I checked with -ksp_view (attached) but no prefix is associated with the matrix. Some are associated to the KSP and PC, but none to the Mat.
Le 03/26/19 à 11:55, Dave May a écrit : > > > On Tue, 26 Mar 2019 at 10:36, Myriam Peyrounette > <myriam.peyroune...@idris.fr <mailto:myriam.peyroune...@idris.fr>> wrote: > > Oh you were right, the three options are unsused (-matptap_via > scalable, -inner_offdiag_matmatmult_via scalable and > -inner_diag_matmatmult_via scalable). Does this mean I am not > using the associated PtAP functions? > > > No - not necessarily. All it means is the options were not parsed. > > If your matrices have an option prefix associated with them (e.g. abc) > , then you need to provide the option as > -abc_matptap_via scalable > > If you are not sure if you matrices have a prefix, look at the result > of -ksp_view (see below for an example) > > Mat Object: 2 MPI processes > > type: mpiaij > > rows=363, cols=363, bs=3 > > total: nonzeros=8649, allocated nonzeros=8649 > > total number of mallocs used during MatSetValues calls =0 > > Mat Object: (B_) 2 MPI processes > > type: mpiaij > > rows=363, cols=363, bs=3 > > total: nonzeros=8649, allocated nonzeros=8649 > > total number of mallocs used during MatSetValues calls =0 > > > The first matrix has no options prefix, but the second does and it's > called "B_". > > > > > > Myriam > > > Le 03/26/19 à 11:10, Dave May a écrit : >> >> On Tue, 26 Mar 2019 at 09:52, Myriam Peyrounette via petsc-users >> <petsc-users@mcs.anl.gov <mailto:petsc-users@mcs.anl.gov>> wrote: >> >> How can I be sure they are indeed used? Can I print this >> information in some log file? >> >> Yes. Re-run the job with the command line option >> >> -options_left true >> >> This will report all options parsed, and importantly, will also >> indicate if any options were unused. >> >> >> Thanks >> Dave >> >> Thanks in advance >> >> Myriam >> >> >> Le 03/25/19 à 18:24, Matthew Knepley a écrit : >>> On Mon, Mar 25, 2019 at 10:54 AM Myriam Peyrounette via >>> petsc-users <petsc-users@mcs.anl.gov >>> <mailto:petsc-users@mcs.anl.gov>> wrote: >>> >>> Hi, >>> >>> thanks for the explanations. I tried the last PETSc >>> version (commit >>> fbc5705bc518d02a4999f188aad4ccff5f754cbf), which >>> includes the patch you talked about. But the memory >>> scaling shows no improvement (see scaling attached), >>> even when using the "scalable" options :( >>> >>> I had a look at the PETSc functions >>> MatPtAPNumeric_MPIAIJ_MPIAIJ and >>> MatPtAPSymbolic_MPIAIJ_MPIAIJ (especially at the >>> differences before and after the first "bad" commit), >>> but I can't find what induced this memory issue. >>> >>> Are you sure that the option was used? It just looks >>> suspicious to me that they use exactly the same amount of >>> memory. It should be different, even if it does not solve >>> the problem. >>> >>> Thanks, >>> >>> Matt >>> >>> Myriam >>> >>> >>> >>> >>> Le 03/20/19 à 17:38, Fande Kong a écrit : >>>> Hi Myriam, >>>> >>>> There are three algorithms in PETSc to do PtAP ( const >>>> char *algTypes[3] = >>>> {"scalable","nonscalable","hypre"};), and can be >>>> specified using the petsc options: -matptap_via xxxx. >>>> >>>> (1) -matptap_via hypre: This call the hypre package to >>>> do the PtAP trough an all-at-once triple product. In >>>> our experiences, it is the most memory efficient, but >>>> could be slow. >>>> >>>> (2) -matptap_via scalable: This involves a row-wise >>>> algorithm plus an outer product. This will use more >>>> memory than hypre, but way faster. This used to have a >>>> bug that could take all your memory, and I have a fix >>>> at >>>> https://bitbucket.org/petsc/petsc/pull-requests/1452/mpiptap-enable-large-scale-simulations/diff. >>>> >>>> When using this option, we may want to have extra >>>> options such as -inner_offdiag_matmatmult_via >>>> scalable -inner_diag_matmatmult_via scalable to select >>>> inner scalable algorithms. >>>> >>>> (3) -matptap_via nonscalable: Suppose to be even >>>> faster, but use more memory. It does dense matrix >>>> operations. >>>> >>>> >>>> Thanks, >>>> >>>> Fande Kong >>>> >>>> >>>> >>>> >>>> On Wed, Mar 20, 2019 at 10:06 AM Myriam Peyrounette via >>>> petsc-users <petsc-users@mcs.anl.gov >>>> <mailto:petsc-users@mcs.anl.gov>> wrote: >>>> >>>> More precisely: something happens when upgrading >>>> the functions MatPtAPNumeric_MPIAIJ_MPIAIJ and/or >>>> MatPtAPSymbolic_MPIAIJ_MPIAIJ. >>>> >>>> Unfortunately, there are a lot of differences >>>> between the old and new versions of these >>>> functions. I keep investigating but if you have any >>>> idea, please let me know. >>>> >>>> Best, >>>> >>>> Myriam >>>> >>>> >>>> Le 03/20/19 à 13:48, Myriam Peyrounette a écrit : >>>>> >>>>> Hi all, >>>>> >>>>> I used git bisect to determine when the memory >>>>> need increased. I found that the first "bad" >>>>> commit is aa690a28a7284adb519c28cb44eae20a2c131c85. >>>>> >>>>> Barry was right, this commit seems to be about an >>>>> evolution of MatPtAPSymbolic_MPIAIJ_MPIAIJ. You >>>>> mentioned the option "-matptap_via scalable" but I >>>>> can't find any information about it. Can you tell >>>>> me more? >>>>> >>>>> Thanks >>>>> >>>>> Myriam >>>>> >>>>> >>>>> Le 03/11/19 à 14:40, Mark Adams a écrit : >>>>>> Is there a difference in memory usage on your >>>>>> tiny problem? I assume no. >>>>>> >>>>>> I don't see anything that could come from GAMG >>>>>> other than the RAP stuff that you have discussed >>>>>> already. >>>>>> >>>>>> On Mon, Mar 11, 2019 at 9:32 AM Myriam >>>>>> Peyrounette <myriam.peyroune...@idris.fr >>>>>> <mailto:myriam.peyroune...@idris.fr>> wrote: >>>>>> >>>>>> The code I am using here is the example 42 of >>>>>> PETSc >>>>>> >>>>>> (https://www.mcs.anl.gov/petsc/petsc-3.9/src/ksp/ksp/examples/tutorials/ex42.c.html). >>>>>> Indeed it solves the Stokes equation. I >>>>>> thought it was a good idea to use an example >>>>>> you might know (and didn't find any that uses >>>>>> GAMG functions). I just changed the PCMG >>>>>> setup so that the memory problem appears. And >>>>>> it appears when adding PCGAMG. >>>>>> >>>>>> I don't care about the performance or even >>>>>> the result rightness here, but only about the >>>>>> difference in memory use between 3.6 and >>>>>> 3.10. Do you think finding a more adapted >>>>>> script would help? >>>>>> >>>>>> I used the threshold of 0.1 only once, at the >>>>>> beginning, to test its influence. I used the >>>>>> default threshold (of 0, I guess) for all the >>>>>> other runs. >>>>>> >>>>>> Myriam >>>>>> >>>>>> >>>>>> Le 03/11/19 à 13:52, Mark Adams a écrit : >>>>>>> In looking at this larger scale run ... >>>>>>> >>>>>>> * Your eigen estimates are much lower than >>>>>>> your tiny test problem. But this is Stokes >>>>>>> apparently and it should not work anyway. >>>>>>> Maybe you have a small time step that adds a >>>>>>> lot of mass that brings the eigen estimates >>>>>>> down. And your min eigenvalue (not used) is >>>>>>> positive. I would expect negative for Stokes ... >>>>>>> >>>>>>> * You seem to be setting a threshold value >>>>>>> of 0.1 -- that is very high >>>>>>> >>>>>>> * v3.6 says "using nonzero initial guess" >>>>>>> but this is not in v3.10. Maybe we just >>>>>>> stopped printing that. >>>>>>> >>>>>>> * There were some changes to coasening >>>>>>> parameters in going from v3.6 but it does >>>>>>> not look like your problem was effected. >>>>>>> (The coarsening algo is non-deterministic by >>>>>>> default and you can see small difference on >>>>>>> different runs) >>>>>>> >>>>>>> * We may have also added a "noisy" RHS for >>>>>>> eigen estimates by default from v3.6. >>>>>>> >>>>>>> * And for non-symetric problems you can try >>>>>>> -pc_gamg_agg_nsmooths 0, but again GAMG is >>>>>>> not built for Stokes anyway. >>>>>>> >>>>>>> >>>>>>> On Tue, Mar 5, 2019 at 11:53 AM Myriam >>>>>>> Peyrounette <myriam.peyroune...@idris.fr >>>>>>> <mailto:myriam.peyroune...@idris.fr>> wrote: >>>>>>> >>>>>>> I used PCView to display the size of the >>>>>>> linear system in each level of the MG. >>>>>>> You'll find the outputs attached to this >>>>>>> mail (zip file) for both the default >>>>>>> threshold value and a value of 0.1, and >>>>>>> for both 3.6 and 3.10 PETSc versions. >>>>>>> >>>>>>> For convenience, I summarized the >>>>>>> information in a graph, also attached >>>>>>> (png file). >>>>>>> >>>>>>> As you can see, there are slight >>>>>>> differences between the two versions but >>>>>>> none is critical, in my opinion. Do you >>>>>>> see anything suspicious in the outputs? >>>>>>> >>>>>>> + I can't find the default threshold >>>>>>> value. Do you know where I can find it? >>>>>>> >>>>>>> Thanks for the follow-up >>>>>>> >>>>>>> Myriam >>>>>>> >>>>>>> >>>>>>> Le 03/05/19 à 14:06, Matthew Knepley a >>>>>>> écrit : >>>>>>>> On Tue, Mar 5, 2019 at 7:14 AM Myriam >>>>>>>> Peyrounette >>>>>>>> <myriam.peyroune...@idris.fr >>>>>>>> <mailto:myriam.peyroune...@idris.fr>> >>>>>>>> wrote: >>>>>>>> >>>>>>>> Hi Matt, >>>>>>>> >>>>>>>> I plotted the memory scalings using >>>>>>>> different threshold values. The two >>>>>>>> scalings are slightly translated >>>>>>>> (from -22 to -88 mB) but this gain >>>>>>>> is neglectable. The 3.6-scaling >>>>>>>> keeps being robust while the >>>>>>>> 3.10-scaling deteriorates. >>>>>>>> >>>>>>>> Do you have any other suggestion? >>>>>>>> >>>>>>>> Mark, what is the option she can give >>>>>>>> to output all the GAMG data? >>>>>>>> >>>>>>>> Also, run using -ksp_view. GAMG will >>>>>>>> report all the sizes of its grids, so >>>>>>>> it should be easy to see >>>>>>>> if the coarse grid sizes are >>>>>>>> increasing, and also what the effect of >>>>>>>> the threshold value is. >>>>>>>> >>>>>>>> Thanks, >>>>>>>> >>>>>>>> Matt >>>>>>>> >>>>>>>> Thanks >>>>>>>> >>>>>>>> Myriam >>>>>>>> >>>>>>>> Le 03/02/19 à 02:27, Matthew >>>>>>>> Knepley a écrit : >>>>>>>>> On Fri, Mar 1, 2019 at 10:53 AM >>>>>>>>> Myriam Peyrounette via petsc-users >>>>>>>>> <petsc-users@mcs.anl.gov >>>>>>>>> <mailto:petsc-users@mcs.anl.gov>> >>>>>>>>> wrote: >>>>>>>>> >>>>>>>>> Hi, >>>>>>>>> >>>>>>>>> I used to run my code with >>>>>>>>> PETSc 3.6. Since I upgraded >>>>>>>>> the PETSc version >>>>>>>>> to 3.10, this code has a bad >>>>>>>>> memory scaling. >>>>>>>>> >>>>>>>>> To report this issue, I took >>>>>>>>> the PETSc script ex42.c and >>>>>>>>> slightly >>>>>>>>> modified it so that the KSP >>>>>>>>> and PC configurations are the >>>>>>>>> same as in my >>>>>>>>> code. In particular, I use a >>>>>>>>> "personnalised" multi-grid >>>>>>>>> method. The >>>>>>>>> modifications are indicated by >>>>>>>>> the keyword "TopBridge" in the >>>>>>>>> attached >>>>>>>>> scripts. >>>>>>>>> >>>>>>>>> To plot the memory (weak) >>>>>>>>> scaling, I ran four >>>>>>>>> calculations for each >>>>>>>>> script with increasing problem >>>>>>>>> sizes and computations cores: >>>>>>>>> >>>>>>>>> 1. 100,000 elts on 4 cores >>>>>>>>> 2. 1 million elts on 40 cores >>>>>>>>> 3. 10 millions elts on 400 cores >>>>>>>>> 4. 100 millions elts on 4,000 >>>>>>>>> cores >>>>>>>>> >>>>>>>>> The resulting graph is also >>>>>>>>> attached. The scaling using >>>>>>>>> PETSc 3.10 >>>>>>>>> clearly deteriorates for large >>>>>>>>> cases, while the one using >>>>>>>>> PETSc 3.6 is >>>>>>>>> robust. >>>>>>>>> >>>>>>>>> After a few tests, I found >>>>>>>>> that the scaling is mostly >>>>>>>>> sensitive to the >>>>>>>>> use of the AMG method for the >>>>>>>>> coarse grid (line 1780 in >>>>>>>>> main_ex42_petsc36.cc). In >>>>>>>>> particular, the performance >>>>>>>>> strongly >>>>>>>>> deteriorates when commenting >>>>>>>>> lines 1777 to 1790 (in >>>>>>>>> main_ex42_petsc36.cc). >>>>>>>>> >>>>>>>>> Do you have any idea of what >>>>>>>>> changed between version 3.6 >>>>>>>>> and version >>>>>>>>> 3.10 that may imply such >>>>>>>>> degradation? >>>>>>>>> >>>>>>>>> >>>>>>>>> I believe the default values for >>>>>>>>> PCGAMG changed between versions. >>>>>>>>> It sounds like the coarsening rate >>>>>>>>> is not great enough, so that these >>>>>>>>> grids are too large. This can be >>>>>>>>> set using: >>>>>>>>> >>>>>>>>> >>>>>>>>> https://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/PC/PCGAMGSetThreshold.html >>>>>>>>> >>>>>>>>> There is some explanation of this >>>>>>>>> effect on that page. Let us know >>>>>>>>> if setting this does not correct >>>>>>>>> the situation. >>>>>>>>> >>>>>>>>> Thanks, >>>>>>>>> >>>>>>>>> Matt >>>>>>>>> >>>>>>>>> >>>>>>>>> Let me know if you need >>>>>>>>> further information. >>>>>>>>> >>>>>>>>> Best, >>>>>>>>> >>>>>>>>> Myriam Peyrounette >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> Myriam Peyrounette >>>>>>>>> CNRS/IDRIS - HLST >>>>>>>>> -- >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> 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/%7Eknepley/> >>>>>>>> >>>>>>>> -- >>>>>>>> Myriam Peyrounette >>>>>>>> CNRS/IDRIS - HLST >>>>>>>> -- >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> 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/%7Eknepley/> >>>>>>> >>>>>>> -- >>>>>>> Myriam Peyrounette >>>>>>> CNRS/IDRIS - HLST >>>>>>> -- >>>>>>> >>>>>> >>>>>> -- >>>>>> Myriam Peyrounette >>>>>> CNRS/IDRIS - HLST >>>>>> -- >>>>>> >>>>> >>>>> -- >>>>> Myriam Peyrounette >>>>> CNRS/IDRIS - HLST >>>>> -- >>>> >>>> -- >>>> Myriam Peyrounette >>>> CNRS/IDRIS - HLST >>>> -- >>>> >>> >>> -- >>> Myriam Peyrounette >>> CNRS/IDRIS - HLST >>> -- >>> >>> >>> >>> -- >>> 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/%7Eknepley/> >> >> -- >> Myriam Peyrounette >> CNRS/IDRIS - HLST >> -- >> > > -- > Myriam Peyrounette > CNRS/IDRIS - HLST > -- > -- Myriam Peyrounette CNRS/IDRIS - HLST --
KSP Object: 8 MPI processes
type: gmres
GMRES: restart=150, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement
GMRES: happy breakdown tolerance 1e-30
maximum iterations=150
tolerances: relative=1e-05, absolute=1e-50, divergence=1e+15
left preconditioning
using nonzero initial guess
using PRECONDITIONED norm type for convergence test
PC Object: 8 MPI processes
type: mg
MG: type is MULTIPLICATIVE, levels=2 cycles=v
Cycles per PCApply=1
Using Galerkin computed coarse grid matrices
Coarse grid solver -- level -------------------------------
KSP Object: (mg_coarse_) 8 MPI processes
type: cg
maximum iterations=200, initial guess is zero
tolerances: relative=1e-08, absolute=1e-50, divergence=1000
left preconditioning
using NONE norm type for convergence test
PC Object: (mg_coarse_) 8 MPI processes
type: gamg
MG: type is MULTIPLICATIVE, levels=4 cycles=v
Cycles per PCApply=1
Using Galerkin computed coarse grid matrices
GAMG specific options
Threshold for dropping small values from graph 0
AGG specific options
Symmetric graph false
Coarse grid solver -- level -------------------------------
KSP Object: (mg_coarse_mg_coarse_) 8 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=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_mg_coarse_) 8 MPI processes
type: bjacobi
block Jacobi: number of blocks = 8
Local solve is same for all blocks, in the following KSP and PC objects:
KSP Object: (mg_coarse_mg_coarse_sub_) 1 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_mg_coarse_sub_) 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 [INBLOCKS]
matrix ordering: nd
factor fill ratio given 5, needed 1
Factored matrix follows:
Mat Object: 1 MPI processes
type: seqaij
rows=18, cols=18, bs=3
package used to perform factorization: petsc
total: nonzeros=324, allocated nonzeros=324
total number of mallocs used during MatSetValues calls =0
using I-node routines: found 4 nodes, limit used is 5
linear system matrix = precond matrix:
Mat Object: 1 MPI processes
type: seqaij
rows=18, cols=18, bs=3
total: nonzeros=324, allocated nonzeros=324
total number of mallocs used during MatSetValues calls =0
using I-node routines: found 4 nodes, limit used is 5
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=18, cols=18, bs=3
total: nonzeros=324, allocated nonzeros=324
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 4 nodes, limit used is 5
Down solver (pre-smoother) on level 1 -------------------------------
KSP Object: (mg_coarse_mg_levels_1_) 8 MPI processes
type: chebyshev
Chebyshev: eigenvalue estimates: min = 0.151062, max = 1.66169
Chebyshev: eigenvalues estimated using gmres with translations [0 0.1; 0 1.1]
KSP Object: (mg_coarse_mg_levels_1_esteig_) 8 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=10, initial guess is zero
tolerances: relative=1e-05, absolute=1e-50, divergence=10000
left preconditioning
using NONE norm type for convergence test
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_coarse_mg_levels_1_) 8 MPI processes
type: sor
SOR: type = local_symmetric, iterations = 1, local iterations = 1, omega = 1
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=111, cols=111, bs=3
total: nonzeros=5661, allocated nonzeros=5661
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 19 nodes, limit used is 5
Up solver (post-smoother) same as down solver (pre-smoother)
Down solver (pre-smoother) on level 2 -------------------------------
KSP Object: (mg_coarse_mg_levels_2_) 8 MPI processes
type: chebyshev
Chebyshev: eigenvalue estimates: min = 0.176955, max = 1.94651
Chebyshev: eigenvalues estimated using gmres with translations [0 0.1; 0 1.1]
KSP Object: (mg_coarse_mg_levels_2_esteig_) 8 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=10, initial guess is zero
tolerances: relative=1e-05, absolute=1e-50, divergence=10000
left preconditioning
using NONE norm type for convergence test
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_coarse_mg_levels_2_) 8 MPI processes
type: sor
SOR: type = local_symmetric, iterations = 1, local iterations = 1, omega = 1
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=1239, cols=1239, bs=3
total: nonzeros=82125, allocated nonzeros=82125
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 47 nodes, limit used is 5
Up solver (post-smoother) same as down solver (pre-smoother)
Down solver (pre-smoother) on level 3 -------------------------------
KSP Object: (mg_coarse_mg_levels_3_) 8 MPI processes
type: chebyshev
Chebyshev: eigenvalue estimates: min = 0.170478, max = 1.87526
Chebyshev: eigenvalues estimated using gmres with translations [0 0.1; 0 1.1]
KSP Object: (mg_coarse_mg_levels_3_esteig_) 8 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=10, initial guess is zero
tolerances: relative=1e-05, absolute=1e-50, divergence=10000
left preconditioning
using NONE norm type for convergence test
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_coarse_mg_levels_3_) 8 MPI processes
type: sor
SOR: type = local_symmetric, iterations = 1, local iterations = 1, omega = 1
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=46314, cols=46314, bs=3
total: nonzeros=3.23669e+06, allocated nonzeros=3.23669e+06
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 2016 nodes, limit used is 5
Up solver (post-smoother) same as down solver (pre-smoother)
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=46314, cols=46314, bs=3
total: nonzeros=3.23669e+06, allocated nonzeros=3.23669e+06
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 2016 nodes, limit used is 5
Down solver (pre-smoother) on level 1 -------------------------------
KSP Object: (mg_levels_1_) 8 MPI processes
type: chebyshev
Chebyshev: eigenvalue estimates: min = 0.176403, max = 1.94043
Chebyshev: eigenvalues estimated using gmres with translations [0 0.1; 0 1.1]
KSP Object: (mg_levels_1_esteig_) 8 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=10, initial guess is zero
tolerances: relative=1e-05, absolute=1e-50, divergence=10000
left preconditioning
using NONE norm type for convergence test
maximum iterations=4
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_) 8 MPI processes
type: sor
SOR: type = local_symmetric, iterations = 1, local iterations = 1, omega = 1
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=332145, cols=332145, bs=3
total: nonzeros=2.4896e+07, allocated nonzeros=2.4896e+07
total number of mallocs used during MatSetValues calls =0
Up solver (post-smoother) same as down solver (pre-smoother)
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=332145, cols=332145, bs=3
total: nonzeros=2.4896e+07, allocated nonzeros=2.4896e+07
total number of mallocs used during MatSetValues calls =0
KSP Object: 8 MPI processes
type: gmres
GMRES: restart=150, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement
GMRES: happy breakdown tolerance 1e-30
maximum iterations=150
tolerances: relative=1e-05, absolute=1e-50, divergence=1e+15
left preconditioning
using nonzero initial guess
using PRECONDITIONED norm type for convergence test
PC Object: 8 MPI processes
type: mg
MG: type is MULTIPLICATIVE, levels=2 cycles=v
Cycles per PCApply=1
Using Galerkin computed coarse grid matrices
Coarse grid solver -- level -------------------------------
KSP Object: (mg_coarse_) 8 MPI processes
type: cg
maximum iterations=200, initial guess is zero
tolerances: relative=1e-08, absolute=1e-50, divergence=1000
left preconditioning
using NONE norm type for convergence test
PC Object: (mg_coarse_) 8 MPI processes
type: gamg
MG: type is MULTIPLICATIVE, levels=4 cycles=v
Cycles per PCApply=1
Using Galerkin computed coarse grid matrices
GAMG specific options
Threshold for dropping small values from graph 0
AGG specific options
Symmetric graph false
Coarse grid solver -- level -------------------------------
KSP Object: (mg_coarse_mg_coarse_) 8 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=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_mg_coarse_) 8 MPI processes
type: bjacobi
block Jacobi: number of blocks = 8
Local solve is same for all blocks, in the following KSP and PC objects:
KSP Object: (mg_coarse_mg_coarse_sub_) 1 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_mg_coarse_sub_) 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 [INBLOCKS]
matrix ordering: nd
factor fill ratio given 5, needed 1
Factored matrix follows:
Mat Object: 1 MPI processes
type: seqaij
rows=18, cols=18, bs=3
package used to perform factorization: petsc
total: nonzeros=324, allocated nonzeros=324
total number of mallocs used during MatSetValues calls =0
using I-node routines: found 4 nodes, limit used is 5
linear system matrix = precond matrix:
Mat Object: 1 MPI processes
type: seqaij
rows=18, cols=18, bs=3
total: nonzeros=324, allocated nonzeros=324
total number of mallocs used during MatSetValues calls =0
using I-node routines: found 4 nodes, limit used is 5
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=18, cols=18, bs=3
total: nonzeros=324, allocated nonzeros=324
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 4 nodes, limit used is 5
Down solver (pre-smoother) on level 1 -------------------------------
KSP Object: (mg_coarse_mg_levels_1_) 8 MPI processes
type: chebyshev
Chebyshev: eigenvalue estimates: min = 0.152589, max = 1.67848
Chebyshev: eigenvalues estimated using gmres with translations [0 0.1; 0 1.1]
KSP Object: (mg_coarse_mg_levels_1_esteig_) 8 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=10, initial guess is zero
tolerances: relative=1e-05, absolute=1e-50, divergence=10000
left preconditioning
using NONE norm type for convergence test
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_coarse_mg_levels_1_) 8 MPI processes
type: sor
SOR: type = local_symmetric, iterations = 1, local iterations = 1, omega = 1
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=111, cols=111, bs=3
total: nonzeros=5661, allocated nonzeros=5661
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 19 nodes, limit used is 5
Up solver (post-smoother) same as down solver (pre-smoother)
Down solver (pre-smoother) on level 2 -------------------------------
KSP Object: (mg_coarse_mg_levels_2_) 8 MPI processes
type: chebyshev
Chebyshev: eigenvalue estimates: min = 0.177057, max = 1.94762
Chebyshev: eigenvalues estimated using gmres with translations [0 0.1; 0 1.1]
KSP Object: (mg_coarse_mg_levels_2_esteig_) 8 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=10, initial guess is zero
tolerances: relative=1e-05, absolute=1e-50, divergence=10000
left preconditioning
using NONE norm type for convergence test
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_coarse_mg_levels_2_) 8 MPI processes
type: sor
SOR: type = local_symmetric, iterations = 1, local iterations = 1, omega = 1
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=1239, cols=1239, bs=3
total: nonzeros=82125, allocated nonzeros=82125
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 47 nodes, limit used is 5
Up solver (post-smoother) same as down solver (pre-smoother)
Down solver (pre-smoother) on level 3 -------------------------------
KSP Object: (mg_coarse_mg_levels_3_) 8 MPI processes
type: chebyshev
Chebyshev: eigenvalue estimates: min = 0.160141, max = 1.76155
Chebyshev: eigenvalues estimated using gmres with translations [0 0.1; 0 1.1]
KSP Object: (mg_coarse_mg_levels_3_esteig_) 8 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=10, initial guess is zero
tolerances: relative=1e-05, absolute=1e-50, divergence=10000
left preconditioning
using NONE norm type for convergence test
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_coarse_mg_levels_3_) 8 MPI processes
type: sor
SOR: type = local_symmetric, iterations = 1, local iterations = 1, omega = 1
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=46314, cols=46314, bs=3
total: nonzeros=3.23669e+06, allocated nonzeros=3.23669e+06
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 2016 nodes, limit used is 5
Up solver (post-smoother) same as down solver (pre-smoother)
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=46314, cols=46314, bs=3
total: nonzeros=3.23669e+06, allocated nonzeros=3.23669e+06
total number of mallocs used during MatSetValues calls =0
using I-node (on process 0) routines: found 2016 nodes, limit used is 5
Down solver (pre-smoother) on level 1 -------------------------------
KSP Object: (mg_levels_1_) 8 MPI processes
type: chebyshev
Chebyshev: eigenvalue estimates: min = 0.149352, max = 1.64288
Chebyshev: eigenvalues estimated using gmres with translations [0 0.1; 0 1.1]
KSP Object: (mg_levels_1_esteig_) 8 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=10, initial guess is zero
tolerances: relative=1e-05, absolute=1e-50, divergence=10000
left preconditioning
using NONE norm type for convergence test
maximum iterations=4
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_) 8 MPI processes
type: sor
SOR: type = local_symmetric, iterations = 1, local iterations = 1, omega = 1
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=332145, cols=332145, bs=3
total: nonzeros=2.4896e+07, allocated nonzeros=2.4896e+07
total number of mallocs used during MatSetValues calls =0
Up solver (post-smoother) same as down solver (pre-smoother)
linear system matrix = precond matrix:
Mat Object: 8 MPI processes
type: mpiaij
rows=332145, cols=332145, bs=3
total: nonzeros=2.4896e+07, allocated nonzeros=2.4896e+07
total number of mallocs used during MatSetValues calls =0
smime.p7s
Description: Signature cryptographique S/MIME
