[petsc-users] sources of floating point randomness in JFNK in serial
Mark Lohry
mlohry at gmail.com
Fri May 5 07:11:37 CDT 2023
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/>
>>
>
>
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