Hmm, Ok you can try running it directly in the debugger since it is one process, type
gdb ./blowup_batch_refine then when the debugger comes up (if it does not cut and paste all output and send it) run -on_error_abort -snes_mf_operator and any other options you normally use Barry > On Sep 7, 2015, at 8:18 PM, Gideon Simpson <[email protected]> wrote: > > Running with that flag gives me this: > > [0]PETSC ERROR: PETSC: Attaching gdb to ./blowup_batch_refine of pid 16111 on > gs_air > Unable to start debugger: No such file or directory > > > > -gideon > >> On Sep 7, 2015, at 9:11 PM, Barry Smith <[email protected]> wrote: >> >> >> This should not happen. Run with a debug version of PETSc installed and the >> option -start_in_debugger noxterm Once the debugger starts up type cont and >> when it crashes type where or bt Send all output >> >> >> >> Barry >> >> >>> On Sep 7, 2015, at 8:09 PM, Gideon Simpson <[email protected]> wrote: >>> >>> 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 >>>>> >>>> >>> >> >
