[petsc-users] sources of floating point randomness in JFNK in serial
Mark Lohry
mlohry at gmail.com
Thu May 4 16:35:53 CDT 2023
>
> Sure, but why only once and why save to disk? Why not just use that
> computed approximate Jacobian at each Newton step to drive the Newton
> solves along for a bunch of time steps?
Ah I get what you mean. Okay I did three newton steps with the same LHS,
with a few repeated manual tests. 3 out of 4 times i got the same exact
history. is it in the realm of possibility that a hardware error could
cause something this subtle, bad memory bit or something?
2 runs of 3 newton solves below, ever-so-slightly different.
0 SNES Function norm 3.424003312857e+04
0 KSP Residual norm 3.424003312857e+04
1 KSP Residual norm 2.886124328003e+04
2 KSP Residual norm 2.504664994246e+04
3 KSP Residual norm 2.104615835161e+04
4 KSP Residual norm 1.938102896632e+04
5 KSP Residual norm 1.793774642408e+04
6 KSP Residual norm 1.671392566980e+04
7 KSP Residual norm 1.501504103873e+04
8 KSP Residual norm 1.366362900747e+04
9 KSP Residual norm 1.240398500429e+04
10 KSP Residual norm 1.156293733914e+04
11 KSP Residual norm 1.066296477958e+04
12 KSP Residual norm 9.835601966950e+03
13 KSP Residual norm 9.017480191491e+03
14 KSP Residual norm 8.415336139780e+03
15 KSP Residual norm 7.807497808435e+03
16 KSP Residual norm 7.341703768294e+03
17 KSP Residual norm 6.979298049282e+03
18 KSP Residual norm 6.521277772081e+03
19 KSP Residual norm 6.174842408773e+03
20 KSP Residual norm 5.889819665003e+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 = precond matrix:
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
1 SNES Function norm 1.000525348433e+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=2
norm schedule ALWAYS
Jacobian is never rebuilt
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 = precond matrix:
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
0 SNES Function norm 1.000525348433e+04
0 KSP Residual norm 1.000525348433e+04
1 KSP Residual norm 7.908741564765e+03
2 KSP Residual norm 6.825263536686e+03
3 KSP Residual norm 6.224930664968e+03
4 KSP Residual norm 6.095547180532e+03
5 KSP Residual norm 5.952968230430e+03
6 KSP Residual norm 5.861251998116e+03
7 KSP Residual norm 5.712439327755e+03
8 KSP Residual norm 5.583056913266e+03
9 KSP Residual norm 5.461768804626e+03
10 KSP Residual norm 5.351937611098e+03
11 KSP Residual norm 5.224288337578e+03
12 KSP Residual norm 5.129863847081e+03
13 KSP Residual norm 5.010818237218e+03
14 KSP Residual norm 4.907162936199e+03
15 KSP Residual norm 4.789564773955e+03
16 KSP Residual norm 4.695173370720e+03
17 KSP Residual norm 4.584070962171e+03
18 KSP Residual norm 4.483061424742e+03
19 KSP Residual norm 4.373384070745e+03
20 KSP Residual norm 4.260704657592e+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 = precond matrix:
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
1 SNES Function norm 4.662386014882e+03
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=2
norm schedule ALWAYS
Jacobian is never rebuilt
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 = precond matrix:
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
0 SNES Function norm 4.662386014882e+03
0 KSP Residual norm 4.662386014882e+03
1 KSP Residual norm 4.408316259864e+03
2 KSP Residual norm 4.184867769829e+03
3 KSP Residual norm 4.079091244351e+03
4 KSP Residual norm 4.009247390166e+03
5 KSP Residual norm 3.928417371428e+03
6 KSP Residual norm 3.865152075780e+03
7 KSP Residual norm 3.795606446033e+03
8 KSP Residual norm 3.735294554158e+03
9 KSP Residual norm 3.674393726487e+03
10 KSP Residual norm 3.617795166786e+03
11 KSP Residual norm 3.563807982274e+03
12 KSP Residual norm 3.512269444921e+03
13 KSP Residual norm 3.455110223236e+03
14 KSP Residual norm 3.407141247372e+03
15 KSP Residual norm 3.356562415982e+03
16 KSP Residual norm 3.312720047685e+03
17 KSP Residual norm 3.263690150810e+03
18 KSP Residual norm 3.219359862444e+03
19 KSP Residual norm 3.173500955995e+03
20 KSP Residual norm 3.127528790155e+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 = precond matrix:
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
1 SNES Function norm 3.186752172556e+03
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=2
norm schedule ALWAYS
Jacobian is never rebuilt
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 = precond matrix:
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
0 SNES Function norm 3.424003312857e+04
0 KSP Residual norm 3.424003312857e+04
1 KSP Residual norm 2.886124328003e+04
2 KSP Residual norm 2.504664994221e+04
3 KSP Residual norm 2.104615835130e+04
4 KSP Residual norm 1.938102896610e+04
5 KSP Residual norm 1.793774642406e+04
6 KSP Residual norm 1.671392566981e+04
7 KSP Residual norm 1.501504103854e+04
8 KSP Residual norm 1.366362900726e+04
9 KSP Residual norm 1.240398500414e+04
10 KSP Residual norm 1.156293733914e+04
11 KSP Residual norm 1.066296477972e+04
12 KSP Residual norm 9.835601967036e+03
13 KSP Residual norm 9.017480191500e+03
14 KSP Residual norm 8.415336139732e+03
15 KSP Residual norm 7.807497808414e+03
16 KSP Residual norm 7.341703768300e+03
17 KSP Residual norm 6.979298049244e+03
18 KSP Residual norm 6.521277772042e+03
19 KSP Residual norm 6.174842408713e+03
20 KSP Residual norm 5.889819664983e+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 = precond matrix:
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
1 SNES Function norm 1.000525348435e+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=2
norm schedule ALWAYS
Jacobian is never rebuilt
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 = precond matrix:
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
0 SNES Function norm 1.000525348435e+04
0 KSP Residual norm 1.000525348435e+04
1 KSP Residual norm 7.908741565645e+03
2 KSP Residual norm 6.825263536988e+03
3 KSP Residual norm 6.224930664967e+03
4 KSP Residual norm 6.095547180474e+03
5 KSP Residual norm 5.952968230397e+03
6 KSP Residual norm 5.861251998127e+03
7 KSP Residual norm 5.712439327726e+03
8 KSP Residual norm 5.583056913167e+03
9 KSP Residual norm 5.461768804526e+03
10 KSP Residual norm 5.351937611030e+03
11 KSP Residual norm 5.224288337536e+03
12 KSP Residual norm 5.129863847028e+03
13 KSP Residual norm 5.010818237161e+03
14 KSP Residual norm 4.907162936143e+03
15 KSP Residual norm 4.789564773923e+03
16 KSP Residual norm 4.695173370709e+03
17 KSP Residual norm 4.584070962145e+03
18 KSP Residual norm 4.483061424714e+03
19 KSP Residual norm 4.373384070713e+03
20 KSP Residual norm 4.260704657576e+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 = precond matrix:
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
1 SNES Function norm 4.662386014874e+03
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=2
norm schedule ALWAYS
Jacobian is never rebuilt
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 = precond matrix:
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
0 SNES Function norm 4.662386014874e+03
0 KSP Residual norm 4.662386014874e+03
1 KSP Residual norm 4.408316259834e+03
2 KSP Residual norm 4.184867769891e+03
3 KSP Residual norm 4.079091244367e+03
4 KSP Residual norm 4.009247390184e+03
5 KSP Residual norm 3.928417371457e+03
6 KSP Residual norm 3.865152075802e+03
7 KSP Residual norm 3.795606446041e+03
8 KSP Residual norm 3.735294554160e+03
9 KSP Residual norm 3.674393726485e+03
10 KSP Residual norm 3.617795166775e+03
11 KSP Residual norm 3.563807982249e+03
12 KSP Residual norm 3.512269444873e+03
13 KSP Residual norm 3.455110223193e+03
14 KSP Residual norm 3.407141247334e+03
15 KSP Residual norm 3.356562415949e+03
16 KSP Residual norm 3.312720047652e+03
17 KSP Residual norm 3.263690150782e+03
18 KSP Residual norm 3.219359862425e+03
19 KSP Residual norm 3.173500955997e+03
20 KSP Residual norm 3.127528790156e+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 = precond matrix:
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
1 SNES Function norm 3.186752172503e+03
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=2
norm schedule ALWAYS
Jacobian is never rebuilt
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 = precond matrix:
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 5:22 PM Matthew Knepley <knepley at gmail.com> wrote:
> On Thu, May 4, 2023 at 5:03 PM Mark Lohry <mlohry at 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 <knepley at gmail.com> wrote:
>>
>>> 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/>
>>>
>>
>
> --
> 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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