similar strong scaling results as the primal XBraid solver and offers great potential for speeding up the overall computational costs for sensitivity analysis using multiple processors.
Similar content being viewed by others
Notes
In this work, the application of \(\Phi ^i_{\rho }\) represents the approximate inversion of an operator (implicit scheme), but in principle, explicit schemes may be considered as well.
References
Albring, T., Dick, T., Gauger, N.R.: Assessment of the recursive projection method for the stabilization of discrete adjoint solvers. In: 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA Aviation (2017)
Barker, A.T., Stoll, M.: Domain decomposition in time for PDE-constrained optimization. Comput. Phys. Commun. 197, 136–143 (2015)
Beyer, H.G., Sendhoff, B.: Robust optimization—a comprehensive survey. Comput. Methods Appl. Mech. Eng. 196(33), 3190–3218 (2007)
Blazek, J.: Computational Fluid Dynamics: Principles and Applications, 2nd edn. Elsevier Ltd., New York (2005)
Bosse, T., Gauger, N.R., Griewank, A., Günther, S., Schulz, V.: One-shot approaches to design optimzation. In: Leugering, G., Benner, P., Engell, S., Griewank, A., Harbrecht, H., Hinze, M., Rannacher, R., Ulbrich, S. (eds.) Trends in PDE Constrained Optimization, pp. 43–66. Springer, Berlin (2014)
Brandt, A.: Multi-level adaptive solutions to boundary-value problems. Math. Comput. 31(138), 333–390 (1977)
CoDiPack—Code Differentiation Package (version 1.0). http://www.scicomp.uni-kl.de/software/codi/ (2017). Accessed 1 May 2017
Du, X., Sarkis, M., Schaerer, C., Szyld, D.B.: Inexact and truncated parareal-in-time krylov subspace methods for parabolic optimal control problems. ETNA 40, 36–57 (2013)
Economon, T., Palacios, F., Alonso, J.: Unsteady aerodynamic design on unstructured meshes with sliding interfaces. In: 51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, p. 632 (2013)
Emmett, M., Minion, M.: Toward an efficient parallel in time method for partial differential equations. Commun. Appl. Math. Comput. Sci. 7(1), 105–132 (2012)
Falgout, R., Friedhoff, S., Kolev, T.V., MacLachlan, S., Schroder, J., Vandewalle, S.: Multigrid methods with space-time concurrency. SIAM J. Sci. Comput. 18(4–5), 123–143 (2015)
Falgout, R.D., Friedhoff, S., Kolev, T.V., MacLachlan, S.P., Schroder, J.B.: Parallel time integration with multigrid. SIAM J. Sci. Comput. 36(6), C635–C661 (2014)
Falgout, R.D., Katz, A., Kolev, T.V., Schroder, J.B., Wissink, A., Yang, U.M.: Parallel time integration with multigrid reduction for a compressible fluid dynamics application. Tech. Rep. LLNL-JRNL-663416, Lawrence Livermore National Laboratory (2015)
Falgout, R.D., Manteuffel, T.A., O’Neill, B., Schroder, J.B.: Multigrid reduction in time for nonlinear parabolic problems: a case study. SIAM J. Sci. Comput. 39(5), S298–S322 (2017)
Farhat, C., Chandesris, M.: Time-decomposed parallel time-integrators: theory and feasibility studies for fluid, structure, and fluid-structure applications. Int. J. Numer. Meth. Eng. 58, 1397–1434 (2003)
Ferziger, J., Peric, M.: Computational Methods for Fluid Dynamics, 3rd edn. Springer, Berlin (2002)
Fischer, P., Hecht, F., Maday, Y.: A parareal in time semi-implicit approximation of the Navier–Stokes equations. In: Proceedings of the Fifteenth International Conference on Domain Decomposition Methods, pp. 433–440. Springer (2005)
Gander, M.J.: 50 years of time parallel time integration. In: Carraro, T., Geiger, M., Krkel, S., Rannacher, R. (eds.) Multiple Shooting and Time Domain Decomposition, pp. 69–114. Springer, Berlin (2015)
Gander, M.J., Kwok, F.: Schwarz methods for the time-parallel solution of parabolic control problems. In: Domain Decomposition Methods in Computational Science and Engineering XXII, Lecture Notes in Computational Science and Engineering, vol. 104, pp. 207–216. Springer (2016)
Gander, M.J., Vandewalle, S.: Analysis of the parareal time-parallel time-integration method. SIAM J. Sci. Comput. 29(2), 556–578 (2007)
Gauger, N., Griewank, A., Hamdi, A., Kratzenstein, C., Özkaya, E., Slawig, T.: Automated extension of fixed point PDE solvers for optimal design with bounded retardation. In: Leugering, G., Engell, S., Griewank, A., Hinze, M., Rannacher, R., Schulz, V., Ulbrich, M., Ulbrich, S. (eds.) Constrained Optimization and Optimal Control for Partial Differential Equations, pp. 99–122. Springer, Basel (2012)
Giles, M., Pierce, N., Giles, M., Pierce, N.: Adjoint equations in CFD: duality, boundary conditions and solution behaviour. In: 13th Computational Fluid Dynamics Conference, p. 1850 (1997)
Giles, M.B., Pierce, N.A.: An introduction to the adjoint approach to design. Flow Turbul. Combust. 65(3), 393–415 (2000)
Giles, M.B., Süli, E.: Adjoint methods for PDEs: a posteriori error analysis and postprocessing by duality. Acta Numer. 11, 145–236 (2002)
Götschel, S., Minion, M.: Parallel-in-time for parabolic optimal control problems using pfasst. Tech. Rep. 17-51, ZIB (2017)
Griewank, A.: Projected hessians for preconditioning in one-step one-shot design optimization. In: Pillo, G., Roma, M. (eds.) Large-Scale Nonlinear Optimization, pp. 151–171. Springer, Berlin (2006)
Griewank, A., Faure, C.: Reduced functions, gradients and hessians from fixed-point iterations for state equations. Numer. Algorithms 30, 113–139 (2002)
Griewank, A., Ponomarenko, A.: Time-lag in derivative convergence for fixed point iterations. In: Proceedings of CARI’04, 7th African Conference on Research in Computer Science, pp. 295–304 (2004)
Griewank, A., Walther, A.: Evaluating derivatives, 2nd edn. Society for Industrial and Applied Mathematics (2008)
Günther, S., Gauger, N.R., Wang, Q.: Simultaneous single-step one-shot optimization with unsteady pdes. J. Comput. Appl. Math. 294, 12–22 (2016)
Heinkenschloss, M.: A time-domain decomposition iterative method for the solution of distributed linear quadratic optimal control problems. J. Comput. Appl. Math. 173(1), 169–198 (2005)
Jameson, A.: Aerodynamic design via control theory. J. Sci. Comput. 3(3), 233–260 (1988)
Jameson, A.: Time dependent calculations using multigrid, with applications to unsteady flows past airfoils and wings. In: Proceedings of 10th Computational Fluid Dynamics Conference, Honolulu, USA, June 24-26, AIAA-Paper 91-1596 (1991)
Josuttis, N.M.: The C++ Standard Library: A Tutorial and Reference. Addison-Wesley, Boston (2012)
Kanamaru, T.: Van der pol oscillator. Scholarpedia 2(1), 2202 (2007)
Krause, R., Ruprecht, D.: Hybrid space-time parallel solution of Burgers equation. In: Sassi, T., Halpern, L., Pichot, G., Widlund, O.B. (eds.) Domain Decomposition Methods in Science and Engineering XXI, pp. 647–655. Springer, Berlin (2014)
Kwok, F.: On the time-domain decomposition of parabolic optimal control problems. In: Lee, C.-O., Cai, X.-C., Hansford, V., Kim, H.H., Klawonn, A., Park, E.-J., Widlund, O.B. (eds.) Domain Decomposition Methods in Science and Engineering XXIII, pp. 55–67. Springer, Berlin (2017)
Korrektur der Referenzangabe: Lions, J.L.: Optimal control of systems governed by partial differential equations (Grundlehren der Mathematischen Wissenschaften), vol. 170. Springer, Berlin (1971)
Lions, J.L., Maday, Y., Turinici, G.: Résolution d’EDP par un schéma en temps pararéel. C. R. Acad. Sci. Paris Sér. I Math. 332, 661–668 (2001)
Mathew, T.P., Sarkis, M., Schaerer, C.E.: Analysis of block parareal preconditioners for parabolic optimal control problems. SIAM J. Sci. Comput. 32(3), 1180–1200 (2010)
Mavriplis, D.: Solution of the unsteady discrete adjoint for three-dimensional problems on dynamically deforming unstructured meshes. In: 46th AIAA Aerospace Sciences Meeting and Exhibit, p. 727 (2008)
Mohammadi, B., Pironneau, O.: Applied Shape Optimization for Fluids. Oxford University Press, Oxford (2010)
Nadarajah, S.K., Jameson, A.: Optimum shape design for unsteady flows with time-accurate continuous and discrete adjoint method. AIAA J. 45(7), 1478–1491 (2007)
Naumann, U.: The art of differentiating computer programs: an introduction to algorithmic differentiation. Environments, and Tools. Society for Industrial and Applied Mathematics, Software (2012)
Navon, I.: Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography. Dyn. Atmos. Oceans 27(1), 55–79 (1998)
Nielsen, E.J., Diskin, B., Yamaleev, N.K.: Discrete adjoint-based design optimization of unsteady turbulent flows on dynamic unstructured grids. AIAA J. 48(6), 1195–1206 (2010)
Nievergelt, J.: Parallel methods for integrating ordinary differential equations. Commun. ACM 7, 731–733 (1964)
Pierce, N.A., Giles, M.B.: Adjoint recovery of superconvergent functionals from PDE approximations. SIAM Rev. 42(2), 247–264 (2000)
Pironneau, O.: On optimum design in fluid mechanics. J. Fluid Mech. 64(1), 97–110 (1974)
Ries, M., Trottenberg, U.: MGR-ein blitzschneller elliptischer löser. Tech. Rep. Preprint 277 SFB 72, Universität Bonn (1979)
Ries, M., Trottenberg, U., Winter, G.: A note on MGR methods. Linear Algebra Appl. 49, 1–26 (1983)
Rumpfkeil, M., Zingg, D.: A general framework for the optimal control of unsteady flows with applications. In: 45th AIAA Aerospace Sciences Meeting and Exhibit, p. 1128 (2007)
Ruprecht, D., Krause, R.: Explicit parallel-in-time integration of a linear acoustic-advection system. Comput. Fluids 59, 72–83 (2012)
Shroff, G.M., Keller, H.B.: Stabilization of unstable procedures: the recursive projection method. SIAM J. Numer. Anal. 30(4), 1099–1120 (1993)
Steiner, J., Ruprecht, D., Speck, R., Krause, R.: Convergence of parareal for the Navier-Stokes equations depending on the Reynolds number. In: Abdulle, A., Deparis, S., Kressner, D., Nobile, F., Picasso, M. (eds.) Numerical Mathematics and Advanced Applications—ENUMATH 2013: Proceedings of ENUMATH 2013, the 10th European Conference on Numerical Mathematics and Advanced Applications, Lausanne, August 2013, pp. 195–202. Springer International Publishing, Cham (2015)
Trottenberg, U., Oosterlee, C., Schüller, A.: Multigrid. Academic Press, San Diego (2001)
Tucker, P.: Unsteady Computational Fluid Dynamics in Aeronautics. Springer, Berlin (2014)
Dobrev, V.A., Kolev, T., Petersson, N.A., Schroder, J.B.: Two-level convergence theory for multigrid reduction in time (MGRIT). SIAM SIAM J. Sci. Comput. 39(5), S501–S527 (2017)
Venditti, D.A., Darmofal, D.L.: Grid adaptation for functional outputs: application to two-dimensional inviscid flows. J. Comput. Phys. 176(1), 40–69 (2002)
XBraid: Parallel multigrid in time. http://llnl.gov/casc/xbraid. Accessed 1 Feb 2017
Acknowledgements
The authors thanks Max Sagebaum (SciComp, TU Kaiserslautern) and Johannes Lotz (STCE, RWTH Aachen University) who provided insight and expertise on the implementation of AD.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52–07NA27344, LLNL-JRNL-730159.
Rights and permissions
About this article
Cite this article
Günther, S., Gauger, N.R. & Schroder, J.B. A non-intrusive parallel-in-time adjoint solver with the XBraid library. Comput. Visual Sci. 19, 85–95 (2018). https://doi.org/10.1007/s00791-018-0300-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00791-018-0300-7