[petsc-users] sources of floating point randomness in JFNK in serial
Mark Lohry
mlohry at gmail.com
Fri May 5 15:45:28 CDT 2023
wow. leaving -O3 and turning off -march=native seems to have made it
repeatable. this is on an avx2 cpu if it matters.
out-of-order instructions may be performed thus, two runs may have
> different order of operations
>
>
this is terrifying if true. the source code path is exactly the same every
time but the cpu does different things?
On Fri, May 5, 2023 at 10:55 AM Barry Smith <bsmith at petsc.dev> wrote:
>
> Mark,
>
> Thank you. You do have aggressive optimizations: -O3 -march=native,
> which means out-of-order instructions may be performed thus, two runs may
> have different order of operations and possibly different round-off values.
>
> You could try turning off all of this with -O0 for an experiment and see
> what happens. My guess is that you will see much smaller differences in the
> residuals.
>
> Barry
>
>
> On May 5, 2023, at 8:11 AM, Mark Lohry <mlohry at gmail.com> wrote:
>
>
>
> On Thu, May 4, 2023 at 9:51 PM Barry Smith <bsmith at petsc.dev> wrote:
>
>>
>> Send configure.log
>>
>>
>> On May 4, 2023, at 5:35 PM, Mark Lohry <mlohry at gmail.com> wrote:
>>
>> 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/>
>>>
>>
>> <configure.log>
>
>
>
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