I’m getting an error with -snes_mf_operator, 0 SNES Function norm 1.421454390131e-02 [0]PETSC ERROR: ------------------------------------------------------------------------ [0]PETSC ERROR: Caught signal number 11 SEGV: Segmentation Violation, probably memory access out of range [0]PETSC ERROR: Try option -start_in_debugger or -on_error_attach_debugger [0]PETSC ERROR: or see http://www.mcs.anl.gov/petsc/documentation/faq.html#valgrind [0]PETSC ERROR: or try http://valgrind.org on GNU/linux and Apple Mac OS X to find memory corruption errors [0]PETSC ERROR: configure using --with-debugging=yes, recompile, link, and run [0]PETSC ERROR: to get more information on the crash. [0]PETSC ERROR: --------------------- Error Message -------------------------------------------------------------- [0]PETSC ERROR: Signal received [0]PETSC ERROR: See http://www.mcs.anl.gov/petsc/documentation/faq.html for trouble shooting. [0]PETSC ERROR: Petsc Release Version 3.5.3, unknown [0]PETSC ERROR: ./blowup_batch_refine on a arch-macports named gs_air by gideon Mon Sep 7 21:08:19 2015 [0]PETSC ERROR: Configure options --prefix=/opt/local --prefix=/opt/local/lib/petsc --with-valgrind=0 --with-shared-libraries --with-debugging=0 --with-c2html-dir=/opt/local --with-x=0 --with-blas-lapack-lib=/System/Library/Frameworks/Accelerate.framework/Versions/Current/Accelerate --with-hwloc-dir=/opt/local --with-suitesparse-dir=/opt/local --with-superlu-dir=/opt/local --with-metis-dir=/opt/local --with-parmetis-dir=/opt/local --with-scalapack-dir=/opt/local --with-mumps-dir=/opt/local --with-superlu_dist-dir=/opt/local CC=/opt/local/bin/mpicc-mpich-mp CXX=/opt/local/bin/mpicxx-mpich-mp FC=/opt/local/bin/mpif90-mpich-mp F77=/opt/local/bin/mpif90-mpich-mp F90=/opt/local/bin/mpif90-mpich-mp COPTFLAGS=-Os CXXOPTFLAGS=-Os FOPTFLAGS=-Os LDFLAGS="-L/opt/local/lib -Wl,-headerpad_max_install_names" CPPFLAGS=-I/opt/local/include CFLAGS="-Os -arch x86_64" CXXFLAGS=-Os FFLAGS=-Os FCFLAGS=-Os F90FLAGS=-Os PETSC_ARCH=arch-macports --with-mpiexec=mpiexec-mpich-mp [0]PETSC ERROR: #1 User provided function() line 0 in unknown file application called MPI_Abort(MPI_COMM_WORLD, 59) - process 0
-gideon > On Sep 7, 2015, at 9:01 PM, Barry Smith <[email protected]> wrote: > > > My guess is the Jacobian is not correct (or correct "enough"), hence PETSc > SNES is generating a poor descent direction. You can try > -snes_mf_operator -ksp_monitor_true residual as additional arguments. What > happens? > > Barry > > > >> On Sep 7, 2015, at 7:49 PM, Gideon Simpson <[email protected]> wrote: >> >> No problem Matt, I don’t think we had previously discussed that output. >> Here is a case where things fail. >> >> 0 SNES Function norm 4.027481756921e-09 >> 1 SNES Function norm 1.760477878365e-12 >> Nonlinear solve converged due to CONVERGED_SNORM_RELATIVE iterations 1 >> 0 SNES Function norm 5.066222213176e+03 >> 1 SNES Function norm 8.484697184230e+02 >> 2 SNES Function norm 6.549559723294e+02 >> 3 SNES Function norm 5.770723278153e+02 >> 4 SNES Function norm 5.237702240594e+02 >> 5 SNES Function norm 4.753909019848e+02 >> 6 SNES Function norm 4.221784590755e+02 >> 7 SNES Function norm 3.806525080483e+02 >> 8 SNES Function norm 3.762054656019e+02 >> 9 SNES Function norm 3.758975226873e+02 >> 10 SNES Function norm 3.757032042706e+02 >> 11 SNES Function norm 3.728798164234e+02 >> 12 SNES Function norm 3.723078741075e+02 >> 13 SNES Function norm 3.721848059825e+02 >> 14 SNES Function norm 3.720227575629e+02 >> 15 SNES Function norm 3.720051998555e+02 >> 16 SNES Function norm 3.718945430587e+02 >> 17 SNES Function norm 3.700412694044e+02 >> 18 SNES Function norm 3.351964889461e+02 >> 19 SNES Function norm 3.096016086233e+02 >> 20 SNES Function norm 3.008410789787e+02 >> 21 SNES Function norm 2.752316716557e+02 >> 22 SNES Function norm 2.707658474165e+02 >> 23 SNES Function norm 2.698436736049e+02 >> 24 SNES Function norm 2.618233857172e+02 >> 25 SNES Function norm 2.600121920634e+02 >> 26 SNES Function norm 2.585046423168e+02 >> 27 SNES Function norm 2.568551090220e+02 >> 28 SNES Function norm 2.556404537064e+02 >> 29 SNES Function norm 2.536353523683e+02 >> 30 SNES Function norm 2.533596070171e+02 >> 31 SNES Function norm 2.532324379596e+02 >> 32 SNES Function norm 2.531842335211e+02 >> 33 SNES Function norm 2.531684527520e+02 >> 34 SNES Function norm 2.531637604618e+02 >> 35 SNES Function norm 2.531624767821e+02 >> 36 SNES Function norm 2.531621359093e+02 >> 37 SNES Function norm 2.531620504925e+02 >> 38 SNES Function norm 2.531620350055e+02 >> 39 SNES Function norm 2.531620310522e+02 >> 40 SNES Function norm 2.531620300471e+02 >> 41 SNES Function norm 2.531620298084e+02 >> 42 SNES Function norm 2.531620297478e+02 >> 43 SNES Function norm 2.531620297324e+02 >> 44 SNES Function norm 2.531620297303e+02 >> 45 SNES Function norm 2.531620297302e+02 >> Nonlinear solve did not converge due to DIVERGED_LINE_SEARCH iterations 45 >> 0 SNES Function norm 9.636339304380e+03 >> 1 SNES Function norm 8.997731184634e+03 >> 2 SNES Function norm 8.120498349232e+03 >> 3 SNES Function norm 7.322379894820e+03 >> 4 SNES Function norm 6.599581599149e+03 >> 5 SNES Function norm 6.374872854688e+03 >> 6 SNES Function norm 6.372518007653e+03 >> 7 SNES Function norm 6.073996314301e+03 >> 8 SNES Function norm 5.635965277054e+03 >> 9 SNES Function norm 5.155389064046e+03 >> 10 SNES Function norm 5.080567902638e+03 >> 11 SNES Function norm 5.058878643969e+03 >> 12 SNES Function norm 5.058835649793e+03 >> 13 SNES Function norm 5.058491285707e+03 >> 14 SNES Function norm 5.057452865337e+03 >> 15 SNES Function norm 5.057226140688e+03 >> 16 SNES Function norm 5.056651272898e+03 >> 17 SNES Function norm 5.056575190057e+03 >> 18 SNES Function norm 5.056574632598e+03 >> 19 SNES Function norm 5.056574520229e+03 >> 20 SNES Function norm 5.056574492569e+03 >> 21 SNES Function norm 5.056574485124e+03 >> 22 SNES Function norm 5.056574483029e+03 >> 23 SNES Function norm 5.056574482427e+03 >> 24 SNES Function norm 5.056574482302e+03 >> 25 SNES Function norm 5.056574482287e+03 >> 26 SNES Function norm 5.056574482282e+03 >> 27 SNES Function norm 5.056574482281e+03 >> Nonlinear solve did not converge due to DIVERGED_LINE_SEARCH iterations 27 >> SNES Object: 1 MPI processes >> type: newtonls >> maximum iterations=50, maximum function evaluations=10000 >> tolerances: relative=1e-08, absolute=1e-50, solution=1e-08 >> total number of linear solver iterations=28 >> total number of function evaluations=323 >> total number of grid sequence refinements=2 >> SNESLineSearch Object: 1 MPI processes >> type: bt >> interpolation: cubic >> alpha=1.000000e-04 >> maxstep=1.000000e+08, minlambda=1.000000e-12 >> tolerances: relative=1.000000e-08, absolute=1.000000e-15, >> lambda=1.000000e-08 >> maximum iterations=40 >> KSP Object: 1 MPI processes >> type: gmres >> GMRES: restart=30, using Classical (unmodified) Gram-Schmidt >> Orthogonalization with no iterative refinement >> GMRES: happy breakdown tolerance 1e-30 >> maximum iterations=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: 1 MPI processes >> type: lu >> LU: out-of-place factorization >> tolerance for zero pivot 2.22045e-14 >> matrix ordering: nd >> factor fill ratio given 0, needed 0 >> Factored matrix follows: >> Mat Object: 1 MPI processes >> type: seqaij >> rows=15991, cols=15991 >> package used to perform factorization: mumps >> total: nonzeros=255801, allocated nonzeros=255801 >> total number of mallocs used during MatSetValues calls =0 >> MUMPS run parameters: >> SYM (matrix type): 0 >> 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):6 >> ICNTL(8) (scalling strategy): 77 >> ICNTL(10) (max num of refinements): 0 >> ICNTL(11) (error analysis): 0 >> ICNTL(12) (efficiency control): 1 >> 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) (somumpstion 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): -8 >> 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) (absomumpste pivoting threshold): 0 >> CNTL(4) (vamumpse of static pivoting): -1 >> CNTL(5) (fixation for null pivots): 0 >> RINFO(1) (local estimated flops for the elimination after >> analysis): >> [0] 1.95838e+06 >> RINFO(2) (local estimated flops for the assembly after >> factorization): >> [0] 143924 >> RINFO(3) (local estimated flops for the elimination after >> factorization): >> [0] 1.95943e+06 >> INFO(15) (estimated size of (in MB) MUMPS internal data for >> running numerical factorization): >> [0] 7 >> INFO(16) (size of (in MB) MUMPS internal data used during >> numerical factorization): >> [0] 7 >> INFO(23) (num of pivots eliminated on this processor after >> factorization): >> [0] 15991 >> RINFOG(1) (global estimated flops for the elimination after >> analysis): 1.95838e+06 >> RINFOG(2) (global estimated flops for the assembly after >> factorization): 143924 >> RINFOG(3) (global estimated flops for the elimination after >> factorization): 1.95943e+06 >> (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (0,0)*(2^0) >> INFOG(3) (estimated real workspace for factors on all >> processors after analysis): 255801 >> INFOG(4) (estimated integer workspace for factors on all >> processors after analysis): 127874 >> INFOG(5) (estimated maximum front size in the complete tree): >> 11 >> INFOG(6) (number of nodes in the complete tree): 3996 >> INFOG(7) (ordering option effectively use after analysis): 6 >> INFOG(8) (structural symmetry in percent of the permuted >> matrix after analysis): 86 >> INFOG(9) (total real/complex workspace to store the matrix >> factors after factorization): 255865 >> INFOG(10) (total integer space store the matrix factors after >> factorization): 127890 >> INFOG(11) (order of largest frontal matrix after >> factorization): 11 >> INFOG(12) (number of off-diagonal pivots): 19 >> INFOG(13) (number of delayed pivots after factorization): 8 >> 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): 7 >> INFOG(17) (estimated size of all MUMPS internal data for >> factorization after analysis: sum over all processors): 7 >> INFOG(18) (size of all MUMPS internal data allocated during >> factorization: value on the most memory consuming processor): 7 >> INFOG(19) (size of all MUMPS internal data allocated during >> factorization: sum over all processors): 7 >> INFOG(20) (estimated number of entries in the factors): >> 255801 >> INFOG(21) (size in MB of memory effectively used during >> factorization - value on the most memory consuming processor): 7 >> INFOG(22) (size in MB of memory effectively used during >> factorization - sum over all processors): 7 >> 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)): 255865 >> INFOG(30, 31) (after solution: size in Mbytes of memory used >> during solution phase): 5, 5 >> 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 >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: seqaij >> rows=15991, cols=15991 >> total: nonzeros=223820, allocated nonzeros=431698 >> total number of mallocs used during MatSetValues calls =15991 >> using I-node routines: found 4000 nodes, limit used is 5 >> >> >> >> >> -gideon >> >>> On Sep 7, 2015, at 8:40 PM, Matthew Knepley <[email protected]> wrote: >>> >>> On Mon, Sep 7, 2015 at 7:32 PM, Gideon Simpson <[email protected]> >>> wrote: >>> Barry, >>> >>> I finally got a chance to really try using the grid sequencing within my >>> code. I find that, in some cases, even if it can solve successfully on the >>> coarsest mesh, the SNES fails, usually due to a line search failure, when >>> it tries to compute along the grid sequence. Would you have any >>> suggestions? >>> >>> I apologize if I have asked before, but can you give me -snes_view for the >>> solver? I could not find it in the email thread. >>> >>> I would suggest trying to fiddle with the line search, or precondition it >>> with Richardson. It would be nice to see -snes_monitor >>> for the runs that fail, and then we can break down the residual into fields >>> and look at it again (if my custom residual monitor >>> does not work we can write one easily). Seeing which part of the residual >>> does not converge is key to designing the NASM >>> for the problem. I have just seen the virtuoso of this, Xiao-Chuan Cai, >>> present it. We need better monitoring in PETSc. >>> >>> Thanks, >>> >>> Matt >>> >>> -gideon >>> >>>> On Aug 28, 2015, at 4:21 PM, Barry Smith <[email protected]> wrote: >>>> >>>> >>>>> On Aug 28, 2015, at 3:04 PM, Gideon Simpson <[email protected]> >>>>> wrote: >>>>> >>>>> Yes, if i continue in this parameter on the coarse mesh, I can generally >>>>> solve at all values. I do find that I need to do some amount of >>>>> continuation to solve near the endpoint. The problem is that on the >>>>> coarse mesh, things are not fully resolved at all the values along the >>>>> continuation parameter, and I would like to do refinement. >>>>> >>>>> One subtlety is that I actually want the intermediate continuation >>>>> solutions too. Currently, without doing any grid sequence, I compute >>>>> each, write it to disk, and then go on to the next one. So I now need to >>>>> go back an refine them. I was thinking that perhaps I could refine them >>>>> on the fly, dump them to disk, and use the coarse solution as the >>>>> starting guess at the next iteration, but that would seem to require >>>>> resetting the snes back to the coarse grid. >>>>> >>>>> The alternative would be to just script the mesh refinement in a post >>>>> processing stage, where each value of the continuation is parameter is >>>>> loaded on the coarse mesh, and refined. Perhaps that’s the most >>>>> practical thing to do. >>>> >>>> I would do the following. Create your DM and create a SNES that will do >>>> the continuation >>>> >>>> loop over continuation parameter >>>> >>>> SNESSolve(snes,NULL,Ucoarse); >>>> >>>> if (you decide you want to see the refined solution at this >>>> continuation point) { >>>> SNESCreate(comm,&snesrefine); >>>> SNESSetDM() >>>> etc >>>> SNESSetGridSequence(snesrefine,) >>>> SNESSolve(snesrefine,0,Ucoarse); >>>> SNESGetSolution(snesrefine,&Ufine); >>>> VecView(Ufine or do whatever you want to do with the Ufine at >>>> that continuation point >>>> SNESDestroy(snesrefine); >>>> end if >>>> >>>> end loop over continuation parameter. >>>> >>>> Barry >>>> >>>>> >>>>> -gideon >>>>> >>>>>> On Aug 28, 2015, at 3:55 PM, Barry Smith <[email protected]> wrote: >>>>>> >>>>>>> >>>>>>> >>>>>>> 3. This problem is actually part of a continuation problem that >>>>>>> roughly looks like this >>>>>>> >>>>>>> for( continuation parameter p = 0 to 1){ >>>>>>> >>>>>>> solve with parameter p_i using solution from p_{i-1}, >>>>>>> } >>>>>>> >>>>>>> What I would like to do is to start the solver, for each value of >>>>>>> parameter p_i on the coarse mesh, and then do grid sequencing on that. >>>>>>> But it appears that after doing grid sequencing on the initial p_0 = 0, >>>>>>> the SNES is set to use the finer mesh. >>>>>> >>>>>> So you are using continuation to give you a good enough initial guess on >>>>>> the coarse level to even get convergence on the coarse level? First I >>>>>> would check if you even need the continuation (or can you not even solve >>>>>> the coarse problem without it). >>>>>> >>>>>> If you do need the continuation then you will need to tweak how you do >>>>>> the grid sequencing. I think this will work: >>>>>> >>>>>> Do not use -snes_grid_sequencing >>>>>> >>>>>> Run SNESSolve() as many times as you want with your continuation >>>>>> parameter. This will all happen on the coarse mesh. >>>>>> >>>>>> Call SNESSetGridSequence() >>>>>> >>>>>> Then call SNESSolve() again and it will do one solve on the coarse level >>>>>> and then interpolate to the next level etc. >>>>> >>>> >>> >>> >>> >>> >>> -- >>> 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 >> >
