[petsc-users] Advice on improving Stokes Schur preconditioners
Dave May
dave.mayhem23 at gmail.com
Mon Jun 12 15:52:32 CDT 2017
I've been following the discussion and have a couple of comments:
1/ For the preconditioners that you are using (Schur factorisation LDU, or
upper block triangular DU), the convergence properties (e.g. 1 iterate for
LDU and 2 iterates for DU) come from analysis involving exact inverses of
A_00 and S
Once you switch from using exact inverses of A_00 and S, you have to rely
on spectral equivalence of operators. That is fine, but the spectral
equivalence does not tell you how many iterates LDU or DU will require to
converge. What it does inform you about is that if you have a spectrally
equivalent operator for A_00 and S (Schur complement), then under mesh
refinement, your iteration count (whatever it was prior to refinement) will
not increase.
2/ Looking at your first set of options, I see you have opted to use
-fieldsplit_ksp_type preonly (for both split 0 and 1). That is nice as it
creates a linear operator thus you don't need something like FGMRES or GCR
applied to the saddle point problem.
Your choice for Schur is fine in the sense that the diagonal of M is
spectrally equivalent to M, and M is spectrally equivalent to S. Whether it
is "fine" in terms of the iteration count for Schur systems, we cannot say
apriori (since the spectral equivalence doesn't give us direct info about
the iterations we should expect).
Your preconditioner for A_00 relies on AMG producing a spectrally
equivalent operator with bounds which are tight enough to ensure
convergence of the saddle point problem. I'll try explain this.
In my experience, for many problems (unstructured FE with variable
coefficients, structured FE meshes with variable coefficients) AMG and
preonly is not a robust choice. To control the approximation (the spectral
equiv bounds), I typically run a stationary or Krylov method on split 0
(e.g. -fieldsplit_0_ksp_type xxx -fieldsplit_0_kps_rtol yyy). Since the AMG
preconditioner generated is spectrally equivalent (usually!), these solves
will converge to a chosen rtol in a constant number of iterates under
h-refinement. In practice, if I don't enforce that I hit something like
rtol=1.0e-1 (or 1.0e-2) on the 0th split, saddle point iterates will
typically increase for "hard" problems under mesh refinement (1e4-1e7
coefficient variation), and may not even converge at all when just using
-fieldsplit_0_ksp_type preonly. Failure ultimately depends on how "strong"
the preconditioner for A_00 block is (consider re-discretized geometric
multigrid versus AMG). Running an iterative solve on the 0th split lets you
control and recover from weak/poor, but spectrally equivalent
preconditioners for A_00. Note that people hate this approach as it
invariably nests Krylov methods, and subsequently adds more global
reductions. However, it is scalable, optimal, tuneable and converges faster
than the case which didn't converge at all :D
3/ I agree with Matt's comments, but I'd do a couple of other things first.
* I'd first check the discretization is implemented correctly. Your P2/P1
element is inf-sup stable - thus the condition number of S
(unpreconditioned) should be independent of the mesh resolution (h). An
easy way to verify this is to run either LDU (schur_fact_type full) or DU
(schur_fact_type upper) and monitor the iterations required for those S
solves. Use -fieldsplit_1_pc_type none -fieldsplit_1_ksp_rtol 1.0e-8
-fieldsplit_1_ksp_monitor_true_residual -fieldsplit_1_ksp_pc_right
-fieldsplit_1_ksp_type gmres -fieldsplit_0_pc_type lu
Then refine the mesh (ideally via sub-division) and repeat the experiment.
If the S iterates don't asymptote, but instead grow with each refinement -
you likely have a problem with the discretisation.
* Do the same experiment, but this time use your mass matrix as the
preconditioner for S and use -fieldsplit_1_pc_type lu. If the iterates,
compared with the previous experiments (without a Schur PC) have gone up
your mass matrix is not defined correctly. If in the previous experiment
(without a Schur PC) iterates on the S solves were bounded, but now when
preconditioned with the mass matrix the iterates go up, then your mass
matrix is definitely not correct.
4/ Lastly, to finally get to your question regarding does +400 iterates
for the solving the Schur seem "reasonable" and what is "normal behaviour"?
It seems "high" to me. However the specifics of your discretisation, mesh
topology, element quality, boundary conditions render it almost impossible
to say what should be expected. When I use a Q2-P2* discretisation on a
structured mesh with a non-constant viscosity I'd expect something like
50-60 for 1.0e-10 with a mass matrix scaled by the inverse (local)
viscosity. For constant viscosity maybe 30 iterates. I think this kind of
statement is not particularly useful or helpful though.
Given you use an unstructured tet mesh, it is possible that some elements
have very bad quality (high aspect ratio (AR), highly skewed). I am certain
that P2/P1 has an inf-sup constant which is sensitive to the element aspect
ratio (I don't recall the exact scaling wrt AR). From experience I know
that using the mass matrix as a preconditioner for Schur is not robust as
AR increases (e.g. iterations for the S solve grow). Hence, with a couple
of "bad" element in your mesh, I could imagine that you could end up having
to perform +400 iterations
5/ Lastly, definitely don't impose one Dirichlet BC on pressure to make the
pressure unique. This really screws up all the nice properties of your
matrices. Just enforce the constant null space for p. And as you noticed,
GMRES magically just does it automatically if the RHS of your original
system was consistent.
Thanks,
Dave
On 12 June 2017 at 20:20, David Nolte <dnolte at dim.uchile.cl> wrote:
> Ok. With "-pc_fieldsplit_schur_fact_type full" the outer iteration
> converges in 1 step. The problem remain the Schur iterations.
>
> I was not sure if the problem was maybe the singular pressure or the
> pressure Dirichlet BC. I tested the solver with a standard Stokes flow in a
> pipe with a constriction (zero Neumann BC for the pressure at the outlet)
> and in a 3D cavity (enclosed flow, no pressure BC or fixed at one point). I
> am not sure if I need to attach the constant pressure nullspace to the
> matrix for GMRES. Not doing so does not alter the convergence of GMRES in
> the Schur solver (nor the pressure solution), using a pressure Dirichlet BC
> however slows down convergence (I suppose because of the scaling of the
> matrix).
>
> I also checked the pressure mass matrix that I give PETSc, it looks
> correct.
>
> In all these cases, the solver behaves just as before. With LU in
> fieldsplit_0 and GMRES/LU with rtol 1e-10 in fieldsplit_1, it converges
> after 1 outer iteration, but the inner Schur solver converges slowly.
>
> How should the convergence of GMRES/LU of the Schur complement *normally*
> behave?
>
> Thanks again!
> David
>
>
>
>
> On 06/12/2017 12:41 PM, Matthew Knepley wrote:
>
> On Mon, Jun 12, 2017 at 10:36 AM, David Nolte <dnolte at dim.uchile.cl>
> wrote:
>
>>
>> On 06/12/2017 07:50 AM, Matthew Knepley wrote:
>>
>> On Sun, Jun 11, 2017 at 11:06 PM, David Nolte <dnolte at dim.uchile.cl>
>> wrote:
>>
>>> Thanks Matt, makes sense to me!
>>>
>>> I skipped direct solvers at first because for these 'real'
>>> configurations LU (mumps/superlu_dist) usally goes out of memory (got 32GB
>>> RAM). It would be reasonable to take one more step back and play with
>>> synthetic examples.
>>> I managed to run one case though with 936k dofs using: ("user" =pressure
>>> mass matrix)
>>>
>>> <...>
>>> -pc_fieldsplit_schur_fact_type upper
>>> -pc_fieldsplit_schur_precondition user
>>> -fieldsplit_0_ksp_type preonly
>>> -fieldsplit_0_pc_type lu
>>> -fieldsplit_0_pc_factor_mat_solver_package mumps
>>>
>>> -fieldsplit_1_ksp_type gmres
>>> -fieldsplit_1_ksp_monitor_true_residuals
>>> -fieldsplit_1_ksp_rtol 1e-10
>>> -fieldsplit_1_pc_type lu
>>> -fieldsplit_1_pc_factor_mat_solver_package mumps
>>>
>>> It takes 2 outer iterations, as expected. However the fieldsplit_1 solve
>>> takes very long.
>>>
>>
>> 1) It should take 1 outer iterate, not two. The problem is that your
>> Schur tolerance is way too high. Use
>>
>> -fieldsplit_1_ksp_rtol 1e-10
>>
>> or something like that. Then it will take 1 iterate.
>>
>>
>> Shouldn't it take 2 with a triangular Schur factorization and exact
>> preconditioners, and 1 with a full factorization? (cf. Benzi et al 2005,
>> p.66, http://www.mathcs.emory.edu/~benzi/Web_papers/bgl05.pdf)
>>
>> That's exactly what I set: -fieldsplit_1_ksp_rtol 1e-10 and the Schur
>> solver does drop below "rtol < 1e-10"
>>
>
> Oh, yes. Take away the upper until things are worked out.
>
> Thanks,
>
> Matt
>
>>
>> 2) There is a problem with the Schur solve. Now from the iterates
>>
>> 423 KSP preconditioned resid norm 2.638419658982e-02 true resid norm
>> 7.229653211635e-11 ||r(i)||/||b|| 7.229653211635e-11
>>
>> it is clear that the preconditioner is really screwing stuff up. For
>> testing, you can use
>>
>> -pc_fieldsplit_schur_precondition full
>>
>> and your same setup here. It should take one iterate. I think there is
>> something wrong with your
>> mass matrix.
>>
>>
>> I agree. I forgot to mention that I am considering an "enclosed flow"
>> problem, with u=0 on all the boundary and a Dirichlet condition for the
>> pressure in one point for fixing the constant pressure. Maybe the
>> preconditioner is not consistent with this setup, need to check this..
>>
>> Thanks a lot
>>
>>
>>
>> Thanks,
>>
>> Matt
>>
>>
>>> 0 KSP unpreconditioned resid norm 4.038466809302e-03 true resid norm
>>> 4.038466809302e-03 ||r(i)||/||b|| 1.000000000000e+00
>>> Residual norms for fieldsplit_1_ solve.
>>> 0 KSP preconditioned resid norm 0.000000000000e+00 true resid norm
>>> 0.000000000000e+00 ||r(i)||/||b|| -nan
>>> Linear fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0
>>> 1 KSP unpreconditioned resid norm 4.860095964831e-06 true resid norm
>>> 4.860095964831e-06 ||r(i)||/||b|| 1.203450763452e-03
>>> Residual norms for fieldsplit_1_ solve.
>>> 0 KSP preconditioned resid norm 2.965546249872e+08 true resid norm
>>> 1.000000000000e+00 ||r(i)||/||b|| 1.000000000000e+00
>>> 1 KSP preconditioned resid norm 1.347596594634e+08 true resid norm
>>> 3.599678801575e-01 ||r(i)||/||b|| 3.599678801575e-01
>>> 2 KSP preconditioned resid norm 5.913230136403e+07 true resid norm
>>> 2.364916760834e-01 ||r(i)||/||b|| 2.364916760834e-01
>>> 3 KSP preconditioned resid norm 4.629700028930e+07 true resid norm
>>> 1.984444715595e-01 ||r(i)||/||b|| 1.984444715595e-01
>>> 4 KSP preconditioned resid norm 3.804431276819e+07 true resid norm
>>> 1.747224559120e-01 ||r(i)||/||b|| 1.747224559120e-01
>>> 5 KSP preconditioned resid norm 3.178769422140e+07 true resid norm
>>> 1.402254864444e-01 ||r(i)||/||b|| 1.402254864444e-01
>>> 6 KSP preconditioned resid norm 2.648669043919e+07 true resid norm
>>> 1.191164310866e-01 ||r(i)||/||b|| 1.191164310866e-01
>>> 7 KSP preconditioned resid norm 2.203522108614e+07 true resid norm
>>> 9.690500018007e-02 ||r(i)||/||b|| 9.690500018007e-02
>>> <...>
>>> 422 KSP preconditioned resid norm 2.984888715147e-02 true resid norm
>>> 8.598401046494e-11 ||r(i)||/||b|| 8.598401046494e-11
>>> 423 KSP preconditioned resid norm 2.638419658982e-02 true resid norm
>>> 7.229653211635e-11 ||r(i)||/||b|| 7.229653211635e-11
>>> Linear fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations
>>> 423
>>> 2 KSP unpreconditioned resid norm 3.539889585599e-16 true resid norm
>>> 3.542279617063e-16 ||r(i)||/||b|| 8.771347603759e-14
>>> Linear solve converged due to CONVERGED_RTOL iterations 2
>>>
>>>
>>> Does the slow convergence of the Schur block mean that my
>>> preconditioning matrix Sp is a poor choice?
>>>
>>> Thanks,
>>> David
>>>
>>>
>>> On 06/11/2017 08:53 AM, Matthew Knepley wrote:
>>>
>>> On Sat, Jun 10, 2017 at 8:25 PM, David Nolte <dnolte at dim.uchile.cl>
>>> wrote:
>>>
>>>> Dear all,
>>>>
>>>> I am solving a Stokes problem in 3D aorta geometries, using a P2/P1
>>>> finite elements discretization on tetrahedral meshes resulting in
>>>> ~1-1.5M DOFs. Viscosity is uniform (can be adjusted arbitrarily), and
>>>> the right hand side is a function of noisy measurement data.
>>>>
>>>> In other settings of "standard" Stokes flow problems I have obtained
>>>> good convergence with an "upper" Schur complement preconditioner, using
>>>> AMG (ML or Hypre) on the velocity block and approximating the Schur
>>>> complement matrix by the diagonal of the pressure mass matrix:
>>>>
>>>> -ksp_converged_reason
>>>> -ksp_monitor_true_residual
>>>> -ksp_initial_guess_nonzero
>>>> -ksp_diagonal_scale
>>>> -ksp_diagonal_scale_fix
>>>> -ksp_type fgmres
>>>> -ksp_rtol 1.0e-8
>>>>
>>>> -pc_type fieldsplit
>>>> -pc_fieldsplit_type schur
>>>> -pc_fieldsplit_detect_saddle_point
>>>> -pc_fieldsplit_schur_fact_type upper
>>>> -pc_fieldsplit_schur_precondition user # <-- pressure mass
>>>> matrix
>>>>
>>>> -fieldsplit_0_ksp_type preonly
>>>> -fieldsplit_0_pc_type ml
>>>>
>>>> -fieldsplit_1_ksp_type preonly
>>>> -fieldsplit_1_pc_type jacobi
>>>>
>>>
>>> 1) I always recommend starting from an exact solver and backing off in
>>> small steps for optimization. Thus
>>> I would start with LU on the upper block and GMRES/LU with toelrance
>>> 1e-10 on the Schur block.
>>> This should converge in 1 iterate.
>>>
>>> 2) I don't think you want preonly on the Schur system. You might want
>>> GMRES/Jacobi to invert the mass matrix.
>>>
>>> 3) You probably want to tighten the tolerance on the Schur solve, at
>>> least to start, and then slowly let it out. The
>>> tight tolerance will show you how effective the preconditioner is
>>> using that Schur operator. Then you can start
>>> to evaluate how effective the Schur linear sovler is.
>>>
>>> Does this make sense?
>>>
>>> Thanks,
>>>
>>> Matt
>>>
>>>
>>>> In my present case this setup gives rather slow convergence (varies for
>>>> different geometries between 200-500 or several thousands!). I obtain
>>>> better convergence with "-pc_fieldsplit_schur_precondition selfp"and
>>>> using multigrid on S, with "-fieldsplit_1_pc_type ml" (I don't think
>>>> this is optimal, though).
>>>>
>>>> I don't understand why the pressure mass matrix approach performs so
>>>> poorly and wonder what I could try to improve the convergence. Until now
>>>> I have been using ML and Hypre BoomerAMG mostly with default parameters.
>>>> Surely they can be improved by tuning some parameters. Which could be a
>>>> good starting point? Are there other options I should consider?
>>>>
>>>> With the above setup (jacobi) for a case that works better than others,
>>>> the KSP terminates with
>>>> 467 KSP unpreconditioned resid norm 2.072014323515e-09 true resid norm
>>>> 2.072014322600e-09 ||r(i)||/||b|| 9.939098100674e-09
>>>>
>>>> You can find the output of -ksp_view below. Let me know if you need more
>>>> details.
>>>>
>>>> Thanks in advance for your advice!
>>>> Best wishes
>>>> David
>>>>
>>>>
>>>> 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-08, absolute=1e-50, divergence=10000.
>>>> right preconditioning
>>>> diagonally scaled system
>>>> using nonzero initial guess
>>>> using UNPRECONDITIONED norm type for convergence test
>>>> PC Object: 1 MPI processes
>>>> type: fieldsplit
>>>> FieldSplit with Schur preconditioner, factorization UPPER
>>>> Preconditioner for the Schur complement formed from user provided
>>>> matrix
>>>> Split info:
>>>> Split number 0 Defined by IS
>>>> Split number 1 Defined by IS
>>>> KSP solver for A00 block
>>>> KSP Object: (fieldsplit_0_) 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: (fieldsplit_0_) 1 MPI processes
>>>> type: ml
>>>> MG: type is MULTIPLICATIVE, levels=5 cycles=v
>>>> Cycles per PCApply=1
>>>> Using Galerkin computed coarse grid matrices
>>>> Coarse grid solver -- level -------------------------------
>>>> KSP Object: (fieldsplit_0_mg_coarse_) 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: (fieldsplit_0_mg_coarse_) 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 1.
>>>> Factored matrix follows:
>>>> Mat Object: 1 MPI processes
>>>> type: seqaij
>>>> rows=3, cols=3
>>>> package used to perform factorization: petsc
>>>> total: nonzeros=3, allocated nonzeros=3
>>>> 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=3, cols=3
>>>> total: nonzeros=3, allocated nonzeros=3
>>>> total number of mallocs used during MatSetValues calls =0
>>>> not using I-node routines
>>>> Down solver (pre-smoother) on level 1
>>>> -------------------------------
>>>> KSP Object: (fieldsplit_0_mg_levels_1_) 1
>>>> 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: (fieldsplit_0_mg_levels_1_) 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=15, cols=15
>>>> total: nonzeros=69, allocated nonzeros=69
>>>> total number of mallocs used during MatSetValues calls =0
>>>> not using I-node routines
>>>> Up solver (post-smoother) same as down solver (pre-smoother)
>>>> Down solver (pre-smoother) on level 2
>>>> -------------------------------
>>>> KSP Object: (fieldsplit_0_mg_levels_2_) 1
>>>> 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: (fieldsplit_0_mg_levels_2_) 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=304, cols=304
>>>> total: nonzeros=7354, allocated nonzeros=7354
>>>> total number of mallocs used during MatSetValues calls =0
>>>> not using I-node routines
>>>> Up solver (post-smoother) same as down solver (pre-smoother)
>>>> Down solver (pre-smoother) on level 3
>>>> -------------------------------
>>>> KSP Object: (fieldsplit_0_mg_levels_3_) 1
>>>> 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: (fieldsplit_0_mg_levels_3_) 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=30236, cols=30236
>>>> total: nonzeros=2730644, allocated nonzeros=2730644
>>>> total number of mallocs used during MatSetValues calls =0
>>>> not using I-node routines
>>>> Up solver (post-smoother) same as down solver (pre-smoother)
>>>> Down solver (pre-smoother) on level 4
>>>> -------------------------------
>>>> KSP Object: (fieldsplit_0_mg_levels_4_) 1
>>>> 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: (fieldsplit_0_mg_levels_4_) 1
>>>> MPI processes
>>>> type: sor
>>>> SOR: type = local_symmetric, iterations = 1, local
>>>> iterations = 1, omega = 1.
>>>> linear system matrix = precond matrix:
>>>> Mat Object: (fieldsplit_0_) 1 MPI
>>>> processes
>>>> type: seqaij
>>>> rows=894132, cols=894132
>>>> total: nonzeros=70684164, allocated nonzeros=70684164
>>>> total number of mallocs used during MatSetValues calls =0
>>>> not using I-node routines
>>>> Up solver (post-smoother) same as down solver (pre-smoother)
>>>> linear system matrix = precond matrix:
>>>> Mat Object: (fieldsplit_0_) 1 MPI processes
>>>> type: seqaij
>>>> rows=894132, cols=894132
>>>> total: nonzeros=70684164, allocated nonzeros=70684164
>>>> total number of mallocs used during MatSetValues calls =0
>>>> not using I-node routines
>>>> KSP solver for S = A11 - A10 inv(A00) A01
>>>> KSP Object: (fieldsplit_1_) 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: (fieldsplit_1_) 1 MPI processes
>>>> type: jacobi
>>>> linear system matrix followed by preconditioner matrix:
>>>> Mat Object: (fieldsplit_1_) 1 MPI processes
>>>> type: schurcomplement
>>>> rows=42025, cols=42025
>>>> Schur complement A11 - A10 inv(A00) A01
>>>> A11
>>>> Mat Object: (fieldsplit_1_) 1
>>>> MPI processes
>>>> type: seqaij
>>>> rows=42025, cols=42025
>>>> total: nonzeros=554063, allocated nonzeros=554063
>>>> total number of mallocs used during MatSetValues calls
>>>> =0
>>>> not using I-node routines
>>>> A10
>>>> Mat Object: 1 MPI processes
>>>> type: seqaij
>>>> rows=42025, cols=894132
>>>> total: nonzeros=6850107, allocated nonzeros=6850107
>>>> total number of mallocs used during MatSetValues calls
>>>> =0
>>>> not using I-node routines
>>>> KSP of A00
>>>> KSP Object: (fieldsplit_0_) 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: (fieldsplit_0_) 1
>>>> MPI processes
>>>> type: ml
>>>> MG: type is MULTIPLICATIVE, levels=5 cycles=v
>>>> Cycles per PCApply=1
>>>> Using Galerkin computed coarse grid matrices
>>>> Coarse grid solver -- level
>>>> -------------------------------
>>>> KSP Object:
>>>> (fieldsplit_0_mg_coarse_) 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:
>>>> (fieldsplit_0_mg_coarse_) 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 1.
>>>> Factored matrix follows:
>>>> Mat Object: 1 MPI
>>>> processes
>>>> type: seqaij
>>>> rows=3, cols=3
>>>> package used to perform factorization: petsc
>>>> total: nonzeros=3, allocated nonzeros=3
>>>> 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=3, cols=3
>>>> total: nonzeros=3, allocated nonzeros=3
>>>> total number of mallocs used during MatSetValues
>>>> calls =0
>>>> not using I-node routines
>>>> Down solver (pre-smoother) on level 1
>>>> -------------------------------
>>>> KSP Object:
>>>> (fieldsplit_0_mg_levels_1_) 1 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:
>>>> (fieldsplit_0_mg_levels_1_) 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=15, cols=15
>>>> total: nonzeros=69, allocated nonzeros=69
>>>> total number of mallocs used during MatSetValues
>>>> calls =0
>>>> not using I-node routines
>>>> Up solver (post-smoother) same as down solver
>>>> (pre-smoother)
>>>> Down solver (pre-smoother) on level 2
>>>> -------------------------------
>>>> KSP Object:
>>>> (fieldsplit_0_mg_levels_2_) 1 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:
>>>> (fieldsplit_0_mg_levels_2_) 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=304, cols=304
>>>> total: nonzeros=7354, allocated nonzeros=7354
>>>> total number of mallocs used during MatSetValues
>>>> calls =0
>>>> not using I-node routines
>>>> Up solver (post-smoother) same as down solver
>>>> (pre-smoother)
>>>> Down solver (pre-smoother) on level 3
>>>> -------------------------------
>>>> KSP Object:
>>>> (fieldsplit_0_mg_levels_3_) 1 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:
>>>> (fieldsplit_0_mg_levels_3_) 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=30236, cols=30236
>>>> total: nonzeros=2730644, allocated
>>>> nonzeros=2730644
>>>> total number of mallocs used during MatSetValues
>>>> calls =0
>>>> not using I-node routines
>>>> Up solver (post-smoother) same as down solver
>>>> (pre-smoother)
>>>> Down solver (pre-smoother) on level 4
>>>> -------------------------------
>>>> KSP Object:
>>>> (fieldsplit_0_mg_levels_4_) 1 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:
>>>> (fieldsplit_0_mg_levels_4_) 1 MPI processes
>>>> type: sor
>>>> SOR: type = local_symmetric, iterations = 1, local
>>>> iterations = 1, omega = 1.
>>>> linear system matrix = precond matrix:
>>>> Mat Object:
>>>> (fieldsplit_0_) 1 MPI processes
>>>> type: seqaij
>>>> rows=894132, cols=894132
>>>> total: nonzeros=70684164, allocated
>>>> nonzeros=70684164
>>>> total number of mallocs used during MatSetValues
>>>> calls =0
>>>> not using I-node routines
>>>> Up solver (post-smoother) same as down solver
>>>> (pre-smoother)
>>>> linear system matrix = precond matrix:
>>>> Mat Object:
>>>> (fieldsplit_0_) 1 MPI processes
>>>> type: seqaij
>>>> rows=894132, cols=894132
>>>> total: nonzeros=70684164, allocated nonzeros=70684164
>>>> total number of mallocs used during MatSetValues
>>>> calls =0
>>>> not using I-node routines
>>>> A01
>>>> Mat Object: 1 MPI processes
>>>> type: seqaij
>>>> rows=894132, cols=42025
>>>> total: nonzeros=6850107, allocated nonzeros=6850107
>>>> total number of mallocs used during MatSetValues calls
>>>> =0
>>>> not using I-node routines
>>>> Mat Object: 1 MPI processes
>>>> type: seqaij
>>>> rows=42025, cols=42025
>>>> total: nonzeros=554063, allocated nonzeros=554063
>>>> 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=936157, cols=936157
>>>> total: nonzeros=84938441, allocated nonzeros=84938441
>>>> total number of mallocs used during MatSetValues calls =0
>>>> not using I-node routines
>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> 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
>>>
>>> http://www.caam.rice.edu/~mk51/
>>>
>>>
>>>
>>
>>
>> --
>> 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
>>
>> http://www.caam.rice.edu/~mk51/
>>
>>
>>
>
>
> --
> 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
>
> http://www.caam.rice.edu/~mk51/
>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20170612/a8e7b9f2/attachment-0001.html>
More information about the petsc-users
mailing list