[petsc-users] Using PCFIELDSPLIT with -pc_fieldsplit_type schur
Matthew Knepley
knepley at gmail.com
Thu Jan 12 05:16:19 CST 2017
On Wed, Jan 11, 2017 at 10:37 PM, David Knezevic <david.knezevic at akselos.com
> wrote:
> On Wed, Jan 11, 2017 at 9:55 PM, Barry Smith <bsmith at mcs.anl.gov> wrote:
>
>>
>> That is disappointing,
>>
>> Please try using
>>
>> -pc_fieldsplit_schur_precondition full
>>
>> with the two cases of -fieldsplit_FE_split_pc_type gamg and
>> -fieldsplit_FE_split_pc_type cholesky
>>
>>
> One more data point: The initial mesh I tried had some somewhat bad
> quality elements. I tried some other cases that have better conditioned
> meshes (nicely shaped hex elements), and using ILU(0) for A11 worked well
> in those cases. So certainly the conditioning of A11 appears to play a
> significant role here (not surprising).
>
>
> Regarding --pc_fieldsplit_schur_precondition full: I must have my
> SetFromOptions call in the wrong place, because I can't get
> "-pc_fieldsplit_schur_precondition full" to have an effect (I'll look
> into that some more).
>
> However, I was able to use PCFieldSplitSetSchurPre to set the
> schur_precondition property via code. That worked with A11, SELF, SELFP,
> but when I did:
>
> PCFieldSplitSetSchurPre (pc, PC_FIELDSPLIT_SCHUR_PRE_FULL, NULL);
>
> I got an error:
> [0]PETSC ERROR: No support for this operation for this object type
> [0]PETSC ERROR: Not yet implemented for Schur complements with
> non-vanishing D
>
Barry fixed this, but it might only be in th 3.7.5
Thanks,
Matt
> David
>
>
> > On Jan 11, 2017, at 8:49 PM, David Knezevic <david.knezevic at akselos.com>
>> wrote:
>> >
>> > OK, that's encouraging. However, OK, that's encouraging. However,
>> regarding this:
>> >
>> > So the next step is to try using -fieldsplit_FE_split_ksp_monitor
>> -fieldsplit_FE_split_pc_type gamg
>> >
>> > I tried this and it didn't converge at all (it hit the 10000 iteration
>> max in the output from -fieldsplit_FE_split_ksp_monitor). So I guess
>> I'd need to attach the near nullspace to make this work reasonably, as you
>> said. Sounds like that may not be easy to do in this case though? I'll try
>> some other preconditioners in the meantime.
>> >
>> > Thanks,
>> > David
>> >
>> >
>> > On Wed, Jan 11, 2017 at 9:31 PM, Barry Smith <bsmith at mcs.anl.gov>
>> wrote:
>> >
>> > Thanks, this is very useful information. It means that
>> >
>> > 1) the approximate Sp is actually a very good approximation to the true
>> Schur complement S, since using Sp^-1 to precondition S gives iteration
>> counts from 8 to 13.
>> >
>> > 2) using ilu(0) as a preconditioner for Sp is not good, since
>> replacing Sp^-1 with ilu(0) of Sp gives absurd iteration counts. This is
>> actually not super surprising since ilu(0) is generally "not so good" for
>> elasticity.
>> >
>> > So the next step is to try using -fieldsplit_FE_split_ksp_monitor
>> -fieldsplit_FE_split_pc_type gamg
>> >
>> > the one open question is if any options should be passed to the gamg to
>> tell it that the underly problem comes from "elasticity"; that is something
>> about the null space.
>> >
>> > Mark Adams, since the GAMG is coming from inside another
>> preconditioner it may not be easy for the easy for the user to attach the
>> near null space to that inner matrix. Would it make sense for there to be a
>> GAMG command line option to indicate that it is a 3d elasticity problem so
>> GAMG could set up the near null space for itself? or does that not make
>> sense?
>> >
>> > Barry
>> >
>> >
>> >
>> > > On Jan 11, 2017, at 7:47 PM, David Knezevic <
>> david.knezevic at akselos.com> wrote:
>> > >
>> > > I've attached the two log files. Using cholesky for "FE_split" seems
>> to have helped a lot!
>> > >
>> > > David
>> > >
>> > >
>> > > --
>> > > David J. Knezevic | CTO
>> > > Akselos | 210 Broadway, #201 | Cambridge, MA | 02139
>> > >
>> > > Phone: +1-617-599-4755
>> > >
>> > > This e-mail and any attachments may contain confidential material for
>> the sole use of the intended recipient(s). Any review or distribution by
>> others is strictly prohibited. If you are not the intended recipient,
>> please contact the sender and delete all copies.
>> > >
>> > > On Wed, Jan 11, 2017 at 8:32 PM, Barry Smith <bsmith at mcs.anl.gov>
>> wrote:
>> > >
>> > > Can you please run with all the monitoring on? So we can see the
>> convergence of all the inner solvers
>> > > -fieldsplit_FE_split_ksp_monitor
>> > >
>> > > Then run again with
>> > >
>> > > -fieldsplit_FE_split_ksp_monitor -fieldsplit_FE_split_pc_type
>> cholesky
>> > >
>> > >
>> > > and send both sets of results
>> > >
>> > > Barry
>> > >
>> > >
>> > > > On Jan 11, 2017, at 6:32 PM, David Knezevic <
>> david.knezevic at akselos.com> wrote:
>> > > >
>> > > > On Wed, Jan 11, 2017 at 5:52 PM, Dave May <dave.mayhem23 at gmail.com>
>> wrote:
>> > > > so I gather that I'll have to look into a user-defined
>> approximation to S.
>> > > >
>> > > > Where does the 2x2 block system come from?
>> > > > Maybe someone on the list knows the right approximation to use for
>> S.
>> > > >
>> > > > The model is 3D linear elasticity using a finite element
>> discretization. I applied substructuring to part of the system to
>> "condense" it, and that results in the small A00 block. The A11 block is
>> just standard 3D elasticity; no substructuring was applied there. There are
>> constraints to connect the degrees of freedom on the interface of the
>> substructured and non-substructured regions.
>> > > >
>> > > > If anyone has suggestions for a good way to precondition this type
>> of system, I'd be most appreciative!
>> > > >
>> > > > Thanks,
>> > > > David
>> > > >
>> > > >
>> > > >
>> > > > -----------------------------------------
>> > > >
>> > > > 0 KSP Residual norm 5.405528187695e+04
>> > > > 1 KSP Residual norm 2.187814910803e+02
>> > > > 2 KSP Residual norm 1.019051577515e-01
>> > > > 3 KSP Residual norm 4.370464012859e-04
>> > > > KSP Object: 1 MPI processes
>> > > > type: cg
>> > > > maximum iterations=1000
>> > > > tolerances: relative=1e-06, absolute=1e-50, divergence=10000.
>> > > > left preconditioning
>> > > > using nonzero initial guess
>> > > > using PRECONDITIONED norm type for convergence test
>> > > > PC Object: 1 MPI processes
>> > > > type: fieldsplit
>> > > > FieldSplit with Schur preconditioner, factorization FULL
>> > > > Preconditioner for the Schur complement formed from Sp, an
>> assembled approximation to S, which uses (lumped, if requested) A00's
>> diagonal's inverse
>> > > > Split info:
>> > > > Split number 0 Defined by IS
>> > > > Split number 1 Defined by IS
>> > > > KSP solver for A00 block
>> > > > KSP Object: (fieldsplit_RB_split_) 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: (fieldsplit_RB_split_) 1 MPI processes
>> > > > type: cholesky
>> > > > Cholesky: out-of-place factorization
>> > > > tolerance for zero pivot 2.22045e-14
>> > > > matrix ordering: natural
>> > > > factor fill ratio given 0., needed 0.
>> > > > Factored matrix follows:
>> > > > Mat Object: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=324, cols=324
>> > > > package used to perform factorization: mumps
>> > > > total: nonzeros=3042, allocated nonzeros=3042
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > MUMPS run parameters:
>> > > > SYM (matrix type): 2
>> > > > PAR (host participation): 1
>> > > > ICNTL(1) (output for error): 6
>> > > > ICNTL(2) (output of diagnostic msg): 0
>> > > > ICNTL(3) (output for global info): 0
>> > > > ICNTL(4) (level of printing): 0
>> > > > ICNTL(5) (input mat struct): 0
>> > > > ICNTL(6) (matrix prescaling): 7
>> > > > ICNTL(7) (sequentia matrix ordering):7
>> > > > ICNTL(8) (scalling strategy): 77
>> > > > ICNTL(10) (max num of refinements): 0
>> > > > ICNTL(11) (error analysis): 0
>> > > > ICNTL(12) (efficiency control):
>> 0
>> > > > ICNTL(13) (efficiency control):
>> 0
>> > > > ICNTL(14) (percentage of estimated workspace
>> increase): 20
>> > > > ICNTL(18) (input mat struct):
>> 0
>> > > > ICNTL(19) (Shur complement info):
>> 0
>> > > > ICNTL(20) (rhs sparse pattern):
>> 0
>> > > > ICNTL(21) (solution struct):
>> 0
>> > > > ICNTL(22) (in-core/out-of-core facility):
>> 0
>> > > > ICNTL(23) (max size of memory can be allocated
>> locally):0
>> > > > ICNTL(24) (detection of null pivot rows):
>> 0
>> > > > ICNTL(25) (computation of a null space basis):
>> 0
>> > > > ICNTL(26) (Schur options for rhs or solution):
>> 0
>> > > > ICNTL(27) (experimental parameter):
>> -24
>> > > > ICNTL(28) (use parallel or sequential
>> ordering): 1
>> > > > ICNTL(29) (parallel ordering):
>> 0
>> > > > ICNTL(30) (user-specified set of entries in
>> inv(A)): 0
>> > > > ICNTL(31) (factors is discarded in the solve
>> phase): 0
>> > > > ICNTL(33) (compute determinant):
>> 0
>> > > > CNTL(1) (relative pivoting threshold): 0.01
>> > > > CNTL(2) (stopping criterion of refinement):
>> 1.49012e-08
>> > > > CNTL(3) (absolute pivoting threshold): 0.
>> > > > CNTL(4) (value of static pivoting): -1.
>> > > > CNTL(5) (fixation for null pivots): 0.
>> > > > RINFO(1) (local estimated flops for the
>> elimination after analysis):
>> > > > [0] 29394.
>> > > > RINFO(2) (local estimated flops for the
>> assembly after factorization):
>> > > > [0] 1092.
>> > > > RINFO(3) (local estimated flops for the
>> elimination after factorization):
>> > > > [0] 29394.
>> > > > INFO(15) (estimated size of (in MB) MUMPS
>> internal data for running numerical factorization):
>> > > > [0] 1
>> > > > INFO(16) (size of (in MB) MUMPS internal data
>> used during numerical factorization):
>> > > > [0] 1
>> > > > INFO(23) (num of pivots eliminated on this
>> processor after factorization):
>> > > > [0] 324
>> > > > RINFOG(1) (global estimated flops for the
>> elimination after analysis): 29394.
>> > > > RINFOG(2) (global estimated flops for the
>> assembly after factorization): 1092.
>> > > > RINFOG(3) (global estimated flops for the
>> elimination after factorization): 29394.
>> > > > (RINFOG(12) RINFOG(13))*2^INFOG(34)
>> (determinant): (0.,0.)*(2^0)
>> > > > INFOG(3) (estimated real workspace for factors
>> on all processors after analysis): 3888
>> > > > INFOG(4) (estimated integer workspace for
>> factors on all processors after analysis): 2067
>> > > > INFOG(5) (estimated maximum front size in the
>> complete tree): 12
>> > > > INFOG(6) (number of nodes in the complete
>> tree): 53
>> > > > INFOG(7) (ordering option effectively use after
>> analysis): 2
>> > > > INFOG(8) (structural symmetry in percent of the
>> permuted matrix after analysis): 100
>> > > > INFOG(9) (total real/complex workspace to store
>> the matrix factors after factorization): 3888
>> > > > INFOG(10) (total integer space store the matrix
>> factors after factorization): 2067
>> > > > INFOG(11) (order of largest frontal matrix
>> after factorization): 12
>> > > > INFOG(12) (number of off-diagonal pivots): 0
>> > > > INFOG(13) (number of delayed pivots after
>> factorization): 0
>> > > > INFOG(14) (number of memory compress after
>> factorization): 0
>> > > > INFOG(15) (number of steps of iterative
>> refinement after solution): 0
>> > > > INFOG(16) (estimated size (in MB) of all MUMPS
>> internal data for factorization after analysis: value on the most memory
>> consuming processor): 1
>> > > > INFOG(17) (estimated size of all MUMPS internal
>> data for factorization after analysis: sum over all processors): 1
>> > > > INFOG(18) (size of all MUMPS internal data
>> allocated during factorization: value on the most memory consuming
>> processor): 1
>> > > > INFOG(19) (size of all MUMPS internal data
>> allocated during factorization: sum over all processors): 1
>> > > > INFOG(20) (estimated number of entries in the
>> factors): 3042
>> > > > INFOG(21) (size in MB of memory effectively
>> used during factorization - value on the most memory consuming processor): 1
>> > > > INFOG(22) (size in MB of memory effectively
>> used during factorization - sum over all processors): 1
>> > > > INFOG(23) (after analysis: value of ICNTL(6)
>> effectively used): 5
>> > > > INFOG(24) (after analysis: value of ICNTL(12)
>> effectively used): 1
>> > > > INFOG(25) (after factorization: number of
>> pivots modified by static pivoting): 0
>> > > > INFOG(28) (after factorization: number of null
>> pivots encountered): 0
>> > > > INFOG(29) (after factorization: effective
>> number of entries in the factors (sum over all processors)): 3042
>> > > > INFOG(30, 31) (after solution: size in Mbytes
>> of memory used during solution phase): 0, 0
>> > > > INFOG(32) (after analysis: type of analysis
>> done): 1
>> > > > INFOG(33) (value used for ICNTL(8)): -2
>> > > > INFOG(34) (exponent of the determinant if
>> determinant is requested): 0
>> > > > linear system matrix = precond matrix:
>> > > > Mat Object: (fieldsplit_RB_split_) 1 MPI
>> processes
>> > > > type: seqaij
>> > > > rows=324, cols=324
>> > > > total: nonzeros=5760, allocated nonzeros=5760
>> > > > total number of mallocs used during MatSetValues calls =0
>> > > > using I-node routines: found 108 nodes, limit used is 5
>> > > > KSP solver for S = A11 - A10 inv(A00) A01
>> > > > KSP Object: (fieldsplit_FE_split_) 1 MPI processes
>> > > > type: cg
>> > > > maximum iterations=10000, initial guess is zero
>> > > > tolerances: relative=1e-05, absolute=1e-50,
>> divergence=10000.
>> > > > left preconditioning
>> > > > using PRECONDITIONED norm type for convergence test
>> > > > PC Object: (fieldsplit_FE_split_) 1 MPI processes
>> > > > type: bjacobi
>> > > > block Jacobi: number of blocks = 1
>> > > > Local solve is same for all blocks, in the following KSP
>> and PC objects:
>> > > > KSP Object: (fieldsplit_FE_split_sub_)
>> 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: (fieldsplit_FE_split_sub_)
>> 1 MPI processes
>> > > > type: ilu
>> > > > 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: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=28476
>> > > > package used to perform factorization: petsc
>> > > > total: nonzeros=1037052, allocated
>> nonzeros=1037052
>> > > > total number of mallocs used during
>> MatSetValues calls =0
>> > > > using I-node routines: found 9489 nodes,
>> limit used is 5
>> > > > linear system matrix = precond matrix:
>> > > > Mat Object: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=28476
>> > > > total: nonzeros=1037052, allocated nonzeros=1037052
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 9489 nodes, limit used
>> is 5
>> > > > linear system matrix followed by preconditioner matrix:
>> > > > Mat Object: (fieldsplit_FE_split_) 1 MPI
>> processes
>> > > > type: schurcomplement
>> > > > rows=28476, cols=28476
>> > > > Schur complement A11 - A10 inv(A00) A01
>> > > > A11
>> > > > Mat Object: (fieldsplit_FE_split_)
>> 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=28476
>> > > > total: nonzeros=1017054, allocated nonzeros=1017054
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 9492 nodes, limit
>> used is 5
>> > > > A10
>> > > > Mat Object: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=324
>> > > > total: nonzeros=936, allocated nonzeros=936
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 5717 nodes, limit
>> used is 5
>> > > > KSP of A00
>> > > > KSP Object: (fieldsplit_RB_split_)
>> 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: (fieldsplit_RB_split_)
>> 1 MPI processes
>> > > > type: cholesky
>> > > > Cholesky: out-of-place factorization
>> > > > tolerance for zero pivot 2.22045e-14
>> > > > matrix ordering: natural
>> > > > factor fill ratio given 0., needed 0.
>> > > > Factored matrix follows:
>> > > > Mat Object: 1 MPI
>> processes
>> > > > type: seqaij
>> > > > rows=324, cols=324
>> > > > package used to perform factorization: mumps
>> > > > total: nonzeros=3042, allocated
>> nonzeros=3042
>> > > > total number of mallocs used during
>> MatSetValues calls =0
>> > > > MUMPS run parameters:
>> > > > SYM (matrix type): 2
>> > > > PAR (host participation): 1
>> > > > ICNTL(1) (output for error): 6
>> > > > ICNTL(2) (output of diagnostic msg): 0
>> > > > ICNTL(3) (output for global info): 0
>> > > > ICNTL(4) (level of printing): 0
>> > > > ICNTL(5) (input mat struct): 0
>> > > > ICNTL(6) (matrix prescaling): 7
>> > > > ICNTL(7) (sequentia matrix ordering):7
>> > > > ICNTL(8) (scalling strategy): 77
>> > > > ICNTL(10) (max num of refinements): 0
>> > > > ICNTL(11) (error analysis): 0
>> > > > ICNTL(12) (efficiency control):
>> 0
>> > > > ICNTL(13) (efficiency control):
>> 0
>> > > > ICNTL(14) (percentage of estimated
>> workspace increase): 20
>> > > > ICNTL(18) (input mat struct):
>> 0
>> > > > ICNTL(19) (Shur complement info):
>> 0
>> > > > ICNTL(20) (rhs sparse pattern):
>> 0
>> > > > ICNTL(21) (solution struct):
>> 0
>> > > > ICNTL(22) (in-core/out-of-core
>> facility): 0
>> > > > ICNTL(23) (max size of memory can be
>> allocated locally):0
>> > > > ICNTL(24) (detection of null pivot
>> rows): 0
>> > > > ICNTL(25) (computation of a null space
>> basis): 0
>> > > > ICNTL(26) (Schur options for rhs or
>> solution): 0
>> > > > ICNTL(27) (experimental parameter):
>> -24
>> > > > ICNTL(28) (use parallel or sequential
>> ordering): 1
>> > > > ICNTL(29) (parallel ordering):
>> 0
>> > > > ICNTL(30) (user-specified set of
>> entries in inv(A)): 0
>> > > > ICNTL(31) (factors is discarded in the
>> solve phase): 0
>> > > > ICNTL(33) (compute determinant):
>> 0
>> > > > CNTL(1) (relative pivoting threshold):
>> 0.01
>> > > > CNTL(2) (stopping criterion of
>> refinement): 1.49012e-08
>> > > > CNTL(3) (absolute pivoting threshold):
>> 0.
>> > > > CNTL(4) (value of static pivoting):
>> -1.
>> > > > CNTL(5) (fixation for null pivots):
>> 0.
>> > > > RINFO(1) (local estimated flops for the
>> elimination after analysis):
>> > > > [0] 29394.
>> > > > RINFO(2) (local estimated flops for the
>> assembly after factorization):
>> > > > [0] 1092.
>> > > > RINFO(3) (local estimated flops for the
>> elimination after factorization):
>> > > > [0] 29394.
>> > > > INFO(15) (estimated size of (in MB)
>> MUMPS internal data for running numerical factorization):
>> > > > [0] 1
>> > > > INFO(16) (size of (in MB) MUMPS
>> internal data used during numerical factorization):
>> > > > [0] 1
>> > > > INFO(23) (num of pivots eliminated on
>> this processor after factorization):
>> > > > [0] 324
>> > > > RINFOG(1) (global estimated flops for
>> the elimination after analysis): 29394.
>> > > > RINFOG(2) (global estimated flops for
>> the assembly after factorization): 1092.
>> > > > RINFOG(3) (global estimated flops for
>> the elimination after factorization): 29394.
>> > > > (RINFOG(12) RINFOG(13))*2^INFOG(34)
>> (determinant): (0.,0.)*(2^0)
>> > > > INFOG(3) (estimated real workspace for
>> factors on all processors after analysis): 3888
>> > > > INFOG(4) (estimated integer workspace
>> for factors on all processors after analysis): 2067
>> > > > INFOG(5) (estimated maximum front size
>> in the complete tree): 12
>> > > > INFOG(6) (number of nodes in the
>> complete tree): 53
>> > > > INFOG(7) (ordering option effectively
>> use after analysis): 2
>> > > > INFOG(8) (structural symmetry in
>> percent of the permuted matrix after analysis): 100
>> > > > INFOG(9) (total real/complex workspace
>> to store the matrix factors after factorization): 3888
>> > > > INFOG(10) (total integer space store
>> the matrix factors after factorization): 2067
>> > > > INFOG(11) (order of largest frontal
>> matrix after factorization): 12
>> > > > INFOG(12) (number of off-diagonal
>> pivots): 0
>> > > > INFOG(13) (number of delayed pivots
>> after factorization): 0
>> > > > INFOG(14) (number of memory compress
>> after factorization): 0
>> > > > INFOG(15) (number of steps of iterative
>> refinement after solution): 0
>> > > > INFOG(16) (estimated size (in MB) of
>> all MUMPS internal data for factorization after analysis: value on the most
>> memory consuming processor): 1
>> > > > INFOG(17) (estimated size of all MUMPS
>> internal data for factorization after analysis: sum over all processors): 1
>> > > > INFOG(18) (size of all MUMPS internal
>> data allocated during factorization: value on the most memory consuming
>> processor): 1
>> > > > INFOG(19) (size of all MUMPS internal
>> data allocated during factorization: sum over all processors): 1
>> > > > INFOG(20) (estimated number of entries
>> in the factors): 3042
>> > > > INFOG(21) (size in MB of memory
>> effectively used during factorization - value on the most memory consuming
>> processor): 1
>> > > > INFOG(22) (size in MB of memory
>> effectively used during factorization - sum over all processors): 1
>> > > > INFOG(23) (after analysis: value of
>> ICNTL(6) effectively used): 5
>> > > > INFOG(24) (after analysis: value of
>> ICNTL(12) effectively used): 1
>> > > > INFOG(25) (after factorization: number
>> of pivots modified by static pivoting): 0
>> > > > INFOG(28) (after factorization: number
>> of null pivots encountered): 0
>> > > > INFOG(29) (after factorization:
>> effective number of entries in the factors (sum over all processors)): 3042
>> > > > INFOG(30, 31) (after solution: size in
>> Mbytes of memory used during solution phase): 0, 0
>> > > > INFOG(32) (after analysis: type of
>> analysis done): 1
>> > > > INFOG(33) (value used for ICNTL(8)): -2
>> > > > INFOG(34) (exponent of the determinant
>> if determinant is requested): 0
>> > > > linear system matrix = precond matrix:
>> > > > Mat Object: (fieldsplit_RB_split_)
>> 1 MPI processes
>> > > > type: seqaij
>> > > > rows=324, cols=324
>> > > > total: nonzeros=5760, allocated nonzeros=5760
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 108 nodes, limit
>> used is 5
>> > > > A01
>> > > > Mat Object: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=324, cols=28476
>> > > > total: nonzeros=936, allocated nonzeros=936
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 67 nodes, limit used
>> is 5
>> > > > Mat Object: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=28476
>> > > > total: nonzeros=1037052, allocated nonzeros=1037052
>> > > > total number of mallocs used during MatSetValues calls =0
>> > > > using I-node routines: found 9489 nodes, limit used is 5
>> > > > linear system matrix = precond matrix:
>> > > > Mat Object: () 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28800, cols=28800
>> > > > total: nonzeros=1024686, allocated nonzeros=1024794
>> > > > total number of mallocs used during MatSetValues calls =0
>> > > > using I-node routines: found 9600 nodes, limit used is 5
>> > > >
>> > > > ---------------------------------------------- PETSc Performance
>> Summary: ----------------------------------------------
>> > > >
>> > > > /home/dknez/akselos-dev/scrbe/build/bin/fe_solver-opt_real on a
>> arch-linux2-c-opt named david-Lenovo with 1 processor, by dknez Wed Jan 11
>> 17:22:10 2017
>> > > > Using Petsc Release Version 3.7.3, unknown
>> > > >
>> > > > Max Max/Min Avg Total
>> > > > Time (sec): 9.638e+01 1.00000 9.638e+01
>> > > > Objects: 2.030e+02 1.00000 2.030e+02
>> > > > Flops: 1.732e+11 1.00000 1.732e+11 1.732e+11
>> > > > Flops/sec: 1.797e+09 1.00000 1.797e+09 1.797e+09
>> > > > MPI Messages: 0.000e+00 0.00000 0.000e+00 0.000e+00
>> > > > MPI Message Lengths: 0.000e+00 0.00000 0.000e+00 0.000e+00
>> > > > MPI Reductions: 0.000e+00 0.00000
>> > > >
>> > > > Flop counting convention: 1 flop = 1 real number operation of type
>> (multiply/divide/add/subtract)
>> > > > e.g., VecAXPY() for real vectors of
>> length N --> 2N flops
>> > > > and VecAXPY() for complex vectors of
>> length N --> 8N flops
>> > > >
>> > > > Summary of Stages: ----- Time ------ ----- Flops ----- ---
>> Messages --- -- Message Lengths -- -- Reductions --
>> > > > Avg %Total Avg %Total counts
>> %Total Avg %Total counts %Total
>> > > > 0: Main Stage: 9.6379e+01 100.0% 1.7318e+11 100.0%
>> 0.000e+00 0.0% 0.000e+00 0.0% 0.000e+00 0.0%
>> > > >
>> > > > ------------------------------------------------------------
>> ------------------------------------------------------------
>> > > > See the 'Profiling' chapter of the users' manual for details on
>> interpreting output.
>> > > > Phase summary info:
>> > > > Count: number of times phase was executed
>> > > > Time and Flops: Max - maximum over all processors
>> > > > Ratio - ratio of maximum to minimum over all
>> processors
>> > > > Mess: number of messages sent
>> > > > Avg. len: average message length (bytes)
>> > > > Reduct: number of global reductions
>> > > > Global: entire computation
>> > > > Stage: stages of a computation. Set stages with
>> PetscLogStagePush() and PetscLogStagePop().
>> > > > %T - percent time in this phase %F - percent flops in
>> this phase
>> > > > %M - percent messages in this phase %L - percent message
>> lengths in this phase
>> > > > %R - percent reductions in this phase
>> > > > Total Mflop/s: 10e-6 * (sum of flops over all processors)/(max
>> time over all processors)
>> > > > ------------------------------------------------------------
>> ------------------------------------------------------------
>> > > > Event Count Time (sec) Flops
>> --- Global --- --- Stage --- Total
>> > > > Max Ratio Max Ratio Max Ratio Mess
>> Avg len Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s
>> > > > ------------------------------------------------------------
>> ------------------------------------------------------------
>> > > >
>> > > > --- Event Stage 0: Main Stage
>> > > >
>> > > > VecDot 42 1.0 2.2411e-05 1.0 8.53e+03 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 380
>> > > > VecTDot 77761 1.0 1.4294e+00 1.0 4.43e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 1 3 0 0 0 1 3 0 0 0 3098
>> > > > VecNorm 38894 1.0 9.1002e-01 1.0 2.22e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 1 1 0 0 0 1 1 0 0 0 2434
>> > > > VecScale 38882 1.0 3.7314e-01 1.0 1.11e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 1 0 0 0 0 1 0 0 0 2967
>> > > > VecCopy 38908 1.0 2.1655e-02 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > VecSet 77887 1.0 3.2034e-01 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > VecAXPY 77777 1.0 1.8382e+00 1.0 4.43e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 2 3 0 0 0 2 3 0 0 0 2409
>> > > > VecAYPX 38875 1.0 1.2884e+00 1.0 2.21e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 1 1 0 0 0 1 1 0 0 0 1718
>> > > > VecAssemblyBegin 68 1.0 1.9407e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > VecAssemblyEnd 68 1.0 2.6941e-05 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > VecScatterBegin 48 1.0 4.6349e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatMult 38891 1.0 4.3045e+01 1.0 8.03e+10 1.0 0.0e+00
>> 0.0e+00 0.0e+00 45 46 0 0 0 45 46 0 0 0 1866
>> > > > MatMultAdd 38889 1.0 3.5360e+01 1.0 7.91e+10 1.0 0.0e+00
>> 0.0e+00 0.0e+00 37 46 0 0 0 37 46 0 0 0 2236
>> > > > MatSolve 77769 1.0 4.8780e+01 1.0 7.95e+10 1.0 0.0e+00
>> 0.0e+00 0.0e+00 51 46 0 0 0 51 46 0 0 0 1631
>> > > > MatLUFactorNum 1 1.0 1.9575e-02 1.0 2.49e+07 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 1274
>> > > > MatCholFctrSym 1 1.0 9.4891e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatCholFctrNum 1 1.0 3.7885e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatILUFactorSym 1 1.0 4.1780e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatConvert 1 1.0 3.0041e-05 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatScale 2 1.0 2.7180e-05 1.0 2.53e+04 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 930
>> > > > MatAssemblyBegin 32 1.0 4.0531e-06 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatAssemblyEnd 32 1.0 1.2032e-02 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatGetRow 114978 1.0 5.9254e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatGetRowIJ 2 1.0 2.1458e-06 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatGetSubMatrice 6 1.0 1.5707e-02 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatGetOrdering 2 1.0 3.2425e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatZeroEntries 6 1.0 3.0580e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatView 7 1.0 3.5119e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatAXPY 1 1.0 1.9384e-02 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatMatMult 1 1.0 2.7120e-03 1.0 3.16e+05 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 117
>> > > > MatMatMultSym 1 1.0 1.8010e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatMatMultNum 1 1.0 6.1703e-04 1.0 3.16e+05 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 513
>> > > > KSPSetUp 4 1.0 9.8944e-05 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > KSPSolve 1 1.0 9.3380e+01 1.0 1.73e+11 1.0 0.0e+00
>> 0.0e+00 0.0e+00 97100 0 0 0 97100 0 0 0 1855
>> > > > PCSetUp 4 1.0 6.6326e-02 1.0 2.53e+07 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 381
>> > > > PCSetUpOnBlocks 5 1.0 2.4082e-02 1.0 2.49e+07 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 1036
>> > > > PCApply 5 1.0 9.3376e+01 1.0 1.73e+11 1.0 0.0e+00
>> 0.0e+00 0.0e+00 97100 0 0 0 97100 0 0 0 1855
>> > > > KSPSolve_FS_0 5 1.0 7.0214e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > KSPSolve_FS_Schu 5 1.0 9.3372e+01 1.0 1.73e+11 1.0 0.0e+00
>> 0.0e+00 0.0e+00 97100 0 0 0 97100 0 0 0 1855
>> > > > KSPSolve_FS_Low 5 1.0 2.1377e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > ------------------------------------------------------------
>> ------------------------------------------------------------
>> > > >
>> > > > Memory usage is given in bytes:
>> > > >
>> > > > Object Type Creations Destructions Memory
>> Descendants' Mem.
>> > > > Reports information only for process 0.
>> > > >
>> > > > --- Event Stage 0: Main Stage
>> > > >
>> > > > Vector 92 92 9698040 0.
>> > > > Vector Scatter 24 24 15936 0.
>> > > > Index Set 51 51 537876 0.
>> > > > IS L to G Mapping 3 3 240408 0.
>> > > > Matrix 16 16 77377776 0.
>> > > > Krylov Solver 6 6 7888 0.
>> > > > Preconditioner 6 6 6288 0.
>> > > > Viewer 1 0 0 0.
>> > > > Distributed Mesh 1 1 4624 0.
>> > > > Star Forest Bipartite Graph 2 2 1616 0.
>> > > > Discrete System 1 1 872 0.
>> > > > ============================================================
>> ============================================================
>> > > > Average time to get PetscTime(): 0.
>> > > > #PETSc Option Table entries:
>> > > > -ksp_monitor
>> > > > -ksp_view
>> > > > -log_view
>> > > > #End of PETSc Option Table entries
>> > > > Compiled without FORTRAN kernels
>> > > > Compiled with full precision matrices (default)
>> > > > sizeof(short) 2 sizeof(int) 4 sizeof(long) 8 sizeof(void*) 8
>> sizeof(PetscScalar) 8 sizeof(PetscInt) 4
>> > > > Configure options: --with-shared-libraries=1 --with-debugging=0
>> --download-suitesparse --download-blacs --download-ptscotch=yes
>> --with-blas-lapack-dir=/opt/intel/system_studio_2015.2.050/mkl
>> --CXXFLAGS=-Wl,--no-as-needed --download-scalapack --download-mumps
>> --download-metis --prefix=/home/dknez/software/libmesh_install/opt_real/petsc
>> --download-hypre --download-ml
>> > > > -----------------------------------------
>> > > > Libraries compiled on Wed Sep 21 17:38:52 2016 on david-Lenovo
>> > > > Machine characteristics: Linux-4.4.0-38-generic-x86_64-
>> with-Ubuntu-16.04-xenial
>> > > > Using PETSc directory: /home/dknez/software/petsc-src
>> > > > Using PETSc arch: arch-linux2-c-opt
>> > > > -----------------------------------------
>> > > >
>> > > > Using C compiler: mpicc -fPIC -Wall -Wwrite-strings
>> -Wno-strict-aliasing -Wno-unknown-pragmas -fvisibility=hidden -g -O
>> ${COPTFLAGS} ${CFLAGS}
>> > > > Using Fortran compiler: mpif90 -fPIC -Wall -ffree-line-length-0
>> -Wno-unused-dummy-argument -g -O ${FOPTFLAGS} ${FFLAGS}
>> > > > -----------------------------------------
>> > > >
>> > > > Using include paths: -I/home/dknez/software/petsc-src/arch-linux2-c-opt/include
>> -I/home/dknez/software/petsc-src/include -I/home/dknez/software/petsc-src/include
>> -I/home/dknez/software/petsc-src/arch-linux2-c-opt/include
>> -I/home/dknez/software/libmesh_install/opt_real/petsc/include
>> -I/usr/lib/openmpi/include/openmpi/opal/mca/event/libevent2021/libevent
>> -I/usr/lib/openmpi/include/openmpi/opal/mca/event/libevent2021/libevent/include
>> -I/usr/lib/openmpi/include -I/usr/lib/openmpi/include/openmpi
>> > > > -----------------------------------------
>> > > >
>> > > > Using C linker: mpicc
>> > > > Using Fortran linker: mpif90
>> > > > Using libraries: -Wl,-rpath,/home/dknez/softwar
>> e/petsc-src/arch-linux2-c-opt/lib -L/home/dknez/software/petsc-src/arch-linux2-c-opt/lib
>> -lpetsc -Wl,-rpath,/home/dknez/software/libmesh_install/opt_real/petsc/lib
>> -L/home/dknez/software/libmesh_install/opt_real/petsc/lib -lcmumps
>> -ldmumps -lsmumps -lzmumps -lmumps_common -lpord -lmetis -lHYPRE
>> -Wl,-rpath,/usr/lib/openmpi/lib -L/usr/lib/openmpi/lib
>> -Wl,-rpath,/usr/lib/gcc/x86_64-linux-gnu/5 -L/usr/lib/gcc/x86_64-linux-gnu/5
>> -Wl,-rpath,/usr/lib/x86_64-linux-gnu -L/usr/lib/x86_64-linux-gnu
>> -Wl,-rpath,/lib/x86_64-linux-gnu -L/lib/x86_64-linux-gnu -lmpi_cxx
>> -lstdc++ -lscalapack -lml -lmpi_cxx -lstdc++ -lumfpack -lklu -lcholmod
>> -lbtf -lccolamd -lcolamd -lcamd -lamd -lsuitesparseconfig
>> -Wl,-rpath,/opt/intel/system_studio_2015.2.050/mkl/lib/intel64
>> -L/opt/intel/system_studio_2015.2.050/mkl/lib/intel64 -lmkl_intel_lp64
>> -lmkl_sequential -lmkl_core -lpthread -lm -lhwloc -lptesmumps -lptscotch
>> -lptscotcherr -lscotch -lscotcherr -lX11 -lm -lmpi_usempif08
>> -lmpi_usempi_ignore_tkr -lmpi_mpifh -lgfortran -lm -lgfortran -lm
>> -lquadmath -lm -lmpi_cxx -lstdc++ -lrt -lm -lpthread -lz
>> -Wl,-rpath,/usr/lib/openmpi/lib -L/usr/lib/openmpi/lib
>> -Wl,-rpath,/usr/lib/gcc/x86_64-linux-gnu/5 -L/usr/lib/gcc/x86_64-linux-gnu/5
>> -Wl,-rpath,/usr/lib/x86_64-linux-gnu -L/usr/lib/x86_64-linux-gnu
>> -Wl,-rpath,/lib/x86_64-linux-gnu -L/lib/x86_64-linux-gnu
>> -Wl,-rpath,/usr/lib/x86_64-linux-gnu -L/usr/lib/x86_64-linux-gnu -ldl
>> -Wl,-rpath,/usr/lib/openmpi/lib -lmpi -lgcc_s -lpthread -ldl
>> > > > -----------------------------------------
>> > > >
>> > > >
>> > > >
>> > > >
>> > > > On Wed, Jan 11, 2017 at 4:49 PM, Dave May <dave.mayhem23 at gmail.com>
>> wrote:
>> > > > It looks like the Schur solve is requiring a huge number of
>> iterates to converge (based on the instances of MatMult).
>> > > > This is killing the performance.
>> > > >
>> > > > Are you sure that A11 is a good approximation to S? You might
>> consider trying the selfp option
>> > > >
>> > > > http://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/
>> PC/PCFieldSplitSetSchurPre.html#PCFieldSplitSetSchurPre
>> > > >
>> > > > Note that the best approx to S is likely both problem and
>> discretisation dependent so if selfp is also terrible, you might want to
>> consider coding up your own approx to S for your specific system.
>> > > >
>> > > >
>> > > > Thanks,
>> > > > Dave
>> > > >
>> > > >
>> > > > On Wed, 11 Jan 2017 at 22:34, David Knezevic <
>> david.knezevic at akselos.com> wrote:
>> > > > I have a definite block 2x2 system and I figured it'd be good to
>> apply the PCFIELDSPLIT functionality with Schur complement, as described in
>> Section 4.5 of the manual.
>> > > >
>> > > > The A00 block of my matrix is very small so I figured I'd specify a
>> direct solver (i.e. MUMPS) for that block.
>> > > >
>> > > > So I did the following:
>> > > > - PCFieldSplitSetIS to specify the indices of the two splits
>> > > > - PCFieldSplitGetSubKSP to get the two KSP objects, and to set the
>> solver and PC types for each (MUMPS for A00, ILU+CG for A11)
>> > > > - I set -pc_fieldsplit_schur_fact_type full
>> > > >
>> > > > Below I have pasted the output of "-ksp_view -ksp_monitor
>> -log_view" for a test case. It seems to converge well, but I'm concerned
>> about the speed (about 90 seconds, vs. about 1 second if I use a direct
>> solver for the entire system). I just wanted to check if I'm setting this
>> up in a good way?
>> > > >
>> > > > Many thanks,
>> > > > David
>> > > >
>> > > > ------------------------------------------------------------
>> -----------------------
>> > > >
>> > > > 0 KSP Residual norm 5.405774214400e+04
>> > > > 1 KSP Residual norm 1.849649014371e+02
>> > > > 2 KSP Residual norm 7.462775074989e-02
>> > > > 3 KSP Residual norm 2.680497175260e-04
>> > > > KSP Object: 1 MPI processes
>> > > > type: cg
>> > > > maximum iterations=1000
>> > > > tolerances: relative=1e-06, absolute=1e-50, divergence=10000.
>> > > > left preconditioning
>> > > > using nonzero initial guess
>> > > > using PRECONDITIONED norm type for convergence test
>> > > > PC Object: 1 MPI processes
>> > > > type: fieldsplit
>> > > > FieldSplit with Schur preconditioner, factorization FULL
>> > > > Preconditioner for the Schur complement formed from A11
>> > > > Split info:
>> > > > Split number 0 Defined by IS
>> > > > Split number 1 Defined by IS
>> > > > KSP solver for A00 block
>> > > > KSP Object: (fieldsplit_RB_split_) 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: (fieldsplit_RB_split_) 1 MPI processes
>> > > > type: cholesky
>> > > > Cholesky: out-of-place factorization
>> > > > tolerance for zero pivot 2.22045e-14
>> > > > matrix ordering: natural
>> > > > factor fill ratio given 0., needed 0.
>> > > > Factored matrix follows:
>> > > > Mat Object: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=324, cols=324
>> > > > package used to perform factorization: mumps
>> > > > total: nonzeros=3042, allocated nonzeros=3042
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > MUMPS run parameters:
>> > > > SYM (matrix type): 2
>> > > > PAR (host participation): 1
>> > > > ICNTL(1) (output for error): 6
>> > > > ICNTL(2) (output of diagnostic msg): 0
>> > > > ICNTL(3) (output for global info): 0
>> > > > ICNTL(4) (level of printing): 0
>> > > > ICNTL(5) (input mat struct): 0
>> > > > ICNTL(6) (matrix prescaling): 7
>> > > > ICNTL(7) (sequentia matrix ordering):7
>> > > > ICNTL(8) (scalling strategy): 77
>> > > > ICNTL(10) (max num of refinements): 0
>> > > > ICNTL(11) (error analysis): 0
>> > > > ICNTL(12) (efficiency control):
>> 0
>> > > > ICNTL(13) (efficiency control):
>> 0
>> > > > ICNTL(14) (percentage of estimated workspace
>> increase): 20
>> > > > ICNTL(18) (input mat struct):
>> 0
>> > > > ICNTL(19) (Shur complement info):
>> 0
>> > > > ICNTL(20) (rhs sparse pattern):
>> 0
>> > > > ICNTL(21) (solution struct):
>> 0
>> > > > ICNTL(22) (in-core/out-of-core facility):
>> 0
>> > > > ICNTL(23) (max size of memory can be allocated
>> locally):0
>> > > > ICNTL(24) (detection of null pivot rows):
>> 0
>> > > > ICNTL(25) (computation of a null space basis):
>> 0
>> > > > ICNTL(26) (Schur options for rhs or solution):
>> 0
>> > > > ICNTL(27) (experimental parameter):
>> -24
>> > > > ICNTL(28) (use parallel or sequential
>> ordering): 1
>> > > > ICNTL(29) (parallel ordering):
>> 0
>> > > > ICNTL(30) (user-specified set of entries in
>> inv(A)): 0
>> > > > ICNTL(31) (factors is discarded in the solve
>> phase): 0
>> > > > ICNTL(33) (compute determinant):
>> 0
>> > > > CNTL(1) (relative pivoting threshold): 0.01
>> > > > CNTL(2) (stopping criterion of refinement):
>> 1.49012e-08
>> > > > CNTL(3) (absolute pivoting threshold): 0.
>> > > > CNTL(4) (value of static pivoting): -1.
>> > > > CNTL(5) (fixation for null pivots): 0.
>> > > > RINFO(1) (local estimated flops for the
>> elimination after analysis):
>> > > > [0] 29394.
>> > > > RINFO(2) (local estimated flops for the
>> assembly after factorization):
>> > > > [0] 1092.
>> > > > RINFO(3) (local estimated flops for the
>> elimination after factorization):
>> > > > [0] 29394.
>> > > > INFO(15) (estimated size of (in MB) MUMPS
>> internal data for running numerical factorization):
>> > > > [0] 1
>> > > > INFO(16) (size of (in MB) MUMPS internal data
>> used during numerical factorization):
>> > > > [0] 1
>> > > > INFO(23) (num of pivots eliminated on this
>> processor after factorization):
>> > > > [0] 324
>> > > > RINFOG(1) (global estimated flops for the
>> elimination after analysis): 29394.
>> > > > RINFOG(2) (global estimated flops for the
>> assembly after factorization): 1092.
>> > > > RINFOG(3) (global estimated flops for the
>> elimination after factorization): 29394.
>> > > > (RINFOG(12) RINFOG(13))*2^INFOG(34)
>> (determinant): (0.,0.)*(2^0)
>> > > > INFOG(3) (estimated real workspace for factors
>> on all processors after analysis): 3888
>> > > > INFOG(4) (estimated integer workspace for
>> factors on all processors after analysis): 2067
>> > > > INFOG(5) (estimated maximum front size in the
>> complete tree): 12
>> > > > INFOG(6) (number of nodes in the complete
>> tree): 53
>> > > > INFOG(7) (ordering option effectively use after
>> analysis): 2
>> > > > INFOG(8) (structural symmetry in percent of the
>> permuted matrix after analysis): 100
>> > > > INFOG(9) (total real/complex workspace to store
>> the matrix factors after factorization): 3888
>> > > > INFOG(10) (total integer space store the matrix
>> factors after factorization): 2067
>> > > > INFOG(11) (order of largest frontal matrix
>> after factorization): 12
>> > > > INFOG(12) (number of off-diagonal pivots): 0
>> > > > INFOG(13) (number of delayed pivots after
>> factorization): 0
>> > > > INFOG(14) (number of memory compress after
>> factorization): 0
>> > > > INFOG(15) (number of steps of iterative
>> refinement after solution): 0
>> > > > INFOG(16) (estimated size (in MB) of all MUMPS
>> internal data for factorization after analysis: value on the most memory
>> consuming processor): 1
>> > > > INFOG(17) (estimated size of all MUMPS internal
>> data for factorization after analysis: sum over all processors): 1
>> > > > INFOG(18) (size of all MUMPS internal data
>> allocated during factorization: value on the most memory consuming
>> processor): 1
>> > > > INFOG(19) (size of all MUMPS internal data
>> allocated during factorization: sum over all processors): 1
>> > > > INFOG(20) (estimated number of entries in the
>> factors): 3042
>> > > > INFOG(21) (size in MB of memory effectively
>> used during factorization - value on the most memory consuming processor): 1
>> > > > INFOG(22) (size in MB of memory effectively
>> used during factorization - sum over all processors): 1
>> > > > INFOG(23) (after analysis: value of ICNTL(6)
>> effectively used): 5
>> > > > INFOG(24) (after analysis: value of ICNTL(12)
>> effectively used): 1
>> > > > INFOG(25) (after factorization: number of
>> pivots modified by static pivoting): 0
>> > > > INFOG(28) (after factorization: number of null
>> pivots encountered): 0
>> > > > INFOG(29) (after factorization: effective
>> number of entries in the factors (sum over all processors)): 3042
>> > > > INFOG(30, 31) (after solution: size in Mbytes
>> of memory used during solution phase): 0, 0
>> > > > INFOG(32) (after analysis: type of analysis
>> done): 1
>> > > > INFOG(33) (value used for ICNTL(8)): -2
>> > > > INFOG(34) (exponent of the determinant if
>> determinant is requested): 0
>> > > > linear system matrix = precond matrix:
>> > > > Mat Object: (fieldsplit_RB_split_) 1 MPI
>> processes
>> > > > type: seqaij
>> > > > rows=324, cols=324
>> > > > total: nonzeros=5760, allocated nonzeros=5760
>> > > > total number of mallocs used during MatSetValues calls =0
>> > > > using I-node routines: found 108 nodes, limit used is 5
>> > > > KSP solver for S = A11 - A10 inv(A00) A01
>> > > > KSP Object: (fieldsplit_FE_split_) 1 MPI processes
>> > > > type: cg
>> > > > maximum iterations=10000, initial guess is zero
>> > > > tolerances: relative=1e-05, absolute=1e-50,
>> divergence=10000.
>> > > > left preconditioning
>> > > > using PRECONDITIONED norm type for convergence test
>> > > > PC Object: (fieldsplit_FE_split_) 1 MPI processes
>> > > > type: bjacobi
>> > > > block Jacobi: number of blocks = 1
>> > > > Local solve is same for all blocks, in the following KSP
>> and PC objects:
>> > > > KSP Object: (fieldsplit_FE_split_sub_)
>> 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: (fieldsplit_FE_split_sub_)
>> 1 MPI processes
>> > > > type: ilu
>> > > > 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: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=28476
>> > > > package used to perform factorization: petsc
>> > > > total: nonzeros=1017054, allocated
>> nonzeros=1017054
>> > > > total number of mallocs used during
>> MatSetValues calls =0
>> > > > using I-node routines: found 9492 nodes,
>> limit used is 5
>> > > > linear system matrix = precond matrix:
>> > > > Mat Object: (fieldsplit_FE_split_)
>> 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=28476
>> > > > total: nonzeros=1017054, allocated nonzeros=1017054
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 9492 nodes, limit used
>> is 5
>> > > > linear system matrix followed by preconditioner matrix:
>> > > > Mat Object: (fieldsplit_FE_split_) 1 MPI
>> processes
>> > > > type: schurcomplement
>> > > > rows=28476, cols=28476
>> > > > Schur complement A11 - A10 inv(A00) A01
>> > > > A11
>> > > > Mat Object: (fieldsplit_FE_split_)
>> 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=28476
>> > > > total: nonzeros=1017054, allocated nonzeros=1017054
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 9492 nodes, limit
>> used is 5
>> > > > A10
>> > > > Mat Object: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28476, cols=324
>> > > > total: nonzeros=936, allocated nonzeros=936
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 5717 nodes, limit
>> used is 5
>> > > > KSP of A00
>> > > > KSP Object: (fieldsplit_RB_split_)
>> 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: (fieldsplit_RB_split_)
>> 1 MPI processes
>> > > > type: cholesky
>> > > > Cholesky: out-of-place factorization
>> > > > tolerance for zero pivot 2.22045e-14
>> > > > matrix ordering: natural
>> > > > factor fill ratio given 0., needed 0.
>> > > > Factored matrix follows:
>> > > > Mat Object: 1 MPI
>> processes
>> > > > type: seqaij
>> > > > rows=324, cols=324
>> > > > package used to perform factorization: mumps
>> > > > total: nonzeros=3042, allocated
>> nonzeros=3042
>> > > > total number of mallocs used during
>> MatSetValues calls =0
>> > > > MUMPS run parameters:
>> > > > SYM (matrix type): 2
>> > > > PAR (host participation): 1
>> > > > ICNTL(1) (output for error): 6
>> > > > ICNTL(2) (output of diagnostic msg): 0
>> > > > ICNTL(3) (output for global info): 0
>> > > > ICNTL(4) (level of printing): 0
>> > > > ICNTL(5) (input mat struct): 0
>> > > > ICNTL(6) (matrix prescaling): 7
>> > > > ICNTL(7) (sequentia matrix ordering):7
>> > > > ICNTL(8) (scalling strategy): 77
>> > > > ICNTL(10) (max num of refinements): 0
>> > > > ICNTL(11) (error analysis): 0
>> > > > ICNTL(12) (efficiency control):
>> 0
>> > > > ICNTL(13) (efficiency control):
>> 0
>> > > > ICNTL(14) (percentage of estimated
>> workspace increase): 20
>> > > > ICNTL(18) (input mat struct):
>> 0
>> > > > ICNTL(19) (Shur complement info):
>> 0
>> > > > ICNTL(20) (rhs sparse pattern):
>> 0
>> > > > ICNTL(21) (solution struct):
>> 0
>> > > > ICNTL(22) (in-core/out-of-core
>> facility): 0
>> > > > ICNTL(23) (max size of memory can be
>> allocated locally):0
>> > > > ICNTL(24) (detection of null pivot
>> rows): 0
>> > > > ICNTL(25) (computation of a null space
>> basis): 0
>> > > > ICNTL(26) (Schur options for rhs or
>> solution): 0
>> > > > ICNTL(27) (experimental parameter):
>> -24
>> > > > ICNTL(28) (use parallel or sequential
>> ordering): 1
>> > > > ICNTL(29) (parallel ordering):
>> 0
>> > > > ICNTL(30) (user-specified set of
>> entries in inv(A)): 0
>> > > > ICNTL(31) (factors is discarded in the
>> solve phase): 0
>> > > > ICNTL(33) (compute determinant):
>> 0
>> > > > CNTL(1) (relative pivoting threshold):
>> 0.01
>> > > > CNTL(2) (stopping criterion of
>> refinement): 1.49012e-08
>> > > > CNTL(3) (absolute pivoting threshold):
>> 0.
>> > > > CNTL(4) (value of static pivoting):
>> -1.
>> > > > CNTL(5) (fixation for null pivots):
>> 0.
>> > > > RINFO(1) (local estimated flops for the
>> elimination after analysis):
>> > > > [0] 29394.
>> > > > RINFO(2) (local estimated flops for the
>> assembly after factorization):
>> > > > [0] 1092.
>> > > > RINFO(3) (local estimated flops for the
>> elimination after factorization):
>> > > > [0] 29394.
>> > > > INFO(15) (estimated size of (in MB)
>> MUMPS internal data for running numerical factorization):
>> > > > [0] 1
>> > > > INFO(16) (size of (in MB) MUMPS
>> internal data used during numerical factorization):
>> > > > [0] 1
>> > > > INFO(23) (num of pivots eliminated on
>> this processor after factorization):
>> > > > [0] 324
>> > > > RINFOG(1) (global estimated flops for
>> the elimination after analysis): 29394.
>> > > > RINFOG(2) (global estimated flops for
>> the assembly after factorization): 1092.
>> > > > RINFOG(3) (global estimated flops for
>> the elimination after factorization): 29394.
>> > > > (RINFOG(12) RINFOG(13))*2^INFOG(34)
>> (determinant): (0.,0.)*(2^0)
>> > > > INFOG(3) (estimated real workspace for
>> factors on all processors after analysis): 3888
>> > > > INFOG(4) (estimated integer workspace
>> for factors on all processors after analysis): 2067
>> > > > INFOG(5) (estimated maximum front size
>> in the complete tree): 12
>> > > > INFOG(6) (number of nodes in the
>> complete tree): 53
>> > > > INFOG(7) (ordering option effectively
>> use after analysis): 2
>> > > > INFOG(8) (structural symmetry in
>> percent of the permuted matrix after analysis): 100
>> > > > INFOG(9) (total real/complex workspace
>> to store the matrix factors after factorization): 3888
>> > > > INFOG(10) (total integer space store
>> the matrix factors after factorization): 2067
>> > > > INFOG(11) (order of largest frontal
>> matrix after factorization): 12
>> > > > INFOG(12) (number of off-diagonal
>> pivots): 0
>> > > > INFOG(13) (number of delayed pivots
>> after factorization): 0
>> > > > INFOG(14) (number of memory compress
>> after factorization): 0
>> > > > INFOG(15) (number of steps of iterative
>> refinement after solution): 0
>> > > > INFOG(16) (estimated size (in MB) of
>> all MUMPS internal data for factorization after analysis: value on the most
>> memory consuming processor): 1
>> > > > INFOG(17) (estimated size of all MUMPS
>> internal data for factorization after analysis: sum over all processors): 1
>> > > > INFOG(18) (size of all MUMPS internal
>> data allocated during factorization: value on the most memory consuming
>> processor): 1
>> > > > INFOG(19) (size of all MUMPS internal
>> data allocated during factorization: sum over all processors): 1
>> > > > INFOG(20) (estimated number of entries
>> in the factors): 3042
>> > > > INFOG(21) (size in MB of memory
>> effectively used during factorization - value on the most memory consuming
>> processor): 1
>> > > > INFOG(22) (size in MB of memory
>> effectively used during factorization - sum over all processors): 1
>> > > > INFOG(23) (after analysis: value of
>> ICNTL(6) effectively used): 5
>> > > > INFOG(24) (after analysis: value of
>> ICNTL(12) effectively used): 1
>> > > > INFOG(25) (after factorization: number
>> of pivots modified by static pivoting): 0
>> > > > INFOG(28) (after factorization: number
>> of null pivots encountered): 0
>> > > > INFOG(29) (after factorization:
>> effective number of entries in the factors (sum over all processors)): 3042
>> > > > INFOG(30, 31) (after solution: size in
>> Mbytes of memory used during solution phase): 0, 0
>> > > > INFOG(32) (after analysis: type of
>> analysis done): 1
>> > > > INFOG(33) (value used for ICNTL(8)): -2
>> > > > INFOG(34) (exponent of the determinant
>> if determinant is requested): 0
>> > > > linear system matrix = precond matrix:
>> > > > Mat Object: (fieldsplit_RB_split_)
>> 1 MPI processes
>> > > > type: seqaij
>> > > > rows=324, cols=324
>> > > > total: nonzeros=5760, allocated nonzeros=5760
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 108 nodes, limit
>> used is 5
>> > > > A01
>> > > > Mat Object: 1 MPI processes
>> > > > type: seqaij
>> > > > rows=324, cols=28476
>> > > > total: nonzeros=936, allocated nonzeros=936
>> > > > total number of mallocs used during MatSetValues
>> calls =0
>> > > > using I-node routines: found 67 nodes, limit used
>> is 5
>> > > > Mat Object: (fieldsplit_FE_split_) 1 MPI
>> processes
>> > > > type: seqaij
>> > > > rows=28476, cols=28476
>> > > > total: nonzeros=1017054, allocated nonzeros=1017054
>> > > > total number of mallocs used during MatSetValues calls =0
>> > > > using I-node routines: found 9492 nodes, limit used is 5
>> > > > linear system matrix = precond matrix:
>> > > > Mat Object: () 1 MPI processes
>> > > > type: seqaij
>> > > > rows=28800, cols=28800
>> > > > total: nonzeros=1024686, allocated nonzeros=1024794
>> > > > total number of mallocs used during MatSetValues calls =0
>> > > > using I-node routines: found 9600 nodes, limit used is 5
>> > > >
>> > > >
>> > > > ---------------------------------------------- PETSc Performance
>> Summary: ----------------------------------------------
>> > > >
>> > > > /home/dknez/akselos-dev/scrbe/build/bin/fe_solver-opt_real on a
>> arch-linux2-c-opt named david-Lenovo with 1 processor, by dknez Wed Jan 11
>> 16:16:47 2017
>> > > > Using Petsc Release Version 3.7.3, unknown
>> > > >
>> > > > Max Max/Min Avg Total
>> > > > Time (sec): 9.179e+01 1.00000 9.179e+01
>> > > > Objects: 1.990e+02 1.00000 1.990e+02
>> > > > Flops: 1.634e+11 1.00000 1.634e+11 1.634e+11
>> > > > Flops/sec: 1.780e+09 1.00000 1.780e+09 1.780e+09
>> > > > MPI Messages: 0.000e+00 0.00000 0.000e+00 0.000e+00
>> > > > MPI Message Lengths: 0.000e+00 0.00000 0.000e+00 0.000e+00
>> > > > MPI Reductions: 0.000e+00 0.00000
>> > > >
>> > > > Flop counting convention: 1 flop = 1 real number operation of type
>> (multiply/divide/add/subtract)
>> > > > e.g., VecAXPY() for real vectors of
>> length N --> 2N flops
>> > > > and VecAXPY() for complex vectors of
>> length N --> 8N flops
>> > > >
>> > > > Summary of Stages: ----- Time ------ ----- Flops ----- ---
>> Messages --- -- Message Lengths -- -- Reductions --
>> > > > Avg %Total Avg %Total counts
>> %Total Avg %Total counts %Total
>> > > > 0: Main Stage: 9.1787e+01 100.0% 1.6336e+11 100.0%
>> 0.000e+00 0.0% 0.000e+00 0.0% 0.000e+00 0.0%
>> > > >
>> > > > ------------------------------------------------------------
>> ------------------------------------------------------------
>> > > > See the 'Profiling' chapter of the users' manual for details on
>> interpreting output.
>> > > > Phase summary info:
>> > > > Count: number of times phase was executed
>> > > > Time and Flops: Max - maximum over all processors
>> > > > Ratio - ratio of maximum to minimum over all
>> processors
>> > > > Mess: number of messages sent
>> > > > Avg. len: average message length (bytes)
>> > > > Reduct: number of global reductions
>> > > > Global: entire computation
>> > > > Stage: stages of a computation. Set stages with
>> PetscLogStagePush() and PetscLogStagePop().
>> > > > %T - percent time in this phase %F - percent flops in
>> this phase
>> > > > %M - percent messages in this phase %L - percent message
>> lengths in this phase
>> > > > %R - percent reductions in this phase
>> > > > Total Mflop/s: 10e-6 * (sum of flops over all processors)/(max
>> time over all processors)
>> > > > ------------------------------------------------------------
>> ------------------------------------------------------------
>> > > > Event Count Time (sec) Flops
>> --- Global --- --- Stage --- Total
>> > > > Max Ratio Max Ratio Max Ratio Mess
>> Avg len Reduct %T %F %M %L %R %T %F %M %L %R Mflop/s
>> > > > ------------------------------------------------------------
>> ------------------------------------------------------------
>> > > >
>> > > > --- Event Stage 0: Main Stage
>> > > >
>> > > > VecDot 42 1.0 2.4080e-05 1.0 8.53e+03 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 354
>> > > > VecTDot 74012 1.0 1.2440e+00 1.0 4.22e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 1 3 0 0 0 1 3 0 0 0 3388
>> > > > VecNorm 37020 1.0 8.3580e-01 1.0 2.11e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 1 1 0 0 0 1 1 0 0 0 2523
>> > > > VecScale 37008 1.0 3.5800e-01 1.0 1.05e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 1 0 0 0 0 1 0 0 0 2944
>> > > > VecCopy 37034 1.0 2.5754e-02 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > VecSet 74137 1.0 3.0537e-01 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > VecAXPY 74029 1.0 1.7233e+00 1.0 4.22e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 2 3 0 0 0 2 3 0 0 0 2446
>> > > > VecAYPX 37001 1.0 1.2214e+00 1.0 2.11e+09 1.0 0.0e+00
>> 0.0e+00 0.0e+00 1 1 0 0 0 1 1 0 0 0 1725
>> > > > VecAssemblyBegin 68 1.0 2.0432e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > VecAssemblyEnd 68 1.0 2.5988e-05 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > VecScatterBegin 48 1.0 4.6921e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatMult 37017 1.0 4.1269e+01 1.0 7.65e+10 1.0 0.0e+00
>> 0.0e+00 0.0e+00 45 47 0 0 0 45 47 0 0 0 1853
>> > > > MatMultAdd 37015 1.0 3.3638e+01 1.0 7.53e+10 1.0 0.0e+00
>> 0.0e+00 0.0e+00 37 46 0 0 0 37 46 0 0 0 2238
>> > > > MatSolve 74021 1.0 4.6602e+01 1.0 7.42e+10 1.0 0.0e+00
>> 0.0e+00 0.0e+00 51 45 0 0 0 51 45 0 0 0 1593
>> > > > MatLUFactorNum 1 1.0 1.7209e-02 1.0 2.44e+07 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 1420
>> > > > MatCholFctrSym 1 1.0 8.8310e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatCholFctrNum 1 1.0 3.6907e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatILUFactorSym 1 1.0 3.7372e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatAssemblyBegin 29 1.0 2.1458e-06 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatAssemblyEnd 29 1.0 9.9473e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatGetRow 58026 1.0 2.8155e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatGetRowIJ 2 1.0 0.0000e+00 0.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatGetSubMatrice 6 1.0 1.5399e-02 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatGetOrdering 2 1.0 3.0112e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatZeroEntries 6 1.0 2.9490e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > MatView 7 1.0 3.4356e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > KSPSetUp 4 1.0 9.4891e-05 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > KSPSolve 1 1.0 8.8793e+01 1.0 1.63e+11 1.0 0.0e+00
>> 0.0e+00 0.0e+00 97100 0 0 0 97100 0 0 0 1840
>> > > > PCSetUp 4 1.0 3.8375e-02 1.0 2.44e+07 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 637
>> > > > PCSetUpOnBlocks 5 1.0 2.1250e-02 1.0 2.44e+07 1.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 1150
>> > > > PCApply 5 1.0 8.8789e+01 1.0 1.63e+11 1.0 0.0e+00
>> 0.0e+00 0.0e+00 97100 0 0 0 97100 0 0 0 1840
>> > > > KSPSolve_FS_0 5 1.0 7.5364e-04 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > KSPSolve_FS_Schu 5 1.0 8.8785e+01 1.0 1.63e+11 1.0 0.0e+00
>> 0.0e+00 0.0e+00 97100 0 0 0 97100 0 0 0 1840
>> > > > KSPSolve_FS_Low 5 1.0 2.1019e-03 1.0 0.00e+00 0.0 0.0e+00
>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 0
>> > > > ------------------------------------------------------------
>> ------------------------------------------------------------
>> > > >
>> > > > Memory usage is given in bytes:
>> > > >
>> > > > Object Type Creations Destructions Memory
>> Descendants' Mem.
>> > > > Reports information only for process 0.
>> > > >
>> > > > --- Event Stage 0: Main Stage
>> > > >
>> > > > Vector 91 91 9693912 0.
>> > > > Vector Scatter 24 24 15936 0.
>> > > > Index Set 51 51 537888 0.
>> > > > IS L to G Mapping 3 3 240408 0.
>> > > > Matrix 13 13 64097868 0.
>> > > > Krylov Solver 6 6 7888 0.
>> > > > Preconditioner 6 6 6288 0.
>> > > > Viewer 1 0 0 0.
>> > > > Distributed Mesh 1 1 4624 0.
>> > > > Star Forest Bipartite Graph 2 2 1616 0.
>> > > > Discrete System 1 1 872 0.
>> > > > ============================================================
>> ============================================================
>> > > > Average time to get PetscTime(): 0.
>> > > > #PETSc Option Table entries:
>> > > > -ksp_monitor
>> > > > -ksp_view
>> > > > -log_view
>> > > > #End of PETSc Option Table entries
>> > > > Compiled without FORTRAN kernels
>> > > > Compiled with full precision matrices (default)
>> > > > sizeof(short) 2 sizeof(int) 4 sizeof(long) 8 sizeof(void*) 8
>> sizeof(PetscScalar) 8 sizeof(PetscInt) 4
>> > > > Configure options: --with-shared-libraries=1 --with-debugging=0
>> --download-suitesparse --download-blacs --download-ptscotch=yes
>> --with-blas-lapack-dir=/opt/intel/system_studio_2015.2.050/mkl
>> --CXXFLAGS=-Wl,--no-as-needed --download-scalapack --download-mumps
>> --download-metis --prefix=/home/dknez/software/libmesh_install/opt_real/petsc
>> --download-hypre --download-ml
>> > > > -----------------------------------------
>> > > > Libraries compiled on Wed Sep 21 17:38:52 2016 on david-Lenovo
>> > > > Machine characteristics: Linux-4.4.0-38-generic-x86_64-
>> with-Ubuntu-16.04-xenial
>> > > > Using PETSc directory: /home/dknez/software/petsc-src
>> > > > Using PETSc arch: arch-linux2-c-opt
>> > > > -----------------------------------------
>> > > >
>> > > > Using C compiler: mpicc -fPIC -Wall -Wwrite-strings
>> -Wno-strict-aliasing -Wno-unknown-pragmas -fvisibility=hidden -g -O
>> ${COPTFLAGS} ${CFLAGS}
>> > > > Using Fortran compiler: mpif90 -fPIC -Wall -ffree-line-length-0
>> -Wno-unused-dummy-argument -g -O ${FOPTFLAGS} ${FFLAGS}
>> > > > -----------------------------------------
>> > > >
>> > > > Using include paths: -I/home/dknez/software/petsc-src/arch-linux2-c-opt/include
>> -I/home/dknez/software/petsc-src/include -I/home/dknez/software/petsc-src/include
>> -I/home/dknez/software/petsc-src/arch-linux2-c-opt/include
>> -I/home/dknez/software/libmesh_install/opt_real/petsc/include
>> -I/usr/lib/openmpi/include/openmpi/opal/mca/event/libevent2021/libevent
>> -I/usr/lib/openmpi/include/openmpi/opal/mca/event/libevent2021/libevent/include
>> -I/usr/lib/openmpi/include -I/usr/lib/openmpi/include/openmpi
>> > > > -----------------------------------------
>> > > >
>> > > > Using C linker: mpicc
>> > > > Using Fortran linker: mpif90
>> > > > Using libraries: -Wl,-rpath,/home/dknez/softwar
>> e/petsc-src/arch-linux2-c-opt/lib -L/home/dknez/software/petsc-src/arch-linux2-c-opt/lib
>> -lpetsc -Wl,-rpath,/home/dknez/software/libmesh_install/opt_real/petsc/lib
>> -L/home/dknez/software/libmesh_install/opt_real/petsc/lib -lcmumps
>> -ldmumps -lsmumps -lzmumps -lmumps_common -lpord -lmetis -lHYPRE
>> -Wl,-rpath,/usr/lib/openmpi/lib -L/usr/lib/openmpi/lib
>> -Wl,-rpath,/usr/lib/gcc/x86_64-linux-gnu/5 -L/usr/lib/gcc/x86_64-linux-gnu/5
>> -Wl,-rpath,/usr/lib/x86_64-linux-gnu -L/usr/lib/x86_64-linux-gnu
>> -Wl,-rpath,/lib/x86_64-linux-gnu -L/lib/x86_64-linux-gnu -lmpi_cxx
>> -lstdc++ -lscalapack -lml -lmpi_cxx -lstdc++ -lumfpack -lklu -lcholmod
>> -lbtf -lccolamd -lcolamd -lcamd -lamd -lsuitesparseconfig
>> -Wl,-rpath,/opt/intel/system_studio_2015.2.050/mkl/lib/intel64
>> -L/opt/intel/system_studio_2015.2.050/mkl/lib/intel64 -lmkl_intel_lp64
>> -lmkl_sequential -lmkl_core -lpthread -lm -lhwloc -lptesmumps -lptscotch
>> -lptscotcherr -lscotch -lscotcherr -lX11 -lm -lmpi_usempif08
>> -lmpi_usempi_ignore_tkr -lmpi_mpifh -lgfortran -lm -lgfortran -lm
>> -lquadmath -lm -lmpi_cxx -lstdc++ -lrt -lm -lpthread -lz
>> -Wl,-rpath,/usr/lib/openmpi/lib -L/usr/lib/openmpi/lib
>> -Wl,-rpath,/usr/lib/gcc/x86_64-linux-gnu/5 -L/usr/lib/gcc/x86_64-linux-gnu/5
>> -Wl,-rpath,/usr/lib/x86_64-linux-gnu -L/usr/lib/x86_64-linux-gnu
>> -Wl,-rpath,/lib/x86_64-linux-gnu -L/lib/x86_64-linux-gnu
>> -Wl,-rpath,/usr/lib/x86_64-linux-gnu -L/usr/lib/x86_64-linux-gnu -ldl
>> -Wl,-rpath,/usr/lib/openmpi/lib -lmpi -lgcc_s -lpthread -ldl
>> > > > -----------------------------------------
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > > >
>> > >
>> > >
>> > > <logfile_1.txt><logfile_2.txt>
>> >
>> >
>>
>>
>
--
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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20170112/2f84d377/attachment-0001.html>
More information about the petsc-users
mailing list