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 <[email protected]> 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 <[email protected]> wrote: > > > > On Thu, May 4, 2023 at 9:51 PM Barry Smith <[email protected]> wrote: > >> >> Send configure.log >> >> >> On May 4, 2023, at 5:35 PM, Mark Lohry <[email protected]> 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 <[email protected]> wrote: >> >>> On Thu, May 4, 2023 at 5:03 PM Mark Lohry <[email protected]> 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 <[email protected]> >>>> wrote: >>>> >>>>> On Thu, May 4, 2023 at 4:44 PM Mark Lohry <[email protected]> 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 <[email protected]> >>>>>> wrote: >>>>>> >>>>>>> On Thu, May 4, 2023 at 8:54 AM Mark Lohry <[email protected]> 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 <[email protected]> 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 <[email protected]> >>>>>>>>> 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 <[email protected]> >>>>>>>>>> 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 < >>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>> >>>>>>>>>>>> On Thu, May 4, 2023 at 8:21 AM Mark Lohry <[email protected]> >>>>>>>>>>>> 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 <[email protected]> >>>>>>>>>>>>> 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 <[email protected]> >>>>>>>>>>>>>> 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 <[email protected]> >>>>>>>>>>>>>> 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 <[email protected]> >>>>>>>>>>>>>>> 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> > > >
