[petsc-users] Hypre BoomerAMG has slow convergence
Robert Annewandter
robert.annewandter at opengosim.com
Tue Jul 4 13:40:45 CDT 2017
Thank you Barry!
I believe the sub problem can be singular, because the first
preconditioner M1 in the CPR-AMG preconditioner
Mcpr^(-1) = M2^(-1) [ I - J M1^(-1) ] + M1^(-1),
where
M1^(-1) = C [ W^T J C ]^(-1) W^T,
has prolongation C and restriction W^T with size 3N x N resp. N x 3N
(and 3 # of primary variables).
Would that make sense?
On 04/07/17 18:24, Barry Smith wrote:
> The large preconditioned norm relative to the true residual norm is often a sign that the preconditioner is not happy.
>
> 0 KSP preconditioned resid norm 2.495457360562e+08 true resid norm 9.213492769259e-01 ||r(i)||/||b|| 1.000000000000e+00
>
> Is there any chance this subproblem is singular?
>
> Run with -flow_sub_0_galerkin_ksp_view_mat binary -flow_sub_0_galerkin_ksp_view_rhs binary for 18 stages. This will save the matrices and the right hand sides for the 18 systems passed to hypre in a file called binaryoutput. Email the file to petsc-maint at mcs.anl.gov
>
> Barry
>
>
>> On Jul 4, 2017, at 11:59 AM, Robert Annewandter <robert.annewandter at opengosim.com> wrote:
>>
>> Hi all,
>>
>>
>> I'm working on a CPR-AMG Two-Stage preconditioner implemented as multiplicative PCComposite with outer FGMRES, where the first PC is Hypre AMG (PCGalerkin + KSPRichardson + PCHYPRE) and the second stage is Block Jacobi with LU. The pde's describe two-phase subsurface flow, and I kept the problem small at 8000 x 8000 dofs.
>>
>> The first stage is hard-wired because of the PCGalerkin part and the second stage Block Jacobi is configured via command line (with pflotran prefix flow_):
>>
>> -flow_sub_1_pc_type bjacobi \
>> -flow_sub_1_sub_pc_type lu \
>>
>> With this configuration I see occasionally that Hypre struggles to converge fast:
>>
>>
>> Step 16
>>
>> 0 2r: 3.95E-03 2x: 0.00E+00 2u: 0.00E+00 ir: 2.53E-03 iu: 0.00E+00 rsn: 0
>> Residual norms for flow_ solve.
>> 0 KSP unpreconditioned resid norm 3.945216988332e-03 true resid norm 3.945216988332e-03 ||r(i)||/||b|| 1.000000000000e+00
>> Residual norms for flow_sub_0_galerkin_ solve.
>> 0 KSP preconditioned resid norm 2.495457360562e+08 true resid norm 9.213492769259e-01 ||r(i)||/||b|| 1.000000000000e+00
>> 1 KSP preconditioned resid norm 3.900401635809e+07 true resid norm 1.211813734614e-01 ||r(i)||/||b|| 1.315259874797e-01
>> 2 KSP preconditioned resid norm 7.264015944695e+06 true resid norm 2.127154159346e-02 ||r(i)||/||b|| 2.308738078618e-02
>> 3 KSP preconditioned resid norm 1.523934370189e+06 true resid norm 4.507204888834e-03 ||r(i)||/||b|| 4.891961172285e-03
>> 4 KSP preconditioned resid norm 3.456355485206e+05 true resid norm 1.017486337883e-03 ||r(i)||/||b|| 1.104343774250e-03
>> 5 KSP preconditioned resid norm 8.215494701640e+04 true resid norm 2.386758602821e-04 ||r(i)||/||b|| 2.590503582729e-04
>> 6 KSP preconditioned resid norm 2.006221595869e+04 true resid norm 5.806707975375e-05 ||r(i)||/||b|| 6.302395975986e-05
>> 7 KSP preconditioned resid norm 4.975749682114e+03 true resid norm 1.457831681999e-05 ||r(i)||/||b|| 1.582279075383e-05
>> 8 KSP preconditioned resid norm 1.245359749620e+03 true resid norm 3.746721600730e-06 ||r(i)||/||b|| 4.066559441204e-06
>> 9 KSP preconditioned resid norm 3.134373137075e+02 true resid norm 9.784665277082e-07 ||r(i)||/||b|| 1.061993048904e-06
>> 10 KSP preconditioned resid norm 7.917076489741e+01 true resid norm 2.582765351245e-07 ||r(i)||/||b|| 2.803242392356e-07
>> 11 KSP preconditioned resid norm 2.004702594193e+01 true resid norm 6.867609287185e-08 ||r(i)||/||b|| 7.453860831257e-08
>> 1 KSP unpreconditioned resid norm 3.022346103074e-11 true resid norm 3.022346103592e-11 ||r(i)||/||b|| 7.660785484121e-09
>> 1 2r: 2.87E-04 2x: 3.70E+09 2u: 3.36E+02 ir: 1.67E-04 iu: 2.19E+01 rsn: stol
>> Nonlinear flow_ solve converged due to CONVERGED_SNORM_RELATIVE iterations 1
>>
>>
>> Step 17
>>
>> 0 2r: 3.85E-03 2x: 0.00E+00 2u: 0.00E+00 ir: 2.69E-03 iu: 0.00E+00 rsn: 0
>> Residual norms for flow_ solve.
>> 0 KSP unpreconditioned resid norm 3.846677237838e-03 true resid norm 3.846677237838e-03 ||r(i)||/||b|| 1.000000000000e+00
>> Residual norms for flow_sub_0_galerkin_ solve.
>> 0 KSP preconditioned resid norm 8.359592959751e+07 true resid norm 8.919381920269e-01 ||r(i)||/||b|| 1.000000000000e+00
>> 1 KSP preconditioned resid norm 2.046474217608e+07 true resid norm 1.356172589724e+00 ||r(i)||/||b|| 1.520478214574e+00
>> 2 KSP preconditioned resid norm 5.534610937223e+06 true resid norm 1.361527715124e+00 ||r(i)||/||b|| 1.526482134406e+00
>> 3 KSP preconditioned resid norm 1.642592089665e+06 true resid norm 1.359990274368e+00 ||r(i)||/||b|| 1.524758426677e+00
>> 4 KSP preconditioned resid norm 6.869446528993e+05 true resid norm 1.357740694885e+00 ||r(i)||/||b|| 1.522236301823e+00
>> 5 KSP preconditioned resid norm 5.245968674991e+05 true resid norm 1.355364470917e+00 ||r(i)||/||b|| 1.519572189007e+00
>> 6 KSP preconditioned resid norm 5.042030663187e+05 true resid norm 1.352962944308e+00 ||r(i)||/||b|| 1.516879708036e+00
>> 7 KSP preconditioned resid norm 5.007302249221e+05 true resid norm 1.350558656878e+00 ||r(i)||/||b|| 1.514184131760e+00
>> 8 KSP preconditioned resid norm 4.994105316949e+05 true resid norm 1.348156961110e+00 ||r(i)||/||b|| 1.511491461137e+00
>> 9 KSP preconditioned resid norm 4.984373051647e+05 true resid norm 1.345759135434e+00 ||r(i)||/||b|| 1.508803129481e+00
>> 10 KSP preconditioned resid norm 4.975323739321e+05 true resid norm 1.343365479502e+00 ||r(i)||/||b|| 1.506119472750e+00
>> 11 KSP preconditioned resid norm 4.966432959339e+05 true resid norm 1.340976058673e+00 ||r(i)||/||b|| 1.503440564224e+00
>> [...]
>> 193 KSP preconditioned resid norm 3.591931201817e+05 true resid norm 9.698521332569e-01 ||r(i)||/||b|| 1.087353520599e+00
>> 194 KSP preconditioned resid norm 3.585542278288e+05 true resid norm 9.681270691497e-01 ||r(i)||/||b|| 1.085419458213e+00
>> 195 KSP preconditioned resid norm 3.579164717745e+05 true resid norm 9.664050733935e-01 ||r(i)||/||b|| 1.083488835922e+00
>> 196 KSP preconditioned resid norm 3.572798501551e+05 true resid norm 9.646861405301e-01 ||r(i)||/||b|| 1.081561647605e+00
>> 197 KSP preconditioned resid norm 3.566443608646e+05 true resid norm 9.629702651108e-01 ||r(i)||/||b|| 1.079637887153e+00
>> 198 KSP preconditioned resid norm 3.560100018703e+05 true resid norm 9.612574416991e-01 ||r(i)||/||b|| 1.077717548471e+00
>> 199 KSP preconditioned resid norm 3.553767713002e+05 true resid norm 9.595476648643e-01 ||r(i)||/||b|| 1.075800625471e+00
>> 200 KSP preconditioned resid norm 3.547446669197e+05 true resid norm 9.578409291897e-01 ||r(i)||/||b|| 1.073887112080e+00
>> 1 KSP unpreconditioned resid norm 3.816569407795e-11 true resid norm 3.816569407353e-11 ||r(i)||/||b|| 9.921730291825e-09
>> 1 2r: 2.74E-02 2x: 3.70E+09 2u: 1.23E+02 ir: 1.99E-02 iu: 2.71E+01 rsn: stol
>> Nonlinear flow_ solve converged due to CONVERGED_SNORM_RELATIVE iterations 1
>>
>> Step 18
>>
>> 0 2r: 2.73E-02 2x: 0.00E+00 2u: 0.00E+00 ir: 2.02E-02 iu: 0.00E+00 rsn: 0
>> Residual norms for flow_ solve.
>> 0 KSP unpreconditioned resid norm 2.734891161446e-02 true resid norm 2.734891161446e-02 ||r(i)||/||b|| 1.000000000000e+00
>> Residual norms for flow_sub_0_galerkin_ solve.
>> 0 KSP preconditioned resid norm 3.550345478098e+07 true resid norm 1.048585361984e+00 ||r(i)||/||b|| 1.000000000000e+00
>> 1 KSP preconditioned resid norm 6.139218831613e+06 true resid norm 1.797822962324e-02 ||r(i)||/||b|| 1.714522276873e-02
>> 2 KSP preconditioned resid norm 1.301871956838e+06 true resid norm 3.761355992926e-03 ||r(i)||/||b|| 3.587076578878e-03
>> 3 KSP preconditioned resid norm 3.070518418113e+05 true resid norm 9.283056182563e-04 ||r(i)||/||b|| 8.852933217570e-04
>> 4 KSP preconditioned resid norm 7.639640178912e+04 true resid norm 2.348078927331e-04 ||r(i)||/||b|| 2.239282572941e-04
>> 5 KSP preconditioned resid norm 1.953032767966e+04 true resid norm 5.930230662989e-05 ||r(i)||/||b|| 5.655458180124e-05
>> 6 KSP preconditioned resid norm 5.066937883132e+03 true resid norm 1.497534370201e-05 ||r(i)||/||b|| 1.428147315892e-05
>> 7 KSP preconditioned resid norm 1.326441080568e+03 true resid norm 3.793872760594e-06 ||r(i)||/||b|| 3.618086708188e-06
>> 8 KSP preconditioned resid norm 3.494353490063e+02 true resid norm 9.659536247849e-07 ||r(i)||/||b|| 9.211969380896e-07
>> 9 KSP preconditioned resid norm 9.251497983280e+01 true resid norm 2.472922526467e-07 ||r(i)||/||b|| 2.358341644011e-07
>> 10 KSP preconditioned resid norm 2.459917675189e+01 true resid norm 6.364691902290e-08 ||r(i)||/||b|| 6.069789006257e-08
>> 11 KSP preconditioned resid norm 6.566117552226e+00 true resid norm 1.646205416458e-08 ||r(i)||/||b|| 1.569929808426e-08
>> 12 KSP preconditioned resid norm 1.758927386308e+00 true resid norm 4.277033775892e-09 ||r(i)||/||b|| 4.078860845245e-09
>> 1 KSP unpreconditioned resid norm 2.831146511164e-10 true resid norm 2.831146511142e-10 ||r(i)||/||b|| 1.035195312725e-08
>> 1 2r: 1.31E-02 2x: 3.70E+09 2u: 3.66E+02 ir: 9.77E-03 iu: 6.03E+01 rsn: stol
>> Nonlinear flow_ solve converged due to CONVERGED_SNORM_RELATIVE iterations 1
>>
>>
>>
>> SNES_view:
>>
>>
>> SNES Object: (flow_) 2 MPI processes
>> type: newtonls
>> maximum iterations=8, maximum function evaluations=10000
>> tolerances: relative=1e-05, absolute=1e-05, solution=1e-05
>> total number of linear solver iterations=1
>> total number of function evaluations=2
>> norm schedule ALWAYS
>> SNESLineSearch Object: (flow_) 2 MPI processes
>> type: basic
>> maxstep=1.000000e+08, minlambda=1.000000e-05
>> tolerances: relative=1.000000e-08, absolute=1.000000e-15, lambda=1.000000e-08
>> maximum iterations=40
>> using user-defined precheck step
>> KSP Object: (flow_) 2 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=200, initial guess is zero
>> tolerances: relative=1e-07, absolute=1e-50, divergence=10000.
>> right preconditioning
>> using UNPRECONDITIONED norm type for convergence test
>> PC Object: (flow_) 2 MPI processes
>> type: composite
>> Composite PC type - MULTIPLICATIVE
>> PCs on composite preconditioner follow
>> ---------------------------------
>> PC Object: (flow_sub_0_) 2 MPI processes
>> type: galerkin
>> Galerkin PC
>> KSP on Galerkin follow
>> ---------------------------------
>> KSP Object: (flow_sub_0_galerkin_) 2 MPI processes
>> type: richardson
>> Richardson: damping factor=1.
>> maximum iterations=200, initial guess is zero
>> tolerances: relative=1e-07, absolute=1e-50, divergence=10000.
>> left preconditioning
>> using PRECONDITIONED norm type for convergence test
>> PC Object: (flow_sub_0_galerkin_) 2 MPI processes
>> type: hypre
>> HYPRE BoomerAMG preconditioning
>> HYPRE BoomerAMG: Cycle type V
>> HYPRE BoomerAMG: Maximum number of levels 25
>> HYPRE BoomerAMG: Maximum number of iterations PER hypre call 1
>> HYPRE BoomerAMG: Convergence tolerance PER hypre call 0.
>> HYPRE BoomerAMG: Threshold for strong coupling 0.25
>> HYPRE BoomerAMG: Interpolation truncation factor 0.
>> HYPRE BoomerAMG: Interpolation: max elements per row 0
>> HYPRE BoomerAMG: Number of levels of aggressive coarsening 0
>> HYPRE BoomerAMG: Number of paths for aggressive coarsening 1
>> HYPRE BoomerAMG: Maximum row sums 0.9
>> HYPRE BoomerAMG: Sweeps down 1
>> HYPRE BoomerAMG: Sweeps up 1
>> HYPRE BoomerAMG: Sweeps on coarse 1
>> HYPRE BoomerAMG: Relax down symmetric-SOR/Jacobi
>> HYPRE BoomerAMG: Relax up symmetric-SOR/Jacobi
>> HYPRE BoomerAMG: Relax on coarse Gaussian-elimination
>> HYPRE BoomerAMG: Relax weight (all) 1.
>> HYPRE BoomerAMG: Outer relax weight (all) 1.
>> HYPRE BoomerAMG: Using CF-relaxation
>> HYPRE BoomerAMG: Not using more complex smoothers.
>> HYPRE BoomerAMG: Measure type local
>> HYPRE BoomerAMG: Coarsen type Falgout
>> HYPRE BoomerAMG: Interpolation type classical
>> linear system matrix = precond matrix:
>> Mat Object: 2 MPI processes
>> type: mpiaij
>> rows=8000, cols=8000
>> total: nonzeros=53600, allocated nonzeros=53600
>> total number of mallocs used during MatSetValues calls =0
>> not using I-node (on process 0) routines
>> linear system matrix = precond matrix:
>> Mat Object: (flow_) 2 MPI processes
>> type: mpibaij
>> rows=24000, cols=24000, bs=3
>> total: nonzeros=482400, allocated nonzeros=482400
>> total number of mallocs used during MatSetValues calls =0
>> PC Object: (flow_sub_1_) 2 MPI processes
>> type: bjacobi
>> block Jacobi: number of blocks = 2
>> Local solve is same for all blocks, in the following KSP and PC objects:
>> KSP Object: (flow_sub_1_sub_) 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: (flow_sub_1_sub_) 1 MPI processes
>> type: lu
>> out-of-place factorization
>> tolerance for zero pivot 2.22045e-14
>> matrix ordering: nd
>> factor fill ratio given 5., needed 18.3108
>> Factored matrix follows:
>> Mat Object: 1 MPI processes
>> type: seqbaij
>> rows=12000, cols=12000, bs=3
>> package used to perform factorization: petsc
>> total: nonzeros=4350654, allocated nonzeros=4350654
>> total number of mallocs used during MatSetValues calls =0
>> block size is 3
>> linear system matrix = precond matrix:
>> Mat Object: (flow_) 1 MPI processes
>> type: seqbaij
>> rows=12000, cols=12000, bs=3
>> total: nonzeros=237600, allocated nonzeros=237600
>> total number of mallocs used during MatSetValues calls =0
>> block size is 3
>> linear system matrix = precond matrix:
>> Mat Object: (flow_) 2 MPI processes
>> type: mpibaij
>> rows=24000, cols=24000, bs=3
>> total: nonzeros=482400, allocated nonzeros=482400
>> total number of mallocs used during MatSetValues calls =0
>> ---------------------------------
>> linear system matrix = precond matrix:
>> Mat Object: (flow_) 2 MPI processes
>> type: mpibaij
>> rows=24000, cols=24000, bs=3
>> total: nonzeros=482400, allocated nonzeros=482400
>> total number of mallocs used during MatSetValues calls =0
>>
>>
>>
>> Is there a way to improve on the AMG part? Do I have to adjust the tolerances (make the inner tighter)? Which Hypre AMG parameters are worth tuning? This problem occurs for 1 MPI process as well, and solving the problem in Standard PFLOTRAN (i.e. Block Jacobi + ILU) is without any issue.
>>
>> Grateful for any help!
>> Robert
>>
>>
>>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20170704/87f3b297/attachment.html>
More information about the petsc-users
mailing list