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?
Try -pc_type none. If the first KSP residual is different, then it is something in the MatMFFD, and then I would guess it is something variable in the residual evaluation routine. That should be easy to check by printing norms of the residual each time it is evaluated. Thanks, Matt
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/>