Thanks Barry. By simply replacing chebychev by richardson I get similar performance with GAMG and ML (GAMG even slightly faster):
-pc_type gamg -pc_gamg_type agg -pc_gamg_threshold 0.03 -pc_gamg_square_graph 10 -pc_gamg_sym_graph -mg_levels_ksp_type richardson -mg_levels_pc_type sor Is it still true that I need to set "-pc_gamg_sym_graph" if the matrix is asymmetric? For serial runs it doesn't seem to matter, but in parallel the PC setup hangs (after calls of PCGAMGFilterGraph()) if -pc_gamg_sym_graph is not set. David On 10/21/2017 12:10 AM, Barry Smith wrote: > David, > > GAMG picks the number of levels based on how the coarsening process etc > proceeds. You cannot hardwire it to a particular value. You can run with > -info to get more info potentially on the decisions GAMG is making. > > Barry > >> On Oct 20, 2017, at 2:06 PM, David Nolte <[email protected]> wrote: >> >> PS: I didn't realize at first, it looks as if the -pc_mg_levels 3 option >> was not taken into account: >> type: gamg >> MG: type is MULTIPLICATIVE, levels=1 cycles=v >> >> >> >> On 10/20/2017 03:32 PM, David Nolte wrote: >>> Dear all, >>> >>> I have some problems using GAMG as a preconditioner for (F)GMRES. >>> Background: I am solving the incompressible, unsteady Navier-Stokes >>> equations with a coupled mixed FEM approach, using P1/P1 elements for >>> velocity and pressure on an unstructured tetrahedron mesh with about >>> 2mio DOFs (and up to 15mio). The method is stabilized with SUPG/PSPG, >>> hence, no zeros on the diagonal of the pressure block. Time >>> discretization with semi-implicit backward Euler. The flow is a >>> convection dominated flow through a nozzle. >>> >>> So far, for this setup, I have been quite happy with a simple FGMRES/ML >>> solver for the full system (rather bruteforce, I admit, but much faster >>> than any block/Schur preconditioners I tried): >>> >>> -ksp_converged_reason >>> -ksp_monitor_true_residual >>> -ksp_type fgmres >>> -ksp_rtol 1.0e-6 >>> -ksp_initial_guess_nonzero >>> >>> -pc_type ml >>> -pc_ml_Threshold 0.03 >>> -pc_ml_maxNlevels 3 >>> >>> This setup converges in ~100 iterations (see below the ksp_view output) >>> to rtol: >>> >>> 119 KSP unpreconditioned resid norm 4.004030812027e-05 true resid norm >>> 4.004030812037e-05 ||r(i)||/||b|| 1.621791251517e-06 >>> 120 KSP unpreconditioned resid norm 3.256863709982e-05 true resid norm >>> 3.256863709982e-05 ||r(i)||/||b|| 1.319158947617e-06 >>> 121 KSP unpreconditioned resid norm 2.751959681502e-05 true resid norm >>> 2.751959681503e-05 ||r(i)||/||b|| 1.114652795021e-06 >>> 122 KSP unpreconditioned resid norm 2.420611122789e-05 true resid norm >>> 2.420611122788e-05 ||r(i)||/||b|| 9.804434897105e-07 >>> >>> >>> Now I'd like to try GAMG instead of ML. However, I don't know how to set >>> it up to get similar performance. >>> The obvious/naive >>> >>> -pc_type gamg >>> -pc_gamg_type agg >>> >>> # with and without >>> -pc_gamg_threshold 0.03 >>> -pc_mg_levels 3 >>> >>> converges very slowly on 1 proc and much worse on 8 (~200k dofs per >>> proc), for instance: >>> np = 1: >>> 980 KSP unpreconditioned resid norm 1.065009356215e-02 true resid norm >>> 1.065009356215e-02 ||r(i)||/||b|| 4.532259705508e-04 >>> 981 KSP unpreconditioned resid norm 1.064978578182e-02 true resid norm >>> 1.064978578182e-02 ||r(i)||/||b|| 4.532128726342e-04 >>> 982 KSP unpreconditioned resid norm 1.064956706598e-02 true resid norm >>> 1.064956706598e-02 ||r(i)||/||b|| 4.532035649508e-04 >>> >>> np = 8: >>> 980 KSP unpreconditioned resid norm 3.179946748495e-02 true resid norm >>> 3.179946748495e-02 ||r(i)||/||b|| 1.353259896710e-03 >>> 981 KSP unpreconditioned resid norm 3.179946748317e-02 true resid norm >>> 3.179946748317e-02 ||r(i)||/||b|| 1.353259896634e-03 >>> 982 KSP unpreconditioned resid norm 3.179946748317e-02 true resid norm >>> 3.179946748317e-02 ||r(i)||/||b|| 1.353259896634e-03 >>> >>> A very high threshold seems to improve the GAMG PC, for instance with >>> 0.75 I get convergence to rtol=1e-6 after 744 iterations. >>> What else should I try? >>> >>> I would very much appreciate any advice on configuring GAMG and >>> differences w.r.t ML to be taken into account (not a multigrid expert >>> though). >>> >>> Thanks, best wishes >>> David >>> >>> >>> ------ >>> ksp_view for -pc_type gamg -pc_gamg_threshold 0.75 -pc_mg_levels 3 >>> >>> KSP Object: 1 MPI processes >>> type: fgmres >>> GMRES: restart=30, using Classical (unmodified) Gram-Schmidt >>> Orthogonalization with no iterative refinement >>> GMRES: happy breakdown tolerance 1e-30 >>> maximum iterations=10000 >>> tolerances: relative=1e-06, absolute=1e-50, divergence=10000. >>> right preconditioning >>> using nonzero initial guess >>> using UNPRECONDITIONED norm type for convergence test >>> PC Object: 1 MPI processes >>> type: gamg >>> MG: type is MULTIPLICATIVE, levels=1 cycles=v >>> Cycles per PCApply=1 >>> Using Galerkin computed coarse grid matrices >>> GAMG specific options >>> Threshold for dropping small values from graph 0.75 >>> AGG specific options >>> Symmetric graph false >>> Coarse grid solver -- level ------------------------------- >>> KSP Object: (mg_levels_0_) 1 MPI processes >>> type: preonly >>> maximum iterations=2, initial guess is zero >>> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >>> left preconditioning >>> using NONE norm type for convergence test >>> PC Object: (mg_levels_0_) 1 MPI processes >>> type: sor >>> SOR: type = local_symmetric, iterations = 1, local iterations = >>> 1, omega = 1. >>> linear system matrix = precond matrix: >>> Mat Object: 1 MPI processes >>> type: seqaij >>> rows=1745224, cols=1745224 >>> total: nonzeros=99452608, allocated nonzeros=99452608 >>> total number of mallocs used during MatSetValues calls =0 >>> using I-node routines: found 1037847 nodes, limit used is 5 >>> linear system matrix = precond matrix: >>> Mat Object: 1 MPI processes >>> type: seqaij >>> rows=1745224, cols=1745224 >>> total: nonzeros=99452608, allocated nonzeros=99452608 >>> total number of mallocs used during MatSetValues calls =0 >>> using I-node routines: found 1037847 nodes, limit used is 5 >>> >>> >>> ------ >>> ksp_view for -pc_type ml: >>> >>> KSP Object: 8 MPI processes >>> type: fgmres >>> GMRES: restart=30, using Classical (unmodified) Gram-Schmidt >>> Orthogonalization with no iterative refinement >>> GMRES: happy breakdown tolerance 1e-30 >>> maximum iterations=10000 >>> tolerances: relative=1e-06, absolute=1e-50, divergence=10000. >>> right preconditioning >>> using nonzero initial guess >>> using UNPRECONDITIONED norm type for convergence test >>> PC Object: 8 MPI processes >>> type: ml >>> MG: type is MULTIPLICATIVE, levels=3 cycles=v >>> Cycles per PCApply=1 >>> Using Galerkin computed coarse grid matrices >>> Coarse grid solver -- level ------------------------------- >>> KSP Object: (mg_coarse_) 8 MPI processes >>> type: preonly >>> maximum iterations=10000, initial guess is zero >>> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >>> left preconditioning >>> using NONE norm type for convergence test >>> PC Object: (mg_coarse_) 8 MPI processes >>> type: redundant >>> Redundant preconditioner: First (color=0) of 8 PCs follows >>> KSP Object: (mg_coarse_redundant_) 1 MPI processes >>> type: preonly >>> maximum iterations=10000, initial guess is zero >>> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >>> left preconditioning >>> using NONE norm type for convergence test >>> PC Object: (mg_coarse_redundant_) 1 MPI processes >>> type: lu >>> LU: out-of-place factorization >>> tolerance for zero pivot 2.22045e-14 >>> using diagonal shift on blocks to prevent zero pivot [INBLOCKS] >>> matrix ordering: nd >>> factor fill ratio given 5., needed 10.4795 >>> Factored matrix follows: >>> Mat Object: 1 MPI processes >>> type: seqaij >>> rows=6822, cols=6822 >>> package used to perform factorization: petsc >>> total: nonzeros=9575688, allocated nonzeros=9575688 >>> total number of mallocs used during MatSetValues calls =0 >>> not using I-node routines >>> linear system matrix = precond matrix: >>> Mat Object: 1 MPI processes >>> type: seqaij >>> rows=6822, cols=6822 >>> total: nonzeros=913758, allocated nonzeros=913758 >>> total number of mallocs used during MatSetValues calls =0 >>> not using I-node routines >>> linear system matrix = precond matrix: >>> Mat Object: 8 MPI processes >>> type: mpiaij >>> rows=6822, cols=6822 >>> total: nonzeros=913758, allocated nonzeros=913758 >>> total number of mallocs used during MatSetValues calls =0 >>> not using I-node (on process 0) routines >>> Down solver (pre-smoother) on level 1 ------------------------------- >>> KSP Object: (mg_levels_1_) 8 MPI processes >>> type: richardson >>> Richardson: damping factor=1. >>> maximum iterations=2 >>> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >>> left preconditioning >>> using nonzero initial guess >>> using NONE norm type for convergence test >>> PC Object: (mg_levels_1_) 8 MPI processes >>> type: sor >>> SOR: type = local_symmetric, iterations = 1, local iterations = >>> 1, omega = 1. >>> linear system matrix = precond matrix: >>> Mat Object: 8 MPI processes >>> type: mpiaij >>> rows=67087, cols=67087 >>> total: nonzeros=9722749, allocated nonzeros=9722749 >>> total number of mallocs used during MatSetValues calls =0 >>> not using I-node (on process 0) routines >>> Up solver (post-smoother) same as down solver (pre-smoother) >>> Down solver (pre-smoother) on level 2 ------------------------------- >>> KSP Object: (mg_levels_2_) 8 MPI processes >>> type: richardson >>> Richardson: damping factor=1. >>> maximum iterations=2 >>> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >>> left preconditioning >>> using nonzero initial guess >>> using NONE norm type for convergence test >>> PC Object: (mg_levels_2_) 8 MPI processes >>> type: sor >>> SOR: type = local_symmetric, iterations = 1, local iterations = >>> 1, omega = 1. >>> linear system matrix = precond matrix: >>> Mat Object: 8 MPI processes >>> type: mpiaij >>> rows=1745224, cols=1745224 >>> total: nonzeros=99452608, allocated nonzeros=99452608 >>> total number of mallocs used during MatSetValues calls =0 >>> using I-node (on process 0) routines: found 126690 nodes, >>> limit used is 5 >>> Up solver (post-smoother) same as down solver (pre-smoother) >>> linear system matrix = precond matrix: >>> Mat Object: 8 MPI processes >>> type: mpiaij >>> rows=1745224, cols=1745224 >>> total: nonzeros=99452608, allocated nonzeros=99452608 >>> total number of mallocs used during MatSetValues calls =0 >>> using I-node (on process 0) routines: found 126690 nodes, limit >>> used is 5 >>>
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