[petsc-users] Problem with AMG packages
Mark F. Adams
mfadams at lbl.gov
Tue Oct 8 22:37:47 CDT 2013
On Oct 8, 2013, at 5:46 PM, Pierre Jolivet <jolivet at ann.jussieu.fr> wrote:
> Please find the log for BoomerAMG, ML and GAMG attached. The set up for
> GAMG doesn't look so bad compared to the other packages, so I'm wondering
> what is going on with those ?
They all have serious problems. It just happens that GAMG's problems are not as bad as the others.
>
>>
>> We need the output from running with -log_summary -pc_mg_log
>>
>> Also you can run with PETSc's AMG called GAMG (run with -pc_type gamg)
>> This will give the most useful information about where it is spending
>> the time.
>>
>>
>> Barry
>>
>>
>> On Oct 8, 2013, at 4:11 PM, Pierre Jolivet <jolivet at ann.jussieu.fr> wrote:
>>
>>> Dear all,
>>> I'm trying to compare linear solvers for a simple Poisson equation in
>>> 3D.
>>> I thought that MG was the way to go, but looking at my log, the
>>> performance looks abysmal (I know that the matrices are way too small
>>> but
>>> if I go bigger, it just never performs a single iteration ..). Even
>>> though
>>> this is neither the BoomerAMG nor the ML mailing list, could you please
>>> tell me if PETSc sets some default flags that make the setup for those
>>> solvers so slow for this simple problem ? The performance of (G)ASM is
>>> in
>>> comparison much better.
>>>
>>> Thanks in advance for your help.
>>>
>>> PS: first the BoomerAMG log, then ML (much more verbose, sorry).
>>>
>>> 0 KSP Residual norm 1.599647112604e+00
>>> 1 KSP Residual norm 5.450838232404e-02
>>> 2 KSP Residual norm 3.549673478318e-03
>>> 3 KSP Residual norm 2.901826808841e-04
>>> 4 KSP Residual norm 2.574235778729e-05
>>> 5 KSP Residual norm 2.253410171682e-06
>>> 6 KSP Residual norm 1.871067784877e-07
>>> 7 KSP Residual norm 1.681162800670e-08
>>> 8 KSP Residual norm 2.120841512414e-09
>>> KSP Object: 2048 MPI processes
>>> type: gmres
>>> GMRES: restart=30, using Classical (unmodified) Gram-Schmidt
>>> Orthogonalization with no iterative refinement
>>> GMRES: happy breakdown tolerance 1e-30
>>> maximum iterations=200, initial guess is zero
>>> tolerances: relative=1e-08, absolute=1e-50, divergence=10000
>>> left preconditioning
>>> using PRECONDITIONED norm type for convergence test
>>> PC Object: 2048 MPI processes
>>> type: hypre
>>> HYPRE BoomerAMG preconditioning
>>> HYPRE BoomerAMG: Cycle type V
>>> HYPRE BoomerAMG: Maximum number of levels 25
>>> HYPRE BoomerAMG: Maximum number of iterations PER hypre call 1
>>> HYPRE BoomerAMG: Convergence tolerance PER hypre call 0
>>> HYPRE BoomerAMG: Threshold for strong coupling 0.25
>>> HYPRE BoomerAMG: Interpolation truncation factor 0
>>> HYPRE BoomerAMG: Interpolation: max elements per row 0
>>> HYPRE BoomerAMG: Number of levels of aggressive coarsening 0
>>> HYPRE BoomerAMG: Number of paths for aggressive coarsening 1
>>> HYPRE BoomerAMG: Maximum row sums 0.9
>>> HYPRE BoomerAMG: Sweeps down 1
>>> HYPRE BoomerAMG: Sweeps up 1
>>> HYPRE BoomerAMG: Sweeps on coarse 1
>>> HYPRE BoomerAMG: Relax down symmetric-SOR/Jacobi
>>> HYPRE BoomerAMG: Relax up symmetric-SOR/Jacobi
>>> HYPRE BoomerAMG: Relax on coarse Gaussian-elimination
>>> HYPRE BoomerAMG: Relax weight (all) 1
>>> HYPRE BoomerAMG: Outer relax weight (all) 1
>>> HYPRE BoomerAMG: Using CF-relaxation
>>> HYPRE BoomerAMG: Measure type local
>>> HYPRE BoomerAMG: Coarsen type Falgout
>>> HYPRE BoomerAMG: Interpolation type classical
>>> linear system matrix = precond matrix:
>>> Matrix Object: 2048 MPI processes
>>> type: mpiaij
>>> rows=4173281, cols=4173281
>>> total: nonzeros=102576661, allocated nonzeros=102576661
>>> total number of mallocs used during MatSetValues calls =0
>>> not using I-node (on process 0) routines
>>> --- system solved with PETSc (in 1.005199e+02 seconds)
>>>
>>> 0 KSP Residual norm 2.368804472986e-01
>>> 1 KSP Residual norm 5.676430019132e-02
>>> 2 KSP Residual norm 1.898005876002e-02
>>> 3 KSP Residual norm 6.193922902926e-03
>>> 4 KSP Residual norm 2.008448794493e-03
>>> 5 KSP Residual norm 6.390465670228e-04
>>> 6 KSP Residual norm 2.157709394389e-04
>>> 7 KSP Residual norm 7.295973819979e-05
>>> 8 KSP Residual norm 2.358343271482e-05
>>> 9 KSP Residual norm 7.489696222066e-06
>>> 10 KSP Residual norm 2.390946857593e-06
>>> 11 KSP Residual norm 8.068086385140e-07
>>> 12 KSP Residual norm 2.706607789749e-07
>>> 13 KSP Residual norm 8.636910863376e-08
>>> 14 KSP Residual norm 2.761981175852e-08
>>> 15 KSP Residual norm 8.755459874369e-09
>>> 16 KSP Residual norm 2.708848598341e-09
>>> 17 KSP Residual norm 8.968748876265e-10
>>> KSP Object: 2048 MPI processes
>>> type: gmres
>>> GMRES: restart=30, using Classical (unmodified) Gram-Schmidt
>>> Orthogonalization with no iterative refinement
>>> GMRES: happy breakdown tolerance 1e-30
>>> maximum iterations=200, initial guess is zero
>>> tolerances: relative=1e-08, absolute=1e-50, divergence=10000
>>> left preconditioning
>>> using PRECONDITIONED norm type for convergence test
>>> PC Object: 2048 MPI processes
>>> type: ml
>>> MG: type is MULTIPLICATIVE, levels=3 cycles=v
>>> Cycles per PCApply=1
>>> Using Galerkin computed coarse grid matrices
>>> Coarse grid solver -- level -------------------------------
>>> KSP Object: (mg_coarse_) 2048 MPI processes
>>> type: preonly
>>> maximum iterations=1, initial guess is zero
>>> tolerances: relative=1e-05, absolute=1e-50, divergence=10000
>>> left preconditioning
>>> using NONE norm type for convergence test
>>> PC Object: (mg_coarse_) 2048 MPI processes
>>> type: redundant
>>> Redundant preconditioner: First (color=0) of 2048 PCs follows
>>> KSP Object: (mg_coarse_redundant_) 1 MPI processes
>>> 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: (mg_coarse_redundant_) 1 MPI processes
>>> type: lu
>>> LU: out-of-place factorization
>>> tolerance for zero pivot 2.22045e-14
>>> using diagonal shift on blocks to prevent zero pivot
>>> matrix ordering: nd
>>> factor fill ratio given 5, needed 4.38504
>>> Factored matrix follows:
>>> Matrix Object: 1 MPI processes
>>> type: seqaij
>>> rows=2055, cols=2055
>>> package used to perform factorization: petsc
>>> total: nonzeros=2476747, allocated nonzeros=2476747
>>> total number of mallocs used during MatSetValues calls =0
>>> using I-node routines: found 1638 nodes, limit used is
>>> 5
>>> linear system matrix = precond matrix:
>>> Matrix Object: 1 MPI processes
>>> type: seqaij
>>> rows=2055, cols=2055
>>> total: nonzeros=564817, allocated nonzeros=1093260
>>> total number of mallocs used during MatSetValues calls =0
>>> not using I-node routines
>>> linear system matrix = precond matrix:
>>> Matrix Object: 2048 MPI processes
>>> type: mpiaij
>>> rows=2055, cols=2055
>>> total: nonzeros=564817, allocated nonzeros=564817
>>> total number of mallocs used during MatSetValues calls =0
>>> not using I-node (on process 0) routines
>>> Down solver (pre-smoother) on level 1 -------------------------------
>>> KSP Object: (mg_levels_1_) 2048 MPI processes
>>> type: richardson
>>> Richardson: damping factor=1
>>> maximum iterations=2
>>> tolerances: relative=1e-05, absolute=1e-50, divergence=10000
>>> left preconditioning
>>> using nonzero initial guess
>>> using NONE norm type for convergence test
>>> PC Object: (mg_levels_1_) 2048 MPI processes
>>> type: sor
>>> SOR: type = local_symmetric, iterations = 1, local iterations =
>>> 1,
>>> omega = 1
>>> linear system matrix = precond matrix:
>>> Matrix Object: 2048 MPI processes
>>> type: mpiaij
>>> rows=30194, cols=30194
>>> total: nonzeros=3368414, allocated nonzeros=3368414
>>> total number of mallocs used during MatSetValues calls =0
>>> not using I-node (on process 0) routines
>>> Up solver (post-smoother) same as down solver (pre-smoother)
>>> Down solver (pre-smoother) on level 2 -------------------------------
>>> KSP Object: (mg_levels_2_) 2048 MPI processes
>>> type: richardson
>>> Richardson: damping factor=1
>>> maximum iterations=2
>>> tolerances: relative=1e-05, absolute=1e-50, divergence=10000
>>> left preconditioning
>>> using nonzero initial guess
>>> using NONE norm type for convergence test
>>> PC Object: (mg_levels_2_) 2048 MPI processes
>>> type: sor
>>> SOR: type = local_symmetric, iterations = 1, local iterations =
>>> 1,
>>> omega = 1
>>> linear system matrix = precond matrix:
>>> Matrix Object: 2048 MPI processes
>>> type: mpiaij
>>> rows=531441, cols=531441
>>> total: nonzeros=12476324, allocated nonzeros=12476324
>>> total number of mallocs used during MatSetValues calls =0
>>> not using I-node (on process 0) routines
>>> Up solver (post-smoother) same as down solver (pre-smoother)
>>> linear system matrix = precond matrix:
>>> Matrix Object: 2048 MPI processes
>>> type: mpiaij
>>> rows=531441, cols=531441
>>> total: nonzeros=12476324, allocated nonzeros=12476324
>>> total number of mallocs used during MatSetValues calls =0
>>> not using I-node (on process 0) routines
>>> --- system solved with PETSc (in 2.407844e+02 seconds)
>>>
>>>
>>
>>
> <log-GAMG><log-ML><log-BoomerAMG>
More information about the petsc-users
mailing list