[petsc-users] About Preconditioner and MUMPS
김성익
ksi2443 at gmail.com
Wed Dec 7 07:15:26 CST 2022
Following your comments,
I used below command
mpirun -np 4 ./app -ksp_type preonly -pc_type mpi -mpi_linear_solver_server
-mpi_pc_type lu -mpi_pc_factor_mat_solver_type mumps -mpi_mat_mumps_icntl_7
5 -mpi_ksp_view
so the output is as below
KSP Object: (mpi_) 1 MPI process
type: gmres
restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization
with no iterative refinement
happy breakdown tolerance 1e-30
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: (mpi_) 1 MPI process
type: lu
out-of-place factorization
tolerance for zero pivot 2.22045e-14
matrix ordering: external
factor fill ratio given 0., needed 0.
Factored matrix follows:
Mat Object: (mpi_) 1 MPI process
type: mumps
rows=192, cols=192
package used to perform factorization: mumps
total: nonzeros=17334, allocated nonzeros=17334
MUMPS run parameters:
Use -mpi_ksp_view ::ascii_info_detail to display information
for all processes
RINFOG(1) (global estimated flops for the elimination after
analysis): 949441.
RINFOG(2) (global estimated flops for the assembly after
factorization): 18774.
RINFOG(3) (global estimated flops for the elimination after
factorization): 949441.
(RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant):
(0.,0.)*(2^0)
INFOG(3) (estimated real workspace for factors on all
processors after analysis): 17334
INFOG(4) (estimated integer workspace for factors on all
processors after analysis): 1724
INFOG(5) (estimated maximum front size in the complete tree):
96
INFOG(6) (number of nodes in the complete tree): 16
INFOG(7) (ordering option effectively used after analysis): 5
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): 17334
INFOG(10) (total integer space store the matrix factors after
factorization): 1724
INFOG(11) (order of largest frontal matrix after
factorization): 96
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): 17334
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): 0
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)): 17334
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)): 7
INFOG(34) (exponent of the determinant if determinant is
requested): 0
INFOG(35) (after factorization: number of entries taking into
account BLR factor compression - sum over all processors): 17334
INFOG(36) (after analysis: estimated size of all MUMPS
internal data for running BLR in-core - value on the most memory consuming
processor): 0
INFOG(37) (after analysis: estimated size of all MUMPS
internal data for running BLR in-core - sum over all processors): 0
INFOG(38) (after analysis: estimated size of all MUMPS
internal data for running BLR out-of-core - value on the most memory
consuming processor): 0
INFOG(39) (after analysis: estimated size of all MUMPS
internal data for running BLR out-of-core - sum over all processors): 0
linear system matrix = precond matrix:
Mat Object: 1 MPI process
type: seqaij
rows=192, cols=192
total: nonzeros=9000, allocated nonzeros=36864
total number of mallocs used during MatSetValues calls=0
using I-node routines: found 64 nodes, limit used is 5
Is it correct that I successfully computed with mumps by using
metis(icntl_7 5)?
Thanks,
Hyung Kim
2022년 12월 7일 (수) 오후 8:41, Matthew Knepley <knepley at gmail.com>님이 작성:
> On Wed, Dec 7, 2022 at 6:15 AM 김성익 <ksi2443 at gmail.com> wrote:
>
>> I think I don't understand the meaning of
>> -pc_type mpi
>>
>
> This option says to use the PCMPI preconditioner. This allows you to
> parallelize the
> solver in what is otherwise a serial code.
>
>
>> -mpi_pc_type lu
>>
>
> This tells the underlying solver in PCMPI to use the LU preconditioner.
>
>
>> What's the exact meaning of -pc_type mpi and -mpi_pc_type lu??
>> Is this difference coming from 'mpi_linear_solver_server' option??
>>
>
> Please use -ksp_view as I asked to look at the entire solver. Send it
> anytime you mail about solver questions.
>
> Thanks
>
> Matt
>
>
>> Thanks,
>> Hyung Kim
>>
>> 2022년 12월 7일 (수) 오후 8:05, Matthew Knepley <knepley at gmail.com>님이 작성:
>>
>>> On Wed, Dec 7, 2022 at 5:13 AM 김성익 <ksi2443 at gmail.com> wrote:
>>>
>>>> I want to use METIS for ordering.
>>>> I heard the MUMPS has good performance with METIS ordering.
>>>>
>>>> However there are some wonder things.
>>>> 1. With option "-mpi_linear_solver_server -ksp_type preonly -pc_type
>>>> mpi -mpi_pc_type lu " the MUMPS solving is slower than with option
>>>> "-mpi_linear_solver_server -pc_type mpi -ksp_type preonly".
>>>> Why does this result happen?
>>>>
>>>
>>> You are probably not using MUMPS. Always always always use -ksp_view to
>>> see exactly what solver you are using.
>>>
>>>
>>>> 2. (MPIRUN case (actually, mpi_linear_solver_server case))) In my
>>>> code, there is already has "PetscCall(PCSetType(pc,PCLU))" . However, to
>>>> use METIS by using "-mpi_mat_mumps_icntl_7 5" I must append this option
>>>> "-mpi_pc_type pu".
>>>> If I don't apply "-mpi_pc_type lu", the metis option
>>>> ("-mpi_mat_mumps_icntl_7 5"). Can I get some information about this?
>>>>
>>>
>>> Again, it seems like the solver configuration is not what you think it
>>> is.
>>>
>>> Thanks,
>>>
>>> Matt
>>>
>>>
>>>> Thanks,
>>>> Hyung Kim
>>>>
>>>> 2022년 12월 7일 (수) 오전 12:24, Barry Smith <bsmith at petsc.dev>님이 작성:
>>>>
>>>>>
>>>>>
>>>>> On Dec 6, 2022, at 5:15 AM, 김성익 <ksi2443 at gmail.com> wrote:
>>>>>
>>>>> Hello,
>>>>>
>>>>>
>>>>> I have some questions about pc and mumps_icntl.
>>>>>
>>>>> 1. What’s the difference between adopt preconditioner by code
>>>>> (for example, PetscCall(PCSetType(pc,PCLU)) and option -pc_type lu??
>>>>> And also, What’s the priority between code pcsettype and option
>>>>> -pc_type ??
>>>>>
>>>>> 2. When I tried to use METIS in MUMPS, I adopted metis by option
>>>>> (for example, -mat_mumps_icntl_7 5). In this situation, it is impossible to
>>>>> use metis without pc_type lu. However, in my case pc type lu makes the
>>>>> performance poor. So I don’t want to use lu preconditioner. How can I do
>>>>> this?
>>>>>
>>>>> The package MUMPS has an option to use metis in its ordering
>>>>> process which can be turned on as indicated while using MUMPS. Most
>>>>> preconditioners that PETSc can use do not use metis for any purpose hence
>>>>> there is no option to turn on its use. For what purpose do you wish to use
>>>>> metis? Partitioning, ordering, ?
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Hyung Kim
>>>>>
>>>>>
>>>>>
>>>
>>> --
>>> What most experimenters take for granted before they begin their
>>> experiments is infinitely more interesting than any results to which their
>>> experiments lead.
>>> -- Norbert Wiener
>>>
>>> https://www.cse.buffalo.edu/~knepley/
>>> <http://www.cse.buffalo.edu/~knepley/>
>>>
>>
>
> --
> What most experimenters take for granted before they begin their
> experiments is infinitely more interesting than any results to which their
> experiments lead.
> -- Norbert Wiener
>
> https://www.cse.buffalo.edu/~knepley/
> <http://www.cse.buffalo.edu/~knepley/>
>
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