Lawrence Mitchell <[email protected]> writes: > Below I show output from a run on 1 process and then two (along with > ksp_view) for the following options: > > -pc_type mg -ksp_rtol 1e-8 -ksp_max_it 6 -pc_mg_levels 2 -mg_levels_pc_type > sor -ksp_monitor > > On 1 process: > 0 KSP Residual norm 5.865090856053e+02 > 1 KSP Residual norm 1.293159126247e+01 > 2 KSP Residual norm 5.181199296299e-01 > 3 KSP Residual norm 1.268870802643e-02 > 4 KSP Residual norm 5.116058930806e-04 > 5 KSP Residual norm 3.735036960550e-05 > 6 KSP Residual norm 1.755288530515e-06 > KSP Object: 1 MPI processes > type: gmres > GMRES: restart=30, using Classical (unmodified) Gram-Schmidt > Orthogonalization with no iterative refinement > GMRES: happy breakdown tolerance 1e-30 > maximum iterations=6, initial guess is zero > tolerances: relative=1e-08, absolute=1e-50, divergence=10000 > left preconditioning > using PRECONDITIONED norm type for convergence test > PC Object: 1 MPI processes > type: mg > MG: type is MULTIPLICATIVE, levels=2 cycles=v > Cycles per PCApply=1 > Not using Galerkin computed coarse grid matrices
How are you sure the rediscretized matrices are correct in parallel? I would stick with the redundant coarse solve and use -mg_levels_ksp_type chebyshev -mg_levels_pc_type jacobi -ksp_monitor_true_residual Use of Jacobi here is to make the smoother the same in parallel as serial. (Usually SOR is a bit stronger, though I think the Cheby/SOR combination is somewhat peculiar and usually overkill.) Compare convergence with and without -pc_mg_galerkin.
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