On Thu, May 4, 2023 at 5:03 PM Mark Lohry <mlo...@gmail.com> wrote:
> Do you get different results (in different runs) without
>> -snes_mf_operator? So just using an explicit matrix?
>
>
> Unfortunately I don't have an explicit matrix available for this, hence
> the MFFD/JFNK.
>
I don't mean the actual matrix, I mean a representative matrix.
>
>> (Note: I am not convinced there is even a problem and think it may be
>> simply different order of floating point operations in different runs.)
>>
>
> I'm not convinced either, but running explicit RK for 10,000 iterations i
> get exactly the same results every time so i'm fairly confident it's not
> the residual evaluation.
> How would there be a different order of floating point ops in different
> runs in serial?
>
> No, I mean without -snes_mf_* (as Barry says), so we are just running that
>> solver with a sparse matrix. This would give me confidence
>> that nothing in the solver is variable.
>>
>> I could do the sparse finite difference jacobian once, save it to disk,
> and then use that system each time.
>
Yes. That would work.
Thanks,
Matt
> On Thu, May 4, 2023 at 4:57 PM Matthew Knepley <knep...@gmail.com> wrote:
>
>> On Thu, May 4, 2023 at 4:44 PM Mark Lohry <mlo...@gmail.com> wrote:
>>
>>> Is your code valgrind clean?
>>>>
>>>
>>> Yes, I also initialize all allocations with NaNs to be sure I'm not
>>> using anything uninitialized.
>>>
>>>
>>>> We can try and test this. Replace your MatMFFD with an actual matrix
>>>> and run. Do you see any variability?
>>>>
>>>
>>> I think I did what you're asking. I have -snes_mf_operator set, and then
>>> SNESSetJacobian(snes, diag_ones, diag_ones, NULL, NULL) where diag_ones is
>>> a matrix with ones on the diagonal. Two runs below, still with differences
>>> but sometimes identical.
>>>
>>
>> No, I mean without -snes_mf_* (as Barry says), so we are just running
>> that solver with a sparse matrix. This would give me confidence
>> that nothing in the solver is variable.
>>
>> Thanks,
>>
>> Matt
>>
>>
>>> 0 SNES Function norm 3.424003312857e+04
>>> 0 KSP Residual norm 3.424003312857e+04
>>> 1 KSP Residual norm 2.871734444536e+04
>>> 2 KSP Residual norm 2.490276930242e+04
>>> 3 KSP Residual norm 2.131675872968e+04
>>> 4 KSP Residual norm 1.973129814235e+04
>>> 5 KSP Residual norm 1.832377856317e+04
>>> 6 KSP Residual norm 1.716783617436e+04
>>> 7 KSP Residual norm 1.583963149542e+04
>>> 8 KSP Residual norm 1.482272170304e+04
>>> 9 KSP Residual norm 1.380312106742e+04
>>> 10 KSP Residual norm 1.297793480658e+04
>>> 11 KSP Residual norm 1.208599123244e+04
>>> 12 KSP Residual norm 1.137345655227e+04
>>> 13 KSP Residual norm 1.059676909366e+04
>>> 14 KSP Residual norm 1.003823862398e+04
>>> 15 KSP Residual norm 9.425879221354e+03
>>> 16 KSP Residual norm 8.954805890038e+03
>>> 17 KSP Residual norm 8.592372470456e+03
>>> 18 KSP Residual norm 8.060707175821e+03
>>> 19 KSP Residual norm 7.782057728723e+03
>>> 20 KSP Residual norm 7.449686095424e+03
>>> Linear solve converged due to CONVERGED_ITS iterations 20
>>> KSP Object: 1 MPI process
>>> type: gmres
>>> restart=30, using Classical (unmodified) Gram-Schmidt
>>> Orthogonalization with no iterative refinement
>>> happy breakdown tolerance 1e-30
>>> maximum iterations=20, initial guess is zero
>>> tolerances: relative=0.1, absolute=1e-15, divergence=10.
>>> left preconditioning
>>> using PRECONDITIONED norm type for convergence test
>>> PC Object: 1 MPI process
>>> type: none
>>> linear system matrix followed by preconditioner matrix:
>>> Mat Object: 1 MPI process
>>> type: mffd
>>> rows=16384, cols=16384
>>> Matrix-free approximation:
>>> err=1.49012e-08 (relative error in function evaluation)
>>> Using wp compute h routine
>>> Does not compute normU
>>> Mat Object: 1 MPI process
>>> type: seqaij
>>> rows=16384, cols=16384
>>> total: nonzeros=16384, allocated nonzeros=16384
>>> total number of mallocs used during MatSetValues calls=0
>>> not using I-node routines
>>> 1 SNES Function norm 1.085015646971e+04
>>> Nonlinear solve converged due to CONVERGED_ITS iterations 1
>>> SNES Object: 1 MPI process
>>> type: newtonls
>>> maximum iterations=1, maximum function evaluations=-1
>>> tolerances: relative=0.1, absolute=1e-15, solution=1e-15
>>> total number of linear solver iterations=20
>>> total number of function evaluations=23
>>> norm schedule ALWAYS
>>> Jacobian is never rebuilt
>>> Jacobian is applied matrix-free with differencing
>>> Preconditioning Jacobian is built using finite differences with
>>> coloring
>>> SNESLineSearch Object: 1 MPI process
>>> type: basic
>>> maxstep=1.000000e+08, minlambda=1.000000e-12
>>> tolerances: relative=1.000000e-08, absolute=1.000000e-15,
>>> lambda=1.000000e-08
>>> maximum iterations=40
>>> KSP Object: 1 MPI process
>>> type: gmres
>>> restart=30, using Classical (unmodified) Gram-Schmidt
>>> Orthogonalization with no iterative refinement
>>> happy breakdown tolerance 1e-30
>>> maximum iterations=20, initial guess is zero
>>> tolerances: relative=0.1, absolute=1e-15, divergence=10.
>>> left preconditioning
>>> using PRECONDITIONED norm type for convergence test
>>> PC Object: 1 MPI process
>>> type: none
>>> linear system matrix followed by preconditioner matrix:
>>> Mat Object: 1 MPI process
>>> type: mffd
>>> rows=16384, cols=16384
>>> Matrix-free approximation:
>>> err=1.49012e-08 (relative error in function evaluation)
>>> Using wp compute h routine
>>> Does not compute normU
>>> Mat Object: 1 MPI process
>>> type: seqaij
>>> rows=16384, cols=16384
>>> total: nonzeros=16384, allocated nonzeros=16384
>>> total number of mallocs used during MatSetValues calls=0
>>> not using I-node routines
>>>
>>> 0 SNES Function norm 3.424003312857e+04
>>> 0 KSP Residual norm 3.424003312857e+04
>>> 1 KSP Residual norm 2.871734444536e+04
>>> 2 KSP Residual norm 2.490276931041e+04
>>> 3 KSP Residual norm 2.131675873776e+04
>>> 4 KSP Residual norm 1.973129814908e+04
>>> 5 KSP Residual norm 1.832377852186e+04
>>> 6 KSP Residual norm 1.716783608174e+04
>>> 7 KSP Residual norm 1.583963128956e+04
>>> 8 KSP Residual norm 1.482272160069e+04
>>> 9 KSP Residual norm 1.380312087005e+04
>>> 10 KSP Residual norm 1.297793458796e+04
>>> 11 KSP Residual norm 1.208599115602e+04
>>> 12 KSP Residual norm 1.137345657533e+04
>>> 13 KSP Residual norm 1.059676906197e+04
>>> 14 KSP Residual norm 1.003823857515e+04
>>> 15 KSP Residual norm 9.425879177747e+03
>>> 16 KSP Residual norm 8.954805850825e+03
>>> 17 KSP Residual norm 8.592372413320e+03
>>> 18 KSP Residual norm 8.060706994110e+03
>>> 19 KSP Residual norm 7.782057560782e+03
>>> 20 KSP Residual norm 7.449686034356e+03
>>> Linear solve converged due to CONVERGED_ITS iterations 20
>>> KSP Object: 1 MPI process
>>> type: gmres
>>> restart=30, using Classical (unmodified) Gram-Schmidt
>>> Orthogonalization with no iterative refinement
>>> happy breakdown tolerance 1e-30
>>> maximum iterations=20, initial guess is zero
>>> tolerances: relative=0.1, absolute=1e-15, divergence=10.
>>> left preconditioning
>>> using PRECONDITIONED norm type for convergence test
>>> PC Object: 1 MPI process
>>> type: none
>>> linear system matrix followed by preconditioner matrix:
>>> Mat Object: 1 MPI process
>>> type: mffd
>>> rows=16384, cols=16384
>>> Matrix-free approximation:
>>> err=1.49012e-08 (relative error in function evaluation)
>>> Using wp compute h routine
>>> Does not compute normU
>>> Mat Object: 1 MPI process
>>> type: seqaij
>>> rows=16384, cols=16384
>>> total: nonzeros=16384, allocated nonzeros=16384
>>> total number of mallocs used during MatSetValues calls=0
>>> not using I-node routines
>>> 1 SNES Function norm 1.085015821006e+04
>>> Nonlinear solve converged due to CONVERGED_ITS iterations 1
>>> SNES Object: 1 MPI process
>>> type: newtonls
>>> maximum iterations=1, maximum function evaluations=-1
>>> tolerances: relative=0.1, absolute=1e-15, solution=1e-15
>>> total number of linear solver iterations=20
>>> total number of function evaluations=23
>>> norm schedule ALWAYS
>>> Jacobian is never rebuilt
>>> Jacobian is applied matrix-free with differencing
>>> Preconditioning Jacobian is built using finite differences with
>>> coloring
>>> SNESLineSearch Object: 1 MPI process
>>> type: basic
>>> maxstep=1.000000e+08, minlambda=1.000000e-12
>>> tolerances: relative=1.000000e-08, absolute=1.000000e-15,
>>> lambda=1.000000e-08
>>> maximum iterations=40
>>> KSP Object: 1 MPI process
>>> type: gmres
>>> restart=30, using Classical (unmodified) Gram-Schmidt
>>> Orthogonalization with no iterative refinement
>>> happy breakdown tolerance 1e-30
>>> maximum iterations=20, initial guess is zero
>>> tolerances: relative=0.1, absolute=1e-15, divergence=10.
>>> left preconditioning
>>> using PRECONDITIONED norm type for convergence test
>>> PC Object: 1 MPI process
>>> type: none
>>> linear system matrix followed by preconditioner matrix:
>>> Mat Object: 1 MPI process
>>> type: mffd
>>> rows=16384, cols=16384
>>> Matrix-free approximation:
>>> err=1.49012e-08 (relative error in function evaluation)
>>> Using wp compute h routine
>>> Does not compute normU
>>> Mat Object: 1 MPI process
>>> type: seqaij
>>> rows=16384, cols=16384
>>> total: nonzeros=16384, allocated nonzeros=16384
>>> total number of mallocs used during MatSetValues calls=0
>>> not using I-node routines
>>>
>>> On Thu, May 4, 2023 at 10:10 AM Matthew Knepley <knep...@gmail.com>
>>> wrote:
>>>
>>>> On Thu, May 4, 2023 at 8:54 AM Mark Lohry <mlo...@gmail.com> wrote:
>>>>
>>>>> Try -pc_type none.
>>>>>>
>>>>>
>>>>> With -pc_type none the 0 KSP residual looks identical. But *sometimes*
>>>>> it's producing exactly the same history and others it's gradually
>>>>> changing. I'm reasonably confident my residual evaluation has no
>>>>> randomness, see info after the petsc output.
>>>>>
>>>>
>>>> We can try and test this. Replace your MatMFFD with an actual matrix
>>>> and run. Do you see any variability?
>>>>
>>>> If not, then it could be your routine, or it could be MatMFFD. So run a
>>>> few with -snes_view, and we can see if the
>>>> "w" parameter changes.
>>>>
>>>> Thanks,
>>>>
>>>> Matt
>>>>
>>>>
>>>>> solve history 1:
>>>>>
>>>>> 0 SNES Function norm 3.424003312857e+04
>>>>> 0 KSP Residual norm 3.424003312857e+04
>>>>> 1 KSP Residual norm 2.871734444536e+04
>>>>> 2 KSP Residual norm 2.490276931041e+04
>>>>> ...
>>>>> 20 KSP Residual norm 7.449686034356e+03
>>>>> Linear solve converged due to CONVERGED_ITS iterations 20
>>>>> 1 SNES Function norm 1.085015821006e+04
>>>>>
>>>>> solve history 2, identical to 1:
>>>>>
>>>>> 0 SNES Function norm 3.424003312857e+04
>>>>> 0 KSP Residual norm 3.424003312857e+04
>>>>> 1 KSP Residual norm 2.871734444536e+04
>>>>> 2 KSP Residual norm 2.490276931041e+04
>>>>> ...
>>>>> 20 KSP Residual norm 7.449686034356e+03
>>>>> Linear solve converged due to CONVERGED_ITS iterations 20
>>>>> 1 SNES Function norm 1.085015821006e+04
>>>>>
>>>>> solve history 3, identical KSP at 0 and 1, slight change at 2, growing
>>>>> difference to the end:
>>>>> 0 SNES Function norm 3.424003312857e+04
>>>>> 0 KSP Residual norm 3.424003312857e+04
>>>>> 1 KSP Residual norm 2.871734444536e+04
>>>>> 2 KSP Residual norm 2.490276930242e+04
>>>>> ...
>>>>> 20 KSP Residual norm 7.449686095424e+03
>>>>> Linear solve converged due to CONVERGED_ITS iterations 20
>>>>> 1 SNES Function norm 1.085015646971e+04
>>>>>
>>>>>
>>>>> Ths is using a standard explicit 3-stage Runge-Kutta smoother for 10
>>>>> iterations, so 30 calls of the same residual evaluation, identical
>>>>> residuals every time
>>>>>
>>>>> run 1:
>>>>>
>>>>> # iteration rho rhou rhov
>>>>> rhoE abs_res rel_res
>>>>> umin vmax vmin elapsed_time
>>>>>
>>>>> #
>>>>>
>>>>>
>>>>> 1.00000e+00 1.086860616292e+00 2.782316758416e+02
>>>>> 4.482867643761e+00 2.993435920340e+02 2.04353e+02
>>>>> 1.00000e+00 -8.23945e-15 -6.15326e-15 -1.35563e-14
>>>>> 6.34834e-01
>>>>> 2.00000e+00 2.310547487017e+00 1.079059352425e+02
>>>>> 3.958323921837e+00 5.058927165686e+02 2.58647e+02
>>>>> 1.26568e+00 -1.02539e-14 -9.35368e-15 -1.69925e-14
>>>>> 6.40063e-01
>>>>> 3.00000e+00 2.361005867444e+00 5.706213331683e+01
>>>>> 6.130016323357e+00 4.688968362579e+02 2.36201e+02
>>>>> 1.15585e+00 -1.19370e-14 -1.15216e-14 -1.59733e-14
>>>>> 6.45166e-01
>>>>> 4.00000e+00 2.167518999963e+00 3.757541401594e+01
>>>>> 6.313917437428e+00 4.054310291628e+02 2.03612e+02
>>>>> 9.96372e-01 -1.81831e-14 -1.28312e-14 -1.46238e-14
>>>>> 6.50494e-01
>>>>> 5.00000e+00 1.941443738676e+00 2.884190334049e+01
>>>>> 6.237106158479e+00 3.539201037156e+02 1.77577e+02
>>>>> 8.68970e-01 3.56633e-14 -8.74089e-15 -1.06666e-14
>>>>> 6.55656e-01
>>>>> 6.00000e+00 1.736947124693e+00 2.429485695670e+01
>>>>> 5.996962200407e+00 3.148280178142e+02 1.57913e+02
>>>>> 7.72745e-01 -8.98634e-14 -2.41152e-14 -1.39713e-14
>>>>> 6.60872e-01
>>>>> 7.00000e+00 1.564153212635e+00 2.149609219810e+01
>>>>> 5.786910705204e+00 2.848717011033e+02 1.42872e+02
>>>>> 6.99144e-01 -2.95352e-13 -2.48158e-14 -2.39351e-14
>>>>> 6.66041e-01
>>>>> 8.00000e+00 1.419280815384e+00 1.950619804089e+01
>>>>> 5.627281158306e+00 2.606623371229e+02 1.30728e+02
>>>>> 6.39715e-01 8.98941e-13 1.09674e-13 3.78905e-14
>>>>> 6.71316e-01
>>>>> 9.00000e+00 1.296115915975e+00 1.794843530745e+01
>>>>> 5.514933264437e+00 2.401524522393e+02 1.20444e+02
>>>>> 5.89394e-01 1.70717e-12 1.38762e-14 1.09825e-13
>>>>> 6.76447e-01
>>>>> 1.00000e+01 1.189639693918e+00 1.665381754953e+01
>>>>> 5.433183087037e+00 2.222572900473e+02 1.11475e+02
>>>>> 5.45501e-01 -4.22462e-12 -7.15206e-13 -2.28736e-13
>>>>> 6.81716e-01
>>>>>
>>>>> run N:
>>>>>
>>>>>
>>>>> #
>>>>>
>>>>>
>>>>> # iteration rho rhou rhov
>>>>> rhoE abs_res rel_res
>>>>> umin vmax vmin elapsed_time
>>>>>
>>>>> #
>>>>>
>>>>>
>>>>> 1.00000e+00 1.086860616292e+00 2.782316758416e+02
>>>>> 4.482867643761e+00 2.993435920340e+02 2.04353e+02
>>>>> 1.00000e+00 -8.23945e-15 -6.15326e-15 -1.35563e-14
>>>>> 6.23316e-01
>>>>> 2.00000e+00 2.310547487017e+00 1.079059352425e+02
>>>>> 3.958323921837e+00 5.058927165686e+02 2.58647e+02
>>>>> 1.26568e+00 -1.02539e-14 -9.35368e-15 -1.69925e-14
>>>>> 6.28510e-01
>>>>> 3.00000e+00 2.361005867444e+00 5.706213331683e+01
>>>>> 6.130016323357e+00 4.688968362579e+02 2.36201e+02
>>>>> 1.15585e+00 -1.19370e-14 -1.15216e-14 -1.59733e-14
>>>>> 6.33558e-01
>>>>> 4.00000e+00 2.167518999963e+00 3.757541401594e+01
>>>>> 6.313917437428e+00 4.054310291628e+02 2.03612e+02
>>>>> 9.96372e-01 -1.81831e-14 -1.28312e-14 -1.46238e-14
>>>>> 6.38773e-01
>>>>> 5.00000e+00 1.941443738676e+00 2.884190334049e+01
>>>>> 6.237106158479e+00 3.539201037156e+02 1.77577e+02
>>>>> 8.68970e-01 3.56633e-14 -8.74089e-15 -1.06666e-14
>>>>> 6.43887e-01
>>>>> 6.00000e+00 1.736947124693e+00 2.429485695670e+01
>>>>> 5.996962200407e+00 3.148280178142e+02 1.57913e+02
>>>>> 7.72745e-01 -8.98634e-14 -2.41152e-14 -1.39713e-14
>>>>> 6.49073e-01
>>>>> 7.00000e+00 1.564153212635e+00 2.149609219810e+01
>>>>> 5.786910705204e+00 2.848717011033e+02 1.42872e+02
>>>>> 6.99144e-01 -2.95352e-13 -2.48158e-14 -2.39351e-14
>>>>> 6.54167e-01
>>>>> 8.00000e+00 1.419280815384e+00 1.950619804089e+01
>>>>> 5.627281158306e+00 2.606623371229e+02 1.30728e+02
>>>>> 6.39715e-01 8.98941e-13 1.09674e-13 3.78905e-14
>>>>> 6.59394e-01
>>>>> 9.00000e+00 1.296115915975e+00 1.794843530745e+01
>>>>> 5.514933264437e+00 2.401524522393e+02 1.20444e+02
>>>>> 5.89394e-01 1.70717e-12 1.38762e-14 1.09825e-13
>>>>> 6.64516e-01
>>>>> 1.00000e+01 1.189639693918e+00 1.665381754953e+01
>>>>> 5.433183087037e+00 2.222572900473e+02 1.11475e+02
>>>>> 5.45501e-01 -4.22462e-12 -7.15206e-13 -2.28736e-13
>>>>> 6.69677e-01
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Thu, May 4, 2023 at 8:41 AM Mark Adams <mfad...@lbl.gov> wrote:
>>>>>
>>>>>> ASM is just the sub PC with one proc but gets weaker with more procs
>>>>>> unless you use jacobi. (maybe I am missing something).
>>>>>>
>>>>>> On Thu, May 4, 2023 at 8:31 AM Mark Lohry <mlo...@gmail.com> wrote:
>>>>>>
>>>>>>> Please send the output of -snes_view.
>>>>>>>>
>>>>>>> pasted below. anything stand out?
>>>>>>>
>>>>>>>
>>>>>>> SNES Object: 1 MPI process
>>>>>>> type: newtonls
>>>>>>> maximum iterations=1, maximum function evaluations=-1
>>>>>>> tolerances: relative=0.1, absolute=1e-15, solution=1e-15
>>>>>>> total number of linear solver iterations=20
>>>>>>> total number of function evaluations=22
>>>>>>> norm schedule ALWAYS
>>>>>>> Jacobian is never rebuilt
>>>>>>> Jacobian is applied matrix-free with differencing
>>>>>>> Preconditioning Jacobian is built using finite differences with
>>>>>>> coloring
>>>>>>> SNESLineSearch Object: 1 MPI process
>>>>>>> type: basic
>>>>>>> maxstep=1.000000e+08, minlambda=1.000000e-12
>>>>>>> tolerances: relative=1.000000e-08, absolute=1.000000e-15,
>>>>>>> lambda=1.000000e-08
>>>>>>> maximum iterations=40
>>>>>>> KSP Object: 1 MPI process
>>>>>>> type: gmres
>>>>>>> restart=30, using Classical (unmodified) Gram-Schmidt
>>>>>>> Orthogonalization with no iterative refinement
>>>>>>> happy breakdown tolerance 1e-30
>>>>>>> maximum iterations=20, initial guess is zero
>>>>>>> tolerances: relative=0.1, absolute=1e-15, divergence=10.
>>>>>>> left preconditioning
>>>>>>> using PRECONDITIONED norm type for convergence test
>>>>>>> PC Object: 1 MPI process
>>>>>>> type: asm
>>>>>>> total subdomain blocks = 1, amount of overlap = 0
>>>>>>> restriction/interpolation type - RESTRICT
>>>>>>> Local solver information for first block is in the following
>>>>>>> KSP and PC objects on rank 0:
>>>>>>> Use -ksp_view ::ascii_info_detail to display information for
>>>>>>> all blocks
>>>>>>> KSP Object: (sub_) 1 MPI process
>>>>>>> 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: (sub_) 1 MPI process
>>>>>>> type: ilu
>>>>>>> out-of-place factorization
>>>>>>> 0 levels of fill
>>>>>>> tolerance for zero pivot 2.22045e-14
>>>>>>> matrix ordering: natural
>>>>>>> factor fill ratio given 1., needed 1.
>>>>>>> Factored matrix follows:
>>>>>>> Mat Object: (sub_) 1 MPI process
>>>>>>> type: seqbaij
>>>>>>> rows=16384, cols=16384, bs=16
>>>>>>> package used to perform factorization: petsc
>>>>>>> total: nonzeros=1277952, allocated nonzeros=1277952
>>>>>>> block size is 16
>>>>>>> linear system matrix = precond matrix:
>>>>>>> Mat Object: (sub_) 1 MPI process
>>>>>>> type: seqbaij
>>>>>>> rows=16384, cols=16384, bs=16
>>>>>>> total: nonzeros=1277952, allocated nonzeros=1277952
>>>>>>> total number of mallocs used during MatSetValues calls=0
>>>>>>> block size is 16
>>>>>>> linear system matrix followed by preconditioner matrix:
>>>>>>> Mat Object: 1 MPI process
>>>>>>> type: mffd
>>>>>>> rows=16384, cols=16384
>>>>>>> Matrix-free approximation:
>>>>>>> err=1.49012e-08 (relative error in function evaluation)
>>>>>>> Using wp compute h routine
>>>>>>> Does not compute normU
>>>>>>> Mat Object: 1 MPI process
>>>>>>> type: seqbaij
>>>>>>> rows=16384, cols=16384, bs=16
>>>>>>> total: nonzeros=1277952, allocated nonzeros=1277952
>>>>>>> total number of mallocs used during MatSetValues calls=0
>>>>>>> block size is 16
>>>>>>>
>>>>>>> On Thu, May 4, 2023 at 8:30 AM Mark Adams <mfad...@lbl.gov> wrote:
>>>>>>>
>>>>>>>> If you are using MG what is the coarse grid solver?
>>>>>>>> -snes_view might give you that.
>>>>>>>>
>>>>>>>> On Thu, May 4, 2023 at 8:25 AM Matthew Knepley <knep...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> On Thu, May 4, 2023 at 8:21 AM Mark Lohry <mlo...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Do they start very similarly and then slowly drift further apart?
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Yes, this. I take it this sounds familiar?
>>>>>>>>>>
>>>>>>>>>> See these two examples with 20 fixed iterations pasted at the
>>>>>>>>>> end. The difference for one solve is slight (final SNES norm is
>>>>>>>>>> identical
>>>>>>>>>> to 5 digits), but in the context I'm using it in (repeated
>>>>>>>>>> applications to
>>>>>>>>>> solve a steady state multigrid problem, though here just one level)
>>>>>>>>>> the
>>>>>>>>>> differences add up such that I might reach global convergence in 35
>>>>>>>>>> iterations or 38. It's not the end of the world, but I was expecting
>>>>>>>>>> that
>>>>>>>>>> with -np 1 these would be identical and I'm not sure where the root
>>>>>>>>>> cause
>>>>>>>>>> would be.
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> The initial KSP residual is different, so its the PC. Please send
>>>>>>>>> the output of -snes_view. If your ASM is using direct factorization,
>>>>>>>>> then it
>>>>>>>>> could be randomness in whatever LU you are using.
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>>
>>>>>>>>> Matt
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> 0 SNES Function norm 2.801842107848e+04
>>>>>>>>>> 0 KSP Residual norm 4.045639499595e+01
>>>>>>>>>> 1 KSP Residual norm 1.917999809040e+01
>>>>>>>>>> 2 KSP Residual norm 1.616048521958e+01
>>>>>>>>>> [...]
>>>>>>>>>> 19 KSP Residual norm 8.788043518111e-01
>>>>>>>>>> 20 KSP Residual norm 6.570851270214e-01
>>>>>>>>>> Linear solve converged due to CONVERGED_ITS iterations 20
>>>>>>>>>> 1 SNES Function norm 1.801309983345e+03
>>>>>>>>>> Nonlinear solve converged due to CONVERGED_ITS iterations 1
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Same system, identical initial 0 SNES norm, 0 KSP is slightly
>>>>>>>>>> different
>>>>>>>>>>
>>>>>>>>>> 0 SNES Function norm 2.801842107848e+04
>>>>>>>>>> 0 KSP Residual norm 4.045639473002e+01
>>>>>>>>>> 1 KSP Residual norm 1.917999883034e+01
>>>>>>>>>> 2 KSP Residual norm 1.616048572016e+01
>>>>>>>>>> [...]
>>>>>>>>>> 19 KSP Residual norm 8.788046348957e-01
>>>>>>>>>> 20 KSP Residual norm 6.570859588610e-01
>>>>>>>>>> Linear solve converged due to CONVERGED_ITS iterations 20
>>>>>>>>>> 1 SNES Function norm 1.801311320322e+03
>>>>>>>>>> Nonlinear solve converged due to CONVERGED_ITS iterations 1
>>>>>>>>>>
>>>>>>>>>> On Wed, May 3, 2023 at 11:05 PM Barry Smith <bsm...@petsc.dev>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Do they start very similarly and then slowly drift further
>>>>>>>>>>> apart? That is the first couple of KSP iterations they are almost
>>>>>>>>>>> identical
>>>>>>>>>>> but then for each iteration get a bit further. Similar for the SNES
>>>>>>>>>>> iterations, starting close and then for more iterations and more
>>>>>>>>>>> solves
>>>>>>>>>>> they start moving apart. Or do they suddenly jump to be very
>>>>>>>>>>> different? You
>>>>>>>>>>> can run with -snes_monitor -ksp_monitor
>>>>>>>>>>>
>>>>>>>>>>> On May 3, 2023, at 9:07 PM, Mark Lohry <mlo...@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>> This is on a single MPI rank. I haven't checked the coloring,
>>>>>>>>>>> was just guessing there. But the solutions/residuals are slightly
>>>>>>>>>>> different
>>>>>>>>>>> from run to run.
>>>>>>>>>>>
>>>>>>>>>>> Fair to say that for serial JFNK/asm ilu0/gmres we should expect
>>>>>>>>>>> bitwise identical results?
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Wed, May 3, 2023, 8:50 PM Barry Smith <bsm...@petsc.dev>
>>>>>>>>>>> wrote:
>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> No, the coloring should be identical every time. Do you see
>>>>>>>>>>>> differences with 1 MPI rank? (Or much smaller ones?).
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> > On May 3, 2023, at 8:42 PM, Mark Lohry <mlo...@gmail.com>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>> >
>>>>>>>>>>>> > I'm running multiple iterations of newtonls with an MFFD/JFNK
>>>>>>>>>>>> nonlinear solver where I give it the sparsity. PC asm, KSP gmres,
>>>>>>>>>>>> with
>>>>>>>>>>>> SNESSetLagJacobian -2 (compute once and then frozen jacobian).
>>>>>>>>>>>> >
>>>>>>>>>>>> > I'm seeing slight (<1%) but nonzero differences in residuals
>>>>>>>>>>>> from run to run. I'm wondering where randomness might enter here
>>>>>>>>>>>> -- does
>>>>>>>>>>>> the jacobian coloring use a random seed?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> 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.cse.buffalo.edu/~knepley/>
>>>>>>>>>
>>>>>>>>
>>>>
>>>> --
>>>> 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.cse.buffalo.edu/~knepley/>
>>>>
>>>
>>
>> --
>> 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.cse.buffalo.edu/~knepley/>
>>
>
--
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.cse.buffalo.edu/~knepley/>
