[petsc-users] sources of floating point randomness in JFNK in serial
Matthew Knepley
knepley at gmail.com
Thu May 4 15:56:53 CDT 2023
On Thu, May 4, 2023 at 4:44 PM Mark Lohry <mlohry at 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 <knepley at gmail.com> wrote:
>
>> On Thu, May 4, 2023 at 8:54 AM Mark Lohry <mlohry at 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 <mfadams at 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 <mlohry at 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 <mfadams at 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 <knepley at gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> On Thu, May 4, 2023 at 8:21 AM Mark Lohry <mlohry at 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 <bsmith at 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 <mlohry at 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 <bsmith at 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 <mlohry at 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/>
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