[petsc-users] Can't expand MemType 1: jcol 16104
Hong
hzhang at mcs.anl.gov
Mon Jul 27 21:50:32 CDT 2015
Sherry,
I can repeat hang using petsc/src/ksp/ksp/examples/tutorials/ex10.c:
mpiexec -n 4 ./ex10 -f0 /homes/hzhang/tmp/Amat_binary.m -rhs 0 -pc_type lu
-pc_factor_mat_solver_package superlu_dist -mat_superlu_dist_parsymbfact
...
.. Starting with 1 OpenMP threads
[0] .. BIG U size 1342464
[0] .. BIG V size 131072
Max row size is 1311
Using buffer_size of 5000000
Threads per process 1
...
using a debugger (with petsc option '-start_in_debugger'), I find that hang
occurs at
#0 0x00007f117d870998 in __GI___poll (fds=0x20da750, nfds=4,
timeout=<optimized out>, timeout at entry=-1)
at ../sysdeps/unix/sysv/linux/poll.c:83
#1 0x00007f117de9f7de in MPIDU_Sock_wait (sock_set=0x20da550,
millisecond_timeout=millisecond_timeout at entry=-1,
eventp=eventp at entry=0x7fff654930b0)
at src/mpid/common/sock/poll/sock_wait.i:123
#2 0x00007f117de898b8 in MPIDI_CH3i_Progress_wait (
progress_state=0x7fff65493120)
at src/mpid/ch3/channels/sock/src/ch3_progress.c:218
#3 MPIDI_CH3I_Progress (blocking=blocking at entry=1,
state=state at entry=0x7fff65493120)
at src/mpid/ch3/channels/sock/src/ch3_progress.c:921
#4 0x00007f117de1a559 in MPIR_Wait_impl (request=request at entry=0x262df90,
status=status at entry=0x7fff65493390) at src/mpi/pt2pt/wait.c:67
#5 0x00007f117de1a818 in PMPI_Wait (request=0x262df90,
status=0x7fff65493390)
at src/mpi/pt2pt/wait.c:168
#6 0x00007f11831da557 in pzgstrf (options=0x23dfda0, m=4900, n=4900,
anorm=13.738475134194639, LUstruct=0x23dfe78, grid=0x23dfd78,
stat=0x7fff65493ea0, info=0x7fff65493edc) at pzgstrf.c:1308
#7 0x00007f11831bf3bd in pzgssvx (options=0x23dfda0, A=0x23dfe30,
ScalePermstruct=0x23dfe50, B=0x0, ldb=1225, nrhs=0, grid=0x23dfd78,
LUstruct=0x23dfe78, SOLVEstruct=0x23dfe98, berr=0x0,
stat=0x7fff65493ea0,
---Type <return> to continue, or q <return> to quit---
info=0x7fff65493edc) at pzgssvx.c:1063
#8 0x00007f11825c2340 in MatLUFactorNumeric_SuperLU_DIST (F=0x23a0110,
A=0x21bb7e0, info=0x2355068)
at
/sandbox/hzhang/petsc/src/mat/impls/aij/mpi/superlu_dist/superlu_dist.c:411
#9 0x00007f1181c6c567 in MatLUFactorNumeric (fact=0x23a0110,
mat=0x21bb7e0,
info=0x2355068) at /sandbox/hzhang/petsc/src/mat/interface/matrix.c:2946
#10 0x00007f1182a56489 in PCSetUp_LU (pc=0x2353a10)
at /sandbox/hzhang/petsc/src/ksp/pc/impls/factor/lu/lu.c:152
#11 0x00007f1182b16f24 in PCSetUp (pc=0x2353a10)
at /sandbox/hzhang/petsc/src/ksp/pc/interface/precon.c:983
#12 0x00007f1182be61b5 in KSPSetUp (ksp=0x232c2a0)
at /sandbox/hzhang/petsc/src/ksp/ksp/interface/itfunc.c:332
#13 0x0000000000405a31 in main (argc=11, args=0x7fff65499578)
at /sandbox/hzhang/petsc/src/ksp/ksp/examples/tutorials/ex10.c:312
You may take a look at it. Sequential symbolic factorization works fine.
Why superlu_dist (v4.0) in complex precision displays
.. Starting with 1 OpenMP threads
[0] .. BIG U size 1342464
[0] .. BIG V size 131072
Max row size is 1311
Using buffer_size of 5000000
Threads per process 1
...
I realize that I use superlu_dist v4.0. Would v4.1 works? I'll give it a
try tomorrow.
Hong
On Mon, Jul 27, 2015 at 1:25 PM, Anthony Paul Haas <aph at email.arizona.edu>
wrote:
> Hi Hong,
>
> No that is not the correct matrix. Note that I forgot to mention that it
> is a complex matrix. I tried loading the matrix I sent you this morning
> with:
>
> !...Load a Matrix in Binary Format
> call
> PetscViewerBinaryOpen(PETSC_COMM_WORLD,"Amat_binary.m",FILE_MODE_READ,viewer,ierr)
> call MatCreate(PETSC_COMM_WORLD,DLOAD,ierr)
> call MatSetType(DLOAD,MATAIJ,ierr)
> call MatLoad(DLOAD,viewer,ierr)
> call PetscViewerDestroy(viewer,ierr)
>
> call MatView(DLOAD,PETSC_VIEWER_STDOUT_WORLD,ierr)
>
> The first 37 rows should look like this:
>
> Mat Object: 2 MPI processes
> type: mpiaij
> row 0: (0, 1)
> row 1: (1, 1)
> row 2: (2, 1)
> row 3: (3, 1)
> row 4: (4, 1)
> row 5: (5, 1)
> row 6: (6, 1)
> row 7: (7, 1)
> row 8: (8, 1)
> row 9: (9, 1)
> row 10: (10, 1)
> row 11: (11, 1)
> row 12: (12, 1)
> row 13: (13, 1)
> row 14: (14, 1)
> row 15: (15, 1)
> row 16: (16, 1)
> row 17: (17, 1)
> row 18: (18, 1)
> row 19: (19, 1)
> row 20: (20, 1)
> row 21: (21, 1)
> row 22: (22, 1)
> row 23: (23, 1)
> row 24: (24, 1)
> row 25: (25, 1)
> row 26: (26, 1)
> row 27: (27, 1)
> row 28: (28, 1)
> row 29: (29, 1)
> row 30: (30, 1)
> row 31: (31, 1)
> row 32: (32, 1)
> row 33: (33, 1)
> row 34: (34, 1)
> row 35: (35, 1)
> row 36: (1, -41.2444) (35, -41.2444) (36, 118.049 - 0.999271 i) (37,
> -21.447) (38, 5.18873) (39, -2.34856) (40, 1.3607) (41, -0.898206)
> (42, 0.642715) (43, -0.48593) (44, 0.382471) (45, -0.310476) (46,
> 0.258302) (47, -0.219268) (48, 0.189304) (49, -0.165815) (50,
> 0.147076) (51, -0.131907) (52, 0.119478) (53, -0.109189) (54, 0.1006)
> (55, -0.0933795) (56, 0.0872779) (57, -0.0821019) (58, 0.0777011) (59,
> -0.0739575) (60, 0.0707775) (61, -0.0680868) (62, 0.0658258) (63,
> -0.0639473) (64, 0.0624137) (65, -0.0611954) (66, 0.0602698) (67,
> -0.0596202) (68, 0.0592349) (69, -0.0295536) (71, -21.447) (106,
> 5.18873) (141, -2.34856) (176, 1.3607) (211, -0.898206) (246,
> 0.642715) (281, -0.48593) (316, 0.382471) (351, -0.310476) (386,
> 0.258302) (421, -0.219268) (456, 0.189304) (491, -0.165815) (526,
> 0.147076) (561, -0.131907) (596, 0.119478) (631, -0.109189) (666,
> 0.1006) (701, -0.0933795) (736, 0.0872779) (771, -0.0821019) (806,
> 0.0777011) (841, -0.0739575) (876, 0.0707775) (911, -0.0680868) (946,
> 0.0658258) (981, -0.0639473) (1016, 0.0624137) (1051, -0.0611954)
> (1086, 0.0602698) (1121, -0.0596202) (1156, 0.0592349) (1191,
> -0.0295536) (1261, 0) (3676, 117.211) (3711, -58.4801) (3746,
> -78.3633) (3781, 29.4911) (3816, -15.8073) (3851, 9.94324) (3886,
> -6.87205) (3921, 5.05774) (3956, -3.89521) (3991, 3.10522) (4026,
> -2.54388) (4061, 2.13082) (4096, -1.8182) (4131, 1.57606) (4166,
> -1.38491) (4201, 1.23155) (4236, -1.10685) (4271, 1.00428) (4306,
> -0.919116) (4341, 0.847829) (4376, -0.787776) (4411, 0.736933) (4446,
> -0.693735) (4481, 0.656958) (4516, -0.625638) (4551, 0.599007) (4586,
> -0.576454) (4621, 0.557491) (4656, -0.541726) (4691, 0.528849) (4726,
> -0.518617) (4761, 0.51084) (4796, -0.50538) (4831, 0.502142) (4866,
> -0.250534)
>
>
> Thanks,
>
> Anthony
>
>
>
>
>
> On Fri, Jul 24, 2015 at 7:56 PM, Hong <hzhang at mcs.anl.gov> wrote:
>
>> Anthony:
>> I test your Amat_binary.m
>> using petsc/src/ksp/ksp/examples/tutorials/ex10.c.
>> Your matrix has many zero rows:
>> ./ex10 -f0 ~/tmp/Amat_binary.m -rhs 0 -mat_view |more
>> Mat Object: 1 MPI processes
>> type: seqaij
>> row 0: (0, 1)
>> row 1: (1, 0)
>> row 2: (2, 1)
>> row 3: (3, 0)
>> row 4: (4, 1)
>> row 5: (5, 0)
>> row 6: (6, 1)
>> row 7: (7, 0)
>> row 8: (8, 1)
>> row 9: (9, 0)
>> ...
>> row 36: (1, 1) (35, 0) (36, 1) (37, 0) (38, 1) (39, 0) (40, 1)
>> (41, 0) (42, 1) (43, 0) (44, 1) (45,
>> 0) (46, 1) (47, 0) (48, 1) (49, 0) (50, 1) (51, 0) (52, 1) (53,
>> 0) (54, 1) (55, 0) (56, 1) (57, 0)
>> (58, 1) (59, 0) (60, 1) ...
>>
>> Do you send us correct matrix?
>>
>>>
>>> I ran my code through valgrind and gdb as suggested by Barry. I am now
>>> coming back to some problem I have had while running with parallel symbolic
>>> factorization. I am attaching a test matrix (petsc binary format) that I LU
>>> decompose and then use to solve a linear system (see code below). I can run
>>> on 2 processors with parsymbfact or with 4 processors without parsymbfact.
>>> However, if I run on 4 procs with parsymbfact, the code is just hanging.
>>> Below is the simplified test case that I have used to test. The matrix A
>>> and B are built somewhere else in my program. The matrix I am attaching is
>>> A-sigma*B (see below).
>>>
>>> One thing is that I don't know for sparse matrices what is the optimum
>>> number of processors to use for a LU decomposition? Does it depend on the
>>> total number of nonzero? Do you have an easy way to compute it?
>>>
>>
>> You have to experiment your matrix on a target machine to find out.
>>
>> Hong
>>
>>>
>>>
>>>
>>> Subroutine HowBigLUCanBe(rank)
>>>
>>> IMPLICIT NONE
>>>
>>> integer(i4b),intent(in) :: rank
>>> integer(i4b) :: i,ct
>>> real(dp) :: begin,endd
>>> complex(dpc) :: sigma
>>>
>>> PetscErrorCode ierr
>>>
>>>
>>> if (rank==0) call cpu_time(begin)
>>>
>>> if (rank==0) then
>>> write(*,*)
>>> write(*,*)'Testing How Big LU Can Be...'
>>> write(*,*)'============================'
>>> write(*,*)
>>> endif
>>>
>>> sigma = (1.0d0,0.0d0)
>>> call MatAXPY(A,-sigma,B,DIFFERENT_NONZERO_PATTERN,ierr) ! on exit
>>> A = A-sigma*B
>>>
>>> !.....Write Matrix to ASCII and Binary Format
>>> !call PetscViewerASCIIOpen(PETSC_COMM_WORLD,"Amat.m",viewer,ierr)
>>> !call MatView(DXX,viewer,ierr)
>>> !call PetscViewerDestroy(viewer,ierr)
>>>
>>> call
>>> PetscViewerBinaryOpen(PETSC_COMM_WORLD,"Amat_binary.m",FILE_MODE_WRITE,viewer,ierr)
>>> call MatView(A,viewer,ierr)
>>> call PetscViewerDestroy(viewer,ierr)
>>>
>>> !.....Create Linear Solver Context
>>> call KSPCreate(PETSC_COMM_WORLD,ksp,ierr)
>>>
>>> !.....Set operators. Here the matrix that defines the linear system also
>>> serves as the preconditioning matrix.
>>> !call KSPSetOperators(ksp,A,A,DIFFERENT_NONZERO_PATTERN,ierr) !aha
>>> commented and replaced by next line
>>> call KSPSetOperators(ksp,A,A,ierr) ! remember: here A = A-sigma*B
>>>
>>> !.....Set Relative and Absolute Tolerances and Uses Default for
>>> Divergence Tol
>>> tol = 1.e-10
>>> call
>>> KSPSetTolerances(ksp,tol,tol,PETSC_DEFAULT_REAL,PETSC_DEFAULT_INTEGER,ierr)
>>>
>>> !.....Set the Direct (LU) Solver
>>> call KSPSetType(ksp,KSPPREONLY,ierr)
>>> call KSPGetPC(ksp,pc,ierr)
>>> call PCSetType(pc,PCLU,ierr)
>>> call PCFactorSetMatSolverPackage(pc,MATSOLVERSUPERLU_DIST,ierr) !
>>> MATSOLVERSUPERLU_DIST MATSOLVERMUMPS
>>>
>>> !.....Create Right-Hand-Side Vector
>>> call MatCreateVecs(A,frhs,PETSC_NULL_OBJECT,ierr)
>>> call MatCreateVecs(A,sol,PETSC_NULL_OBJECT,ierr)
>>>
>>> allocate(xwork1(IendA-IstartA))
>>> allocate(loc(IendA-IstartA))
>>>
>>> ct=0
>>> do i=IstartA,IendA-1
>>> ct=ct+1
>>> loc(ct)=i
>>> xwork1(ct)=(1.0d0,0.0d0)
>>> enddo
>>>
>>> call VecSetValues(frhs,IendA-IstartA,loc,xwork1,INSERT_VALUES,ierr)
>>> call VecZeroEntries(sol,ierr)
>>>
>>> deallocate(xwork1,loc)
>>>
>>> !.....Assemble Vectors
>>> call VecAssemblyBegin(frhs,ierr)
>>> call VecAssemblyEnd(frhs,ierr)
>>>
>>> !.....Solve the Linear System
>>> call KSPSolve(ksp,frhs,sol,ierr)
>>>
>>> !call VecView(sol,PETSC_VIEWER_STDOUT_WORLD,ierr)
>>>
>>> if (rank==0) then
>>> call cpu_time(endd)
>>> write(*,*)
>>> print '("Total time for HowBigLUCanBe = ",f21.3,"
>>> seconds.")',endd-begin
>>> endif
>>>
>>> call SlepcFinalize(ierr)
>>>
>>> STOP
>>>
>>>
>>> end Subroutine HowBigLUCanBe
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On 07/08/2015 11:23 AM, Xiaoye S. Li wrote:
>>>
>>> Indeed, the parallel symbolic factorization routine needs power of 2
>>> processes, however, you can use however many processes you need;
>>> internally, we redistribute matrix to nearest power of 2 processes, do
>>> symbolic, then redistribute back to all the processes to do factorization,
>>> triangular solve etc. So, there is no restriction from the users
>>> viewpoint.
>>>
>>> It's difficult to tell what the problem is. Do you think you can
>>> print your matrix, then, I can do some debugging by running superlu_dist
>>> standalone?
>>>
>>> Sherry
>>>
>>>
>>> On Wed, Jul 8, 2015 at 10:34 AM, Anthony Paul Haas <
>>> aph at email.arizona.edu> wrote:
>>>
>>>> Hi,
>>>>
>>>> I have used the switch -mat_superlu_dist_parsymbfact in my pbs script.
>>>> However, although my program worked fine with sequential symbolic
>>>> factorization, I get one of the following 2 behaviors when I run with
>>>> parallel symbolic factorization (depending on the number of processors that
>>>> I use):
>>>>
>>>> 1) the program just hangs (it seems stuck in some subroutine ==> see
>>>> test.out-hangs)
>>>> 2) I get a floating point exception ==> see
>>>> test.out-floating-point-exception
>>>>
>>>> Note that as suggested in the Superlu manual, I use a power of 2
>>>> number of procs. Are there any tunable parameters for the parallel symbolic
>>>> factorization? Note that when I build my sparse matrix, most elements I add
>>>> are nonzero of course but to simplify the programming, I also add a few
>>>> zero elements in the sparse matrix. I was thinking that maybe if the
>>>> parallel symbolic factorization proceed by block, there could be some
>>>> blocks where the pivot would be zero, hence creating the FPE??
>>>>
>>>> Thanks,
>>>>
>>>> Anthony
>>>>
>>>>
>>>>
>>>> On Wed, Jul 8, 2015 at 6:46 AM, Xiaoye S. Li <xsli at lbl.gov> wrote:
>>>>
>>>>> Did you find out how to change option to use parallel symbolic
>>>>> factorization? Perhaps PETSc team can help.
>>>>>
>>>>> Sherry
>>>>>
>>>>>
>>>>> On Tue, Jul 7, 2015 at 3:58 PM, Xiaoye S. Li <xsli at lbl.gov> wrote:
>>>>>
>>>>>> Is there an inquiry function that tells you all the available
>>>>>> options?
>>>>>>
>>>>>> Sherry
>>>>>>
>>>>>> On Tue, Jul 7, 2015 at 3:25 PM, Anthony Paul Haas <
>>>>>> aph at email.arizona.edu> wrote:
>>>>>>
>>>>>>> Hi Sherry,
>>>>>>>
>>>>>>> Thanks for your message. I have used superlu_dist default options.
>>>>>>> I did not realize that I was doing serial symbolic factorization. That is
>>>>>>> probably the cause of my problem.
>>>>>>> Each node on Garnet has 60GB usable memory and I can run with
>>>>>>> 1,2,4,8,16 or 32 core per node.
>>>>>>>
>>>>>>> So I should use:
>>>>>>>
>>>>>>> -mat_superlu_dist_r 20
>>>>>>> -mat_superlu_dist_c 32
>>>>>>>
>>>>>>> How do you specify the parallel symbolic factorization option? is
>>>>>>> it -mat_superlu_dist_matinput 1
>>>>>>>
>>>>>>> Thanks,
>>>>>>>
>>>>>>> Anthony
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Jul 7, 2015 at 3:08 PM, Xiaoye S. Li <xsli at lbl.gov> wrote:
>>>>>>>
>>>>>>>> For superlu_dist failure, this occurs during symbolic
>>>>>>>> factorization. Since you are using serial symbolic factorization, it
>>>>>>>> requires the entire graph of A to be available in the memory of one MPI
>>>>>>>> task. How much memory do you have for each MPI task?
>>>>>>>>
>>>>>>>> It won't help even if you use more processes. You should try to
>>>>>>>> use parallel symbolic factorization option.
>>>>>>>>
>>>>>>>> Another point. You set up process grid as:
>>>>>>>> Process grid nprow 32 x npcol 20
>>>>>>>> For better performance, you show swap the grid dimension. That
>>>>>>>> is, it's better to use 20 x 32, never gives nprow larger than npcol.
>>>>>>>>
>>>>>>>>
>>>>>>>> Sherry
>>>>>>>>
>>>>>>>>
>>>>>>>> On Tue, Jul 7, 2015 at 1:27 PM, Barry Smith <bsmith at mcs.anl.gov>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>>
>>>>>>>>> I would suggest running a sequence of problems, 101 by 101 111
>>>>>>>>> by 111 etc and get the memory usage in each case (when you run out of
>>>>>>>>> memory you can get NO useful information out about memory needs). You can
>>>>>>>>> then plot memory usage as a function of problem size to get a handle on how
>>>>>>>>> much memory it is using. You can also run on more and more processes
>>>>>>>>> (which have a total of more memory) to see how large a problem you may be
>>>>>>>>> able to reach.
>>>>>>>>>
>>>>>>>>> MUMPS also has an "out of core" version (which we have never
>>>>>>>>> used) that could in theory anyways let you get to large problems if you
>>>>>>>>> have lots of disk space, but you are on your own figuring out how to use it.
>>>>>>>>>
>>>>>>>>> Barry
>>>>>>>>>
>>>>>>>>> > On Jul 7, 2015, at 2:37 PM, Anthony Paul Haas <
>>>>>>>>> aph at email.arizona.edu> wrote:
>>>>>>>>> >
>>>>>>>>> > Hi Jose,
>>>>>>>>> >
>>>>>>>>> > In my code, I use once PETSc to solve a linear system to get the
>>>>>>>>> baseflow (without using SLEPc) and then I use SLEPc to do the stability
>>>>>>>>> analysis of that baseflow. This is why, there are some SLEPc options that
>>>>>>>>> are not used in test.out-superlu_dist-151x151 (when I am solving for the
>>>>>>>>> baseflow with PETSc only). I have attached a 101x101 case for which I get
>>>>>>>>> the eigenvalues. That case works fine. However If i increase to 151x151, I
>>>>>>>>> get the error that you can see in test.out-superlu_dist-151x151 (similar
>>>>>>>>> error with mumps: see test.out-mumps-151x151 line 2918 ). If you look a the
>>>>>>>>> very end of the files test.out-superlu_dist-151x151 and
>>>>>>>>> test.out-mumps-151x151, you will see that the last info message printed is:
>>>>>>>>> >
>>>>>>>>> > On Processor (after EPSSetFromOptions) 0 memory:
>>>>>>>>> 0.65073152000E+08 =====> (see line 807 of module_petsc.F90)
>>>>>>>>> >
>>>>>>>>> > This means that the memory error probably occurs in the call to
>>>>>>>>> EPSSolve (see module_petsc.F90 line 810). I would like to evaluate how much
>>>>>>>>> memory is required by the most memory intensive operation within EPSSolve.
>>>>>>>>> Since I am solving a generalized EVP, I would imagine that it would be the
>>>>>>>>> LU decomposition. But is there an accurate way of doing it?
>>>>>>>>> >
>>>>>>>>> > Before starting with iterative solvers, I would like to exploit
>>>>>>>>> as much as I can direct solvers. I tried GMRES with default preconditioner
>>>>>>>>> at some point but I had convergence problem. What solver/preconditioner
>>>>>>>>> would you recommend for a generalized non-Hermitian (EPS_GNHEP) EVP?
>>>>>>>>> >
>>>>>>>>> > Thanks,
>>>>>>>>> >
>>>>>>>>> > Anthony
>>>>>>>>> >
>>>>>>>>> > On Tue, Jul 7, 2015 at 12:17 AM, Jose E. Roman <
>>>>>>>>> jroman at dsic.upv.es> wrote:
>>>>>>>>> >
>>>>>>>>> > El 07/07/2015, a las 02:33, Anthony Haas escribió:
>>>>>>>>> >
>>>>>>>>> > > Hi,
>>>>>>>>> > >
>>>>>>>>> > > I am computing eigenvalues using PETSc/SLEPc and superlu_dist
>>>>>>>>> for the LU decomposition (my problem is a generalized eigenvalue problem).
>>>>>>>>> The code runs fine for a grid with 101x101 but when I increase to 151x151,
>>>>>>>>> I get the following error:
>>>>>>>>> > >
>>>>>>>>> > > Can't expand MemType 1: jcol 16104 (and then [NID 00037]
>>>>>>>>> 2015-07-06 19:19:17 Apid 31025976: OOM killer terminated this process.)
>>>>>>>>> > >
>>>>>>>>> > > It seems to be a memory problem. I monitor the memory usage as
>>>>>>>>> far as I can and it seems that memory usage is pretty low. The most memory
>>>>>>>>> intensive part of the program is probably the LU decomposition in the
>>>>>>>>> context of the generalized EVP. Is there a way to evaluate how much memory
>>>>>>>>> will be required for that step? I am currently running the debug version of
>>>>>>>>> the code which I would assume would use more memory?
>>>>>>>>> > >
>>>>>>>>> > > I have attached the output of the job. Note that the program
>>>>>>>>> uses twice PETSc: 1) to solve a linear system for which no problem occurs,
>>>>>>>>> and, 2) to solve the Generalized EVP with SLEPc, where I get the error.
>>>>>>>>> > >
>>>>>>>>> > > Thanks
>>>>>>>>> > >
>>>>>>>>> > > Anthony
>>>>>>>>> > > <test.out-superlu_dist-151x151>
>>>>>>>>> >
>>>>>>>>> > In the output you are attaching there are no SLEPc objects in
>>>>>>>>> the report and SLEPc options are not used. It seems that SLEPc calls are
>>>>>>>>> skipped?
>>>>>>>>> >
>>>>>>>>> > Do you get the same error with MUMPS? Have you tried to solve
>>>>>>>>> linear systems with a preconditioned iterative solver?
>>>>>>>>> >
>>>>>>>>> > Jose
>>>>>>>>> >
>>>>>>>>> >
>>>>>>>>> >
>>>>>>>>> <module_petsc.F90><test.out-mumps-151x151><test.out_superlu_dist-101x101><test.out-superlu_dist-151x151>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>>
>>
>
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