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 >>> >> >
