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 >>>> >>> >> >
