[petsc-users] DIVERGED_PCSETUP_FAILED
Michele Rosso
mrosso at uci.edu
Thu Feb 11 13:17:50 CST 2016
I am using periodic along x and Neumann along y. I remove the nullspace
via -ksp_constant_null_space.
Matt's suggestion worked; I will also give a try to umfpack and cholesky.
Thanks,
Michele
On 02/11/2016 06:50 AM, Matthew Knepley wrote:
> On Thu, Feb 11, 2016 at 12:30 AM, Dave May <dave.mayhem23 at gmail.com
> <mailto:dave.mayhem23 at gmail.com>> wrote:
>
>
>
> On 11 February 2016 at 07:05, Michele Rosso <mrosso at uci.edu
> <mailto:mrosso at uci.edu>> wrote:
>
> I tried setting -mat_superlu_dist_replacetinypivot true: it
> does help to advance the run past the previous "critical"
> point but eventually it stops later with the same error.
> I forgot to mention my system is singular: I remove the
> constant null space but I am not sure if the coarse solver
> needs to be explicity informed of this.
>
>
> Right - are you using pure Newmann boundary conditions?
>
> To make the solution unique, are you
> (a) imposing a single Dichletet boundary condition on your field
> by explicitly modifying the matrix
> (b) imposing a a condition like
> \int \phi dV = 0
> via something like -ksp_constant_null_space
>
> If you removed removed the null space by modifying the matrix
> explicitly (a), the sparse direct solver
> should go through. If you use (b), then this method cannot be used
> to help the direct solver.
>
> If this is the intended target problem size (16x16), gather the
> matrix and using petsc Cholesky or Umfpack.
> Cholesky is more stable than LU and can usually deal with a single
> zero eigenvaue without resorting to tricks. Umfpack will solve the
> problem easily as it uses clever re-ordering. If you have access
> to MKL-Pardiso, that will also work great.
>
>
> An easy fix is just to use -pc_type svd on the coarse grid.
>
> Matt
>
> Thanks,
> Dave
>
>
>
> Michele
>
>
> On Wed, 2016-02-10 at 22:15 -0600, Barry Smith wrote:
>> You can try the option
>>
>> -mat_superlu_dist_replacetinypivot true
>>
>> if you are luck it get you past the zero pivot but still give an adequate preconditioner.
>>
>> Barry
>>
>> > On Feb 10, 2016, at 9:49 PM, Michele Rosso <mrosso at uci.edu
>> <mailto:mrosso at uci.edu>> wrote:
>> >
>> > Hong,
>> >
>> > here if the output of grep -info:
>> >
>> > using diagonal shift on blocks to prevent zero pivot [INBLOCKS]
>> > Replace tiny pivots FALSE
>> > tolerance for zero pivot 2.22045e-14
>> >
>> > It seems it is not replacing small pivots: could this be
>> the problem?
>> > I will also try Barry's suggestion to diagnose the problem.
>> >
>> > Thanks,
>> > Michele
>> >
>> >
>> > On Wed, 2016-02-10 at 21:22 -0600, Barry Smith wrote:
>> >> > On Feb 10, 2016, at 9:00 PM, Hong <hzhang at mcs.anl.gov
>> <mailto:hzhang at mcs.anl.gov>> wrote:
>> >> >
>> >> > Michele :
>> >> > Superlu_dist LU is used for coarse grid PC, which likely
>> produces a zero-pivot.
>> >> > Run your code with '-info |grep pivot' to verify.
>> >>
>> >>
>> >> Michele
>> >>
>> >> You can also run with -ksp_error_if_not_converged in or
>> not in the debugger and it will stop immediately when the
>> problem is detected and hopefully provide additional useful
>> information about what has happened.
>> >>
>> >> Barry
>> >>
>> >>
>> >> >
>> >> > Hong
>> >> >
>> >> > Hi Matt,
>> >> >
>> >> > the ksp_view output was an attachment to my previous email.
>> >> > Here it is:
>> >> >
>> >> > KSP Object: 1 MPI processes
>> >> > type: cg
>> >> > maximum iterations=10000
>> >> > tolerances: relative=1e-08, absolute=1e-50,
>> divergence=10000.
>> >> > left preconditioning
>> >> > using nonzero initial guess
>> >> > using UNPRECONDITIONED norm type for convergence test
>> >> > PC Object: 1 MPI processes
>> >> > type: mg
>> >> > MG: type is MULTIPLICATIVE, levels=4 cycles=v
>> >> > Cycles per PCApply=1
>> >> > Using Galerkin computed coarse grid matrices
>> >> > Coarse grid solver -- level -------------------------------
>> >> > KSP Object: (mg_coarse_) 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_) 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 0., needed 0.
>> >> > Factored matrix follows:
>> >> > Mat Object: 1 MPI processes
>> >> > type: seqaij
>> >> > rows=16, cols=16
>> >> > package used to perform factorization: superlu_dist
>> >> > total: nonzeros=0, allocated nonzeros=0
>> >> > total number of mallocs used during MatSetValues calls =0
>> >> > SuperLU_DIST run parameters:
>> >> > Process grid nprow 1 x npcol 1
>> >> > Equilibrate matrix TRUE
>> >> > Matrix input mode 0
>> >> > Replace tiny pivots FALSE
>> >> > Use iterative refinement FALSE
>> >> > Processors in row 1 col partition 1
>> >> > Row permutation LargeDiag
>> >> > Column permutation METIS_AT_PLUS_A
>> >> > Parallel symbolic factorization FALSE
>> >> > Repeated factorization SamePattern
>> >> > linear system matrix = precond matrix:
>> >> > Mat Object: 1 MPI processes
>> >> > type: seqaij
>> >> > rows=16, cols=16
>> >> > total: nonzeros=72, allocated nonzeros=72
>> >> > total number of mallocs used during MatSetValues calls =0
>> >> > not using I-node routines
>> >> > Down solver (pre-smoother) on level 1
>> -------------------------------
>> >> > KSP Object: (mg_levels_1_) 1 MPI processes
>> >> > type: richardson
>> >> > Richardson: damping factor=1.
>> >> > maximum iterations=2
>> >> > tolerances: relative=1e-05, absolute=1e-50,
>> divergence=10000.
>> >> > left preconditioning
>> >> > using nonzero initial guess
>> >> > using NONE norm type for convergence test
>> >> > PC Object: (mg_levels_1_) 1 MPI processes
>> >> > type: sor
>> >> > SOR: type = local_symmetric, iterations = 1, local
>> iterations = 1, omega = 1.
>> >> > linear system matrix = precond matrix:
>> >> > Mat Object: 1 MPI processes
>> >> > type: seqaij
>> >> > rows=64, cols=64
>> >> > total: nonzeros=304, allocated nonzeros=304
>> >> > total number of mallocs used during MatSetValues calls =0
>> >> > not using I-node routines
>> >> > Up solver (post-smoother) same as down solver (pre-smoother)
>> >> > Down solver (pre-smoother) on level 2
>> -------------------------------
>> >> > KSP Object: (mg_levels_2_) 1 MPI processes
>> >> > type: richardson
>> >> > Richardson: damping factor=1.
>> >> > maximum iterations=2
>> >> > tolerances: relative=1e-05, absolute=1e-50,
>> divergence=10000.
>> >> > left preconditioning
>> >> > using nonzero initial guess
>> >> > using NONE norm type for convergence test
>> >> > PC Object: (mg_levels_2_) 1 MPI processes
>> >> > type: sor
>> >> > SOR: type = local_symmetric, iterations = 1, local
>> iterations = 1, omega = 1.
>> >> > linear system matrix = precond matrix:
>> >> > Mat Object: 1 MPI processes
>> >> > type: seqaij
>> >> > rows=256, cols=256
>> >> > total: nonzeros=1248, allocated nonzeros=1248
>> >> > total number of mallocs used during MatSetValues calls =0
>> >> > not using I-node routines
>> >> > Up solver (post-smoother) same as down solver (pre-smoother)
>> >> > Down solver (pre-smoother) on level 3
>> -------------------------------
>> >> > KSP Object: (mg_levels_3_) 1 MPI processes
>> >> > type: richardson
>> >> > Richardson: damping factor=1.
>> >> > maximum iterations=2
>> >> > tolerances: relative=1e-05, absolute=1e-50,
>> divergence=10000.
>> >> > left preconditioning
>> >> > using nonzero initial guess
>> >> > using NONE norm type for convergence test
>> >> > PC Object: (mg_levels_3_) 1 MPI processes
>> >> > type: sor
>> >> > SOR: type = local_symmetric, iterations = 1, local
>> iterations = 1, omega = 1.
>> >> > linear system matrix = precond matrix:
>> >> > Mat Object: 1 MPI processes
>> >> > type: seqaij
>> >> > rows=1024, cols=1024
>> >> > total: nonzeros=5056, allocated nonzeros=5056
>> >> > total number of mallocs used during MatSetValues calls =0
>> >> > has attached null space
>> >> > not using I-node routines
>> >> > Up solver (post-smoother) same as down solver (pre-smoother)
>> >> > linear system matrix = precond matrix:
>> >> > Mat Object: 1 MPI processes
>> >> > type: seqaij
>> >> > rows=1024, cols=1024
>> >> > total: nonzeros=5056, allocated nonzeros=5056
>> >> > total number of mallocs used during MatSetValues calls =0
>> >> > has attached null space
>> >> > not using I-node routines
>> >> >
>> >> >
>> >> > Michele
>> >> >
>> >> >
>> >> >
>> >> >
>> >> > On Wed, 2016-02-10 at 19:37 -0600, Matthew Knepley wrote:
>> >> >> On Wed, Feb 10, 2016 at 7:33 PM, Michele Rosso
>> <mrosso at uci.edu <mailto:mrosso at uci.edu>> wrote:
>> >> >> Hi,
>> >> >>
>> >> >> I encountered the following error while solving a
>> symmetric positive defined system:
>> >> >>
>> >> >> Linear solve did not converge due to
>> DIVERGED_PCSETUP_FAILED iterations 0
>> >> >> PCSETUP_FAILED due to SUBPC_ERROR
>> >> >>
>> >> >> This error appears only if I use the optimized version
>> of both petsc and my code ( compiler: gfortran, flags: -O3 ).
>> >> >> It is weird since I am solving a time-dependent problem
>> and everything, i.e. results and convergence rate, are as
>> expected until the above error shows up. If I run both petsc
>> and my code in debug mode, everything goes smooth till the
>> end of the simulation.
>> >> >> However, if I reduce the ksp_rtol, even the debug run
>> fails, after running as expected for a while, because of a
>> KSP_DIVERGED_INDEFINITE_PC .
>> >> >> The options I am using are:
>> >> >>
>> >> >> -ksp_type cg
>> >> >> -ksp_norm_type unpreconditioned
>> >> >> -ksp_rtol 1e-8
>> >> >> -ksp_lag_norm
>> >> >> -ksp_initial_guess_nonzero yes
>> >> >> -pc_type mg
>> >> >> -pc_mg_galerkin
>> >> >> -pc_mg_levels 4
>> >> >> -mg_levels_ksp_type richardson
>> >> >> -mg_coarse_ksp_constant_null_space
>> >> >> -mg_coarse_pc_type lu
>> >> >> -mg_coarse_pc_factor_mat_solver_package superlu_dist
>> >> >> -options_left
>> >> >>
>> >> >> I attached a copy of ksp_view. I am currently using
>> petsc-master (last updated yesterday).
>> >> >> I would appreciate any suggestion on this matter.
>> >> >>
>> >> >>
>> >> >>
>> >> >> I suspect you have a nonlinear PC. Can you send the
>> output of -ksp_view?
>> >> >>
>> >> >>
>> >> >> Matt
>> >> >>
>> >> >> Thanks,
>> >> >> Michele
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >> --
>> >> >> 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
>> >> >
>> >> >
>> >>
>> >>
>> >>
>> >
>>
>
>
>
>
>
> --
> 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
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