On Fri, Aug 3, 2018 at 9:42 AM Pierpaolo Minelli <pierpaolo.mine...@cnr.it> wrote:
> I tried to use these options with GAMG: > > -mg_levels_ksp_type richardson > > -mg_levels_pc_type sor > > > but also in this case i don’t obtain a convergence. > Can you send the convergence and solver view so we can check? Also, if you send the matrix we can try running it. > I used also direct solvers (MUMPS ad SuperLU) and they works fine, but > slower (solve time) then iterative method with ML and hypre. > > If i check with -ksp_monitor_true_residual, is it advisable to continue > using the iterative method? > Yes. Matt > Pierpaolo > > > Il giorno 03 ago 2018, alle ore 15:16, Mark Adams <mfad...@lbl.gov> ha > scritto: > > So this is a complex valued indefinite Helmholtz operator (very hard to > solve scalably) with axisymmetric coordinates. ML, hypre and GAMG all > performed about the same, with a big jump in residual initially and > essentially not solving it. You scaled it and this fixed ML and hypre but > not GAMG. > > From this output I can see that the eigenvalue estimates are strange. Your > equations look fine so I have to assume that the complex values are the > problem. If this is symmetric the CG is a much better solver and eigen > estimator. But this is not a big deal, especially since you have two > options that work. I would suggest not using cheby smoother, it uses these > bad eigen estimates, and it is basically not smoothing on some levels. You > can use this instead: > > -mg_levels_ksp_type richardson > -mg_levels_pc_type sor > > Note, if you have a large shift these equations are very hard to solve > iteratively and you should just use a direct solver. Direct solvers in 2D > are not bad, > > Mark > > > On Fri, Aug 3, 2018 at 3:02 AM Pierpaolo Minelli <pierpaolo.mine...@cnr.it> > wrote: > >> In this simulation I'm solving two equations in a two-dimensional domain >> (z,r) at each time step. The first is an equation derived from the Maxwell >> equation. Taking advantage of the fact that the domain is axialsymmetric >> and applying a temporal harmonic approximation, the equation I am solving >> is the following: >> >> >> The second equation is a Poisson’s equation in cylindrical coordinates >> (z,r) in the field of real numbers. >> >> This is the output obtained using these options (Note that at this moment >> of development I am only using a processor): >> >> *-pc_type gamg -pc_gamg_agg_nsmooths 1 -pc_gamg_reuse_interpolation true >> -pc_gamg_square_graph 1 -pc_gamg_threshold 0. -ksp_rtol 1.e-7 >> -ksp_max_it 30 -ksp_monitor_true_residual -info | grep GAMG* >> >> [0] PCSetUp_GAMG(): level 0) N=321201, n data rows=1, n data cols=1, >> nnz/row (ave)=5, np=1 >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 4.97011 nnz ave. (N=321201) >> [0] PCGAMGCoarsen_AGG(): Square Graph on level 1 of 1 to square >> [0] PCGAMGProlongator_AGG(): New grid 45754 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.972526e+00 >> min=3.461411e-03 PC=jacobi >> [0] PCSetUp_GAMG(): 1) N=45754, n data cols=1, nnz/row (ave)=10, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 10.7695 nnz ave. (N=45754) >> [0] PCGAMGProlongator_AGG(): New grid 7893 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=5.686837e+00 >> min=5.062501e-01 PC=jacobi >> [0] PCSetUp_GAMG(): 2) N=7893, n data cols=1, nnz/row (ave)=23, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 23.2179 nnz ave. (N=7893) >> [0] PCGAMGProlongator_AGG(): New grid 752 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.504451e+01 >> min=2.124898e-02 PC=jacobi >> [0] PCSetUp_GAMG(): 3) N=752, n data cols=1, nnz/row (ave)=30, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 30.7367 nnz ave. (N=752) >> [0] PCGAMGProlongator_AGG(): New grid 56 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=7.781296e+00 >> min=2.212257e-02 PC=jacobi >> [0] PCSetUp_GAMG(): 4) N=56, n data cols=1, nnz/row (ave)=22, 1 active pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 22.9643 nnz ave. (N=56) >> [0] PCGAMGProlongator_AGG(): New grid 6 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.525086e+00 >> min=1.375043e-01 PC=jacobi >> [0] PCSetUp_GAMG(): 5) N=6, n data cols=1, nnz/row (ave)=6, 1 active pes >> [0] PCSetUp_GAMG(): 6 levels, grid complexity = 1.43876 >> [0] PCSetUp_GAMG(): level 0) N=321201, n data rows=1, n data cols=1, >> nnz/row (ave)=5, np=1 >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 4.97011 nnz ave. (N=321201) >> [0] PCGAMGCoarsen_AGG(): Square Graph on level 1 of 1 to square >> [0] PCGAMGProlongator_AGG(): New grid 45754 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.972526e+00 >> min=3.461411e-03 PC=jacobi >> [0] PCSetUp_GAMG(): 1) N=45754, n data cols=1, nnz/row (ave)=10, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 10.7695 nnz ave. (N=45754) >> [0] PCGAMGProlongator_AGG(): New grid 7893 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=5.686837e+00 >> min=5.062501e-01 PC=jacobi >> [0] PCSetUp_GAMG(): 2) N=7893, n data cols=1, nnz/row (ave)=23, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 23.2179 nnz ave. (N=7893) >> [0] PCGAMGProlongator_AGG(): New grid 752 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.504451e+01 >> min=2.124898e-02 PC=jacobi >> [0] PCSetUp_GAMG(): 3) N=752, n data cols=1, nnz/row (ave)=30, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 30.7367 nnz ave. (N=752) >> [0] PCGAMGProlongator_AGG(): New grid 56 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=7.781296e+00 >> min=2.212257e-02 PC=jacobi >> [0] PCSetUp_GAMG(): 4) N=56, n data cols=1, nnz/row (ave)=22, 1 active pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 22.9643 nnz ave. (N=56) >> [0] PCGAMGProlongator_AGG(): New grid 6 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.525086e+00 >> min=1.375043e-01 PC=jacobi >> [0] PCSetUp_GAMG(): 5) N=6, n data cols=1, nnz/row (ave)=6, 1 active pes >> [0] PCSetUp_GAMG(): 6 levels, grid complexity = 1.43876 >> [0] PCSetUp_GAMG(): level 0) N=271201, n data rows=1, n data cols=1, >> nnz/row (ave)=5, np=1 >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 4.97455 nnz ave. (N=271201) >> [0] PCGAMGCoarsen_AGG(): Square Graph on level 1 of 1 to square >> [0] PCGAMGProlongator_AGG(): New grid 38501 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.933798e+00 >> min=4.684075e-02 PC=jacobi >> [0] PCSetUp_GAMG(): 1) N=38501, n data cols=1, nnz/row (ave)=10, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 10.7732 nnz ave. (N=38501) >> [0] PCGAMGProlongator_AGG(): New grid 6664 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.623029e+00 >> min=1.250957e-02 PC=jacobi >> [0] PCSetUp_GAMG(): 2) N=6664, n data cols=1, nnz/row (ave)=23, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 23.2098 nnz ave. (N=6664) >> [0] PCGAMGProlongator_AGG(): New grid 620 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.763329e+00 >> min=1.611776e-02 PC=jacobi >> [0] PCSetUp_GAMG(): 3) N=620, n data cols=1, nnz/row (ave)=29, 1 active >> pes >> [0] PCGAMGFilterGraph(): 100.% nnz after filtering, with threshold 0., >> 29.6129 nnz ave. (N=620) >> [0] PCGAMGProlongator_AGG(): New grid 46 nodes >> [0] PCGAMGOptProlongator_AGG(): Smooth P0: max eigen=1.497611e+00 >> min=2.630403e-02 PC=jacobi >> [0] PCSetUp_GAMG(): 4) N=46, n data cols=1, nnz/row (ave)=20, 1 active pes >> [0] PCSetUp_GAMG(): 5 levels, grid complexity = 1.43639 >> >> >> >> >> Il giorno 02 ago 2018, alle ore 17:39, Mark Adams <mfad...@lbl.gov> ha >> scritto: >> >> It looks like ML and hypre are working well now. If you want to debug >> GAMG you can run with -info, which is very noisy, and grep on GAMG or send >> the whole output. >> >> BTW, what equations are you solving? >> >> On Thu, Aug 2, 2018 at 5:12 AM Pierpaolo Minelli < >> pierpaolo.mine...@cnr.it> wrote: >> >>> Thank you very much for the correction. >>> By rebalancing the matrix coefficients, making them dimensionless, in my >>> complex problem i managed to obtain a better result with both ML and HYPRE. >>> GAMG instead seems to be unable to converge and in fact I get unexpected >>> results. I report the outputs of the three simulations again (i tried to >>> use also -pc_gamg_square_graph 20 without any improvement). >>> >>> Pierpaolo >>> >>> *-pc_type hypre -ksp_rtol 1.e-7 -ksp_monitor_true_residual* >>> >>> 0 KSP preconditioned resid norm 1.984853668904e-02 true resid norm >>> 2.979865703850e-03 ||r(i)||/||b|| 1.000000000000e+00 >>> 1 KSP preconditioned resid norm 1.924446712661e-04 true resid norm >>> 1.204260204811e-04 ||r(i)||/||b|| 4.041323752460e-02 >>> 2 KSP preconditioned resid norm 5.161509100765e-06 true resid norm >>> 2.810809726926e-06 ||r(i)||/||b|| 9.432672496933e-04 >>> 3 KSP preconditioned resid norm 9.297326931238e-08 true resid norm >>> 4.474617977876e-08 ||r(i)||/||b|| 1.501617328625e-05 >>> 4 KSP preconditioned resid norm 1.910271882670e-09 true resid norm >>> 9.637470658283e-10 ||r(i)||/||b|| 3.234196308186e-07 >>> 0 KSP preconditioned resid norm 2.157687745805e+04 true resid norm >>> 3.182001523188e+03 ||r(i)||/||b|| 1.000000000000e+00 >>> 1 KSP preconditioned resid norm 1.949268476386e+02 true resid norm >>> 1.243419788627e+02 ||r(i)||/||b|| 3.907665598415e-02 >>> 2 KSP preconditioned resid norm 5.078054475792e+00 true resid norm >>> 2.745355604400e+00 ||r(i)||/||b|| 8.627763325675e-04 >>> 3 KSP preconditioned resid norm 8.663802743529e-02 true resid norm >>> 4.254290979292e-02 ||r(i)||/||b|| 1.336985839979e-05 >>> 4 KSP preconditioned resid norm 1.795605563039e-03 true resid norm >>> 9.040507428245e-04 ||r(i)||/||b|| 2.841138623714e-07 >>> 0 KSP preconditioned resid norm 6.728304961395e+02 true resid norm >>> 1.879478105170e+02 ||r(i)||/||b|| 1.000000000000e+00 >>> 1 KSP preconditioned resid norm 2.190497539532e+01 true resid norm >>> 4.630095820203e+02 ||r(i)||/||b|| 2.463500802413e+00 >>> 2 KSP preconditioned resid norm 8.425561564252e-01 true resid norm >>> 7.012565302251e+01 ||r(i)||/||b|| 3.731123700223e-01 >>> 3 KSP preconditioned resid norm 3.029848345705e-02 true resid norm >>> 4.379018464663e+00 ||r(i)||/||b|| 2.329911932795e-02 >>> 4 KSP preconditioned resid norm 7.374025528575e-04 true resid norm >>> 1.337183702137e-01 ||r(i)||/||b|| 7.114654320570e-04 >>> 5 KSP preconditioned resid norm 3.009400175162e-05 true resid norm >>> 7.731135032616e-03 ||r(i)||/||b|| 4.113447776459e-05 >>> >>> *-pc_type ml -ksp_rtol 1.e-7 -ksp_monitor_true_residual* >>> >>> 0 KSP preconditioned resid norm 1.825767020538e-02 true resid norm >>> 2.979865703850e-03 ||r(i)||/||b|| 1.000000000000e+00 >>> 1 KSP preconditioned resid norm 6.495628259383e-04 true resid norm >>> 3.739440526742e-04 ||r(i)||/||b|| 1.254902367550e-01 >>> 2 KSP preconditioned resid norm 4.971875712015e-05 true resid norm >>> 2.118856024328e-05 ||r(i)||/||b|| 7.110575559127e-03 >>> 3 KSP preconditioned resid norm 3.726806462912e-06 true resid norm >>> 1.370355844514e-06 ||r(i)||/||b|| 4.598716790303e-04 >>> 4 KSP preconditioned resid norm 2.496898447120e-07 true resid norm >>> 9.494701893753 >>> >> <clip_image002.png><clip_image002.png> > > > -- What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead. -- Norbert Wiener https://www.cse.buffalo.edu/~knepley/ <http://www.caam.rice.edu/~mk51/>