Do you get different results (in different runs) without -snes_mf_operator? So just using an explicit 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.) Barry > On May 4, 2023, at 4:43 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. > > 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/>
