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> 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] > <mailto:[email protected]>> wrote: >> On Thu, May 4, 2023 at 5:03 PM Mark Lohry <[email protected] >> <mailto:[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] >>> <mailto:[email protected]>> wrote: >>>> On Thu, May 4, 2023 at 4:44 PM Mark Lohry <[email protected] >>>> <mailto:[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] >>>>> <mailto:[email protected]>> wrote: >>>>>> On Thu, May 4, 2023 at 8:54 AM Mark Lohry <[email protected] >>>>>> <mailto:[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] >>>>>>> <mailto:[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] >>>>>>>> <mailto:[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] >>>>>>>>> <mailto:[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] >>>>>>>>>> <mailto:[email protected]>> wrote: >>>>>>>>>>> On Thu, May 4, 2023 at 8:21 AM Mark Lohry <[email protected] >>>>>>>>>>> <mailto:[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] >>>>>>>>>>>> <mailto:[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] >>>>>>>>>>>>>> <mailto:[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] >>>>>>>>>>>>>> <mailto:[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] >>>>>>>>>>>>>>> > <mailto:[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/>
