May 2024 - petsc-users - mailman3.cels.anl.gov

Spring 2024 PETSc meeting and release announcement 3.20
by Barry Smith 09 Nov '24

09 Nov '24

The May 23-24, 2024 PETSc annual meeting in Cologne Germany website is now available: https://cds.uni-koeln.de/en/workshops/petsc-2024/home Please register and start making your travel plans. We are pleased to announce the release of PETSc version 3.20.0 at https://petsc.org/release/download/ A list of the major changes and updates can be found at https://petsc.org/release/changes/320/ The release tarball location is changed from https://ftp.mcs.anl.gov/pub/petsc to https://web.cels.anl.gov/projects/petsc/download The final update to petsc-3.19 i.e petsc-3.19.6 is also available We recommend upgrading to PETSc 3.20.0 soon. As always, please report problems to petsc-maint(a)mcs.anl.gov and ask questions at petsc-users(a)mcs.anl.gov This release includes contributions from David Ham Iglesia Dolci Jack Betteridge Koki Sagiyama Marcin Rogowski Nathan Collier Aidan Hamilton Albert Cowie Barry Smith Blaise Bourdin Connor Ward David Andrs David Wells Duncan Campbell Hansol Suh Hong Zhang Jacob Faibussowitsch James Wright Jed Brown jeremy theler Joseph Pusztay Jose Roman Junchao Zhang Lisandro Dalcin Mark Adams Matthew Knepley Mr. Hong Zhang Pablo Brubeck Pierre Jolivet Pieter Ghysels Richard Tran Mills Satish Balay Sebastian Grimberg Stefano Zampini Suyash Tandon Toby Isaac YANG Zongze Zach Atkins and bug reports/proposed improvements received from Carl-Johan Thore Chris Douglas Daniel@OLCF Duan Junming Edoardo alinovi Philip Fackler Frederic Vi Getnet Betrie Aron Graham Hongrui Yu James Wright Jin Chen Jonas Lundgren Karsten Lettmann Boris Kaus LEONARDO MUTTI Maciej Lewkowicz maitri ksh Milan Pelletier Mr. Hong Zhang Ngoc Mai Monica Huynh Peng Wang Sébastien Gilles Daniel Taller Vibhas Mishra Wei Liu Li Xiaoye As always, thanks for your support, Barry

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How to combine different element types into a single DMPlex?
by Eric Chamberland 01 Aug '24

01 Aug '24

Hi, while playing with DMPlexBuildFromCellListParallel, I noticed we have to specify "numCorners" which is a fixed value, then gives a fixed number of nodes for a series of elements. How can I then add, for example, triangles and quadrangles into a DMPlex? Thanks, Eric -- Eric Chamberland, ing., M. Ing Professionnel de recherche GIREF/Université Laval (418) 656-2131 poste 41 22 42

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finite difference jacobian errors when given non-constant initial condition
by Mark Lohry 12 Jul '24

12 Jul '24

I have a 1-dimensional P1 discontinuous Galerkin discretization of the linear advection equation with 4 cells and periodic boundaries on [-pi,+pi]. I'm comparing the results from SNESComputeJacobian with a hand-written Jacobian. Being linear, the Jacobian should be constant/independent of the solution. When I set the initial condition passed to SNESComputeJacobian as some constant, say f(x)=1 or 0, the petsc finite difference jacobian agrees with my hand coded-version. But when I pass it some non-constant value, e.g. f(x)=sin(x), something goes horribly wrong in the petsc jacobian. Implementing my own rudimentary finite difference approximation (similar to how I thought petsc computes it) it returns the correct jacobian to expected error. Any idea what could be going on? Analytically computed Jacobian: 4.44089e-16 -1.10266 0.31831 -0.0852909 0 0 -0.31831 1.18795 1.10266 -4.44089e-16 -1.18795 0.31831 0 0 0.0852909 -0.31831 -0.31831 1.18795 4.44089e-16 -1.10266 0.31831 -0.0852909 0 0 0.0852909 -0.31831 1.10266 -4.44089e-16 -1.18795 0.31831 0 0 0 0 -0.31831 1.18795 4.44089e-16 -1.10266 0.31831 -0.0852909 0 0 0.0852909 -0.31831 1.10266 -4.44089e-16 -1.18795 0.31831 0.31831 -0.0852909 0 0 -0.31831 1.18795 4.44089e-16 -1.10266 -1.18795 0.31831 0 0 0.0852909 -0.31831 1.10266 -4.44089e-16 petsc finite difference jacobian when given f(x)=1: 4.44089e-16 -1.10266 0.31831 -0.0852909 0 0 -0.31831 1.18795 1.10266 -4.44089e-16 -1.18795 0.31831 0 0 0.0852909 -0.31831 -0.31831 1.18795 4.44089e-16 -1.10266 0.31831 -0.0852909 0 0 0.0852909 -0.31831 1.10266 -4.44089e-16 -1.18795 0.31831 0 0 0 0 -0.31831 1.18795 4.44089e-16 -1.10266 0.31831 -0.0852909 0 0 0.0852909 -0.31831 1.10266 -4.44089e-16 -1.18795 0.31831 0.31831 -0.0852909 0 0 -0.31831 1.18795 4.44089e-16 -1.10266 -1.18795 0.31831 0 0 0.0852909 -0.31831 1.10266 -4.44089e-16 petsc finite difference jacobian when given f(x) = sin(x): -1.65547e+08 -3.31856e+08 -1.25427e+09 4.4844e+08 0 0 1.03206e+08 7.86375e+07 9.13788e+07 1.83178e+08 6.92336e+08 -2.4753e+08 0 0 -5.69678e+07 -4.34064e+07 3.7084e+07 7.43387e+07 2.80969e+08 -1.00455e+08 -5.0384e+07 -2.99747e+07 0 0 3.7084e+07 7.43387e+07 2.80969e+08 -1.00455e+08 -5.0384e+07 -2.99747e+07 0 0 0 0 2.80969e+08 -1.00455e+08 -5.0384e+07 -2.99747e+07 -2.31191e+07 -1.76155e+07 0 0 2.80969e+08 -1.00455e+08 -5.0384e+07 -2.99747e+07 -2.31191e+07 -1.76155e+07 9.13788e+07 1.83178e+08 0 0 -1.24151e+08 -7.38608e+07 -5.69678e+07 -4.34064e+07 -1.65547e+08 -3.31856e+08 0 0 2.24919e+08 1.3381e+08 1.03206e+08 7.86375e+07

3 7

2^32 integer problems
by Carpenter, Mark H. (LARC-D302) 02 Jun '24

02 Jun '24

Mark Carpenter, NASA Langley. I am a novice PETSC user of about 10 years. I've build a DG-FEM code with petsc as one of the solver paths (I have my own as well). Furthermore, I use petsc for MPI communication. I'm running the DG-FEM code on our NAS supercomputer. Everything works when my integer sizes are small. When I exceed the 2^32 limit of integer arithmetic the code fails in very strange ways. The users that originally set up the petsc infrastructure in the code are no longer at NASA and I'm "dead in the water". I think I've promoted all the integers that are problematic in my code (F95). On PETSC side: I've tried 1. Reinstall petsc with -with-64-bit-integers (no luck) 2. Reinstall petsc with -with-64-bit-integers and -with-64-bit-indices (code will not compile with these options. Additional variables on F90 side require promotion and then the errors cascade through code when making PETSC calls. 3. It's possible that I've missed offending integers, but the petsc error messages are so cryptic that I can't even tell where it is failing. Further complicating matters: The problem by definition needs to be HUGE. Problem sizes requiring 1000 cores (10^6 elements at P5) are needed to experience the errors, which involves waiting in queues for ½ day at least. Attached are the 1. Install script used to install PETSC on our machine 2. The Makefile used on the fortran side 3. A data dump from an offending simulation (which is huge and I can't see any useful information.) How do I attack this problem. (I've never gotten debugging working properly). Mark

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Re: [petsc-users] Diagnosing Convergence Issue in Fieldsplit Problem
by Barry Smith 30 May '24

30 May '24

Use -pc_fieldsplit_0_ksp_type preonly > On May 23, 2024, at 12:43 PM, Colton Bryant <coltonbryant2021(a)u.northwestern.edu> wrote: > > That produces the following error: > > [0]PETSC ERROR: Residual norm computed by GMRES recursion formula 2.79175e-07 is far from the computed residual norm 0.000113154 at restart, residual norm at start of cycle 2.83065e-07 > [0]PETSC ERROR: See https://urldefense.us/v3/__https://petsc.org/release/faq/__;!!G_uCfscf7eWS!… for trouble shooting. > [0]PETSC ERROR: Petsc Release Version 3.21.0, unknown > [0]PETSC ERROR: ./mainOversetLS_exe on a arch-linux-c-opt named glass by colton Thu May 23 10:41:09 2024 > [0]PETSC ERROR: Configure options --download-mpich --with-cc=gcc --with-cxx=g++ --with-debugging=no --with-fc=gfortran COPTFLAGS=-O3 CXXOPTFLAGS=-O3 FOPTFLAGS=-O3 PETSC_ARCH=arch-linux-c-opt --download-sowing > [0]PETSC ERROR: #1 KSPGMRESCycle() at /home/colton/petsc/src/ksp/ksp/impls/gmres/gmres.c:115 > [0]PETSC ERROR: #2 KSPSolve_GMRES() at /home/colton/petsc/src/ksp/ksp/impls/gmres/gmres.c:227 > [0]PETSC ERROR: #3 KSPSolve_Private() at /home/colton/petsc/src/ksp/ksp/interface/itfunc.c:905 > [0]PETSC ERROR: #4 KSPSolve() at /home/colton/petsc/src/ksp/ksp/interface/itfunc.c:1078 > [0]PETSC ERROR: #5 PCApply_FieldSplit_Schur() at /home/colton/petsc/src/ksp/pc/impls/fieldsplit/fieldsplit.c:1203 > [0]PETSC ERROR: #6 PCApply() at /home/colton/petsc/src/ksp/pc/interface/precon.c:497 > [0]PETSC ERROR: #7 KSP_PCApply() at /home/colton/petsc/include/petsc/private/kspimpl.h:409 > [0]PETSC ERROR: #8 KSPFGMRESCycle() at /home/colton/petsc/src/ksp/ksp/impls/gmres/fgmres/fgmres.c:123 > [0]PETSC ERROR: #9 KSPSolve_FGMRES() at /home/colton/petsc/src/ksp/ksp/impls/gmres/fgmres/fgmres.c:235 > [0]PETSC ERROR: #10 KSPSolve_Private() at /home/colton/petsc/src/ksp/ksp/interface/itfunc.c:905 > [0]PETSC ERROR: #11 KSPSolve() at /home/colton/petsc/src/ksp/ksp/interface/itfunc.c:1078 > [0]PETSC ERROR: #12 solveStokes() at cartesianStokesGrid.cpp:1403 > > > > On Thu, May 23, 2024 at 10:33 AM Barry Smith <bsmith(a)petsc.dev <mailto:[email protected]>> wrote: >> >> Run the failing case with also -ksp_error_if_not_converged so we see exactly where the problem is first detected. >> >> >> >> >>> On May 23, 2024, at 11:51 AM, Colton Bryant <coltonbryant2021(a)u.northwestern.edu <mailto:[email protected]>> wrote: >>> >>> Hi Barry, >>> >>> Thanks for letting me know about the need to use fgmres in this case. I ran a smaller problem (1230 in the first block) and saw similar behavior in the true residual. >>> >>> I also ran the same problem with the options -fieldsplit_0_pc_type svd -fieldsplit_0_pc_svd_monitor and get the following output: >>> SVD: condition number 1.933639985881e+03, 0 of 1230 singular values are (nearly) zero >>> SVD: smallest singular values: 4.132036392141e-03 4.166444542385e-03 4.669534028645e-03 4.845532162256e-03 5.047038625390e-03 >>> SVD: largest singular values : 7.947990616611e+00 7.961437414477e+00 7.961851612473e+00 7.971335373142e+00 7.989870790960e+00 >>> >>> I would be surprised if the A_{00} block is ill conditioned as it's just a standard discretization of the laplacian with some rows replaced with ones on the diagonal due to interpolations from the overset mesh. I'm wondering if I'm somehow violating a solvability condition of the problem? >>> >>> Thanks for the help! >>> >>> -Colton >>> >>> On Wed, May 22, 2024 at 6:09 PM Barry Smith <bsmith(a)petsc.dev <mailto:[email protected]>> wrote: >>>> >>>> Thanks for the info. I see you are using GMRES inside the Schur complement solver, this is ok but when you do you need to use fgmres as the outer solver. But this is unlikely to be the cause of the exact problem you are seeing. >>>> >>>> I'm not sure why the Schur complement KSP is suddenly seeing a large increase in the true residual norm. Is it possible the A_{00} block is ill-conditioned? >>>> >>>> Can you run with a smaller problem? Say 2,000 or so in the first block? Is there still a problem? >>>> >>>> >>>> >>>> >>>> >>>>> On May 22, 2024, at 6:00 PM, Colton Bryant <coltonbryant2021(a)u.northwestern.edu <mailto:[email protected]>> wrote: >>>>> >>>>> Hi Barry, >>>>> >>>>> I have not used any other solver parameters in the code and the full set of solver related command line options are those I mentioned in the previous email. >>>>> >>>>> Below is the output from -ksp_view: >>>>> >>>>> KSP Object: (back_) 1 MPI process >>>>> type: gmres >>>>> restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement >>>>> happy breakdown tolerance 1e-30 >>>>> maximum iterations=10000, initial guess is zero >>>>> tolerances: relative=1e-08, absolute=1e-50, divergence=10000. >>>>> left preconditioning >>>>> using PRECONDITIONED norm type for convergence test >>>>> PC Object: (back_) 1 MPI process >>>>> type: fieldsplit >>>>> FieldSplit with Schur preconditioner, blocksize = 1, factorization FULL >>>>> Preconditioner for the Schur complement formed from S itself >>>>> Split info: >>>>> Split number 0 Defined by IS >>>>> Split number 1 Defined by IS >>>>> KSP solver for A00 block >>>>> KSP Object: (back_fieldsplit_0_) 1 MPI process >>>>> type: gmres >>>>> restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement >>>>> 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: (back_fieldsplit_0_) 1 MPI process >>>>> type: lu >>>>> out-of-place factorization >>>>> tolerance for zero pivot 2.22045e-14 >>>>> matrix ordering: nd >>>>> factor fill ratio given 5., needed 8.83482 >>>>> Factored matrix follows: >>>>> Mat Object: (back_fieldsplit_0_) 1 MPI process >>>>> type: seqaij >>>>> rows=30150, cols=30150 >>>>> package used to perform factorization: petsc >>>>> total: nonzeros=2649120, allocated nonzeros=2649120 >>>>> using I-node routines: found 15019 nodes, limit used is 5 >>>>> linear system matrix = precond matrix: >>>>> Mat Object: (back_fieldsplit_0_) 1 MPI process >>>>> type: seqaij >>>>> rows=30150, cols=30150 >>>>> total: nonzeros=299850, allocated nonzeros=299850 >>>>> total number of mallocs used during MatSetValues calls=0 >>>>> using I-node routines: found 15150 nodes, limit used is 5 >>>>> KSP solver for S = A11 - A10 inv(A00) A01 >>>>> KSP Object: (back_fieldsplit_1_) 1 MPI process >>>>> type: gmres >>>>> restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement >>>>> happy breakdown tolerance 1e-30 >>>>> maximum iterations=10000, initial guess is zero >>>>> tolerances: relative=1e-08, absolute=1e-50, divergence=10000. >>>>> left preconditioning >>>>> using PRECONDITIONED norm type for convergence test >>>>> PC Object: (back_fieldsplit_1_) 1 MPI process >>>>> type: none >>>>> linear system matrix = precond matrix: >>>>> Mat Object: (back_fieldsplit_1_) 1 MPI process >>>>> type: schurcomplement >>>>> rows=15000, cols=15000 >>>>> Schur complement A11 - A10 inv(A00) A01 >>>>> A11 >>>>> Mat Object: (back_fieldsplit_1_) 1 MPI process >>>>> type: seqaij >>>>> rows=15000, cols=15000 >>>>> total: nonzeros=74700, allocated nonzeros=74700 >>>>> total number of mallocs used during MatSetValues calls=0 >>>>> not using I-node routines >>>>> A10 >>>>> Mat Object: 1 MPI process >>>>> type: seqaij >>>>> rows=15000, cols=30150 >>>>> total: nonzeros=149550, allocated nonzeros=149550 >>>>> total number of mallocs used during MatSetValues calls=0 >>>>> not using I-node routines >>>>> KSP solver for A00 block viewable with the additional option -back_fieldsplit_0_ksp_view >>>>> A01 >>>>> Mat Object: 1 MPI process >>>>> type: seqaij >>>>> rows=30150, cols=15000 >>>>> total: nonzeros=149550, allocated nonzeros=149550 >>>>> total number of mallocs used during MatSetValues calls=0 >>>>> using I-node routines: found 15150 nodes, limit used is 5 >>>>> linear system matrix = precond matrix: >>>>> Mat Object: (back_) 1 MPI process >>>>> type: seqaij >>>>> rows=45150, cols=45150 >>>>> total: nonzeros=673650, allocated nonzeros=673650 >>>>> total number of mallocs used during MatSetValues calls=0 >>>>> has attached null space >>>>> using I-node routines: found 15150 nodes, limit used is 5 >>>>> >>>>> Thanks again! >>>>> >>>>> -Colton >>>>> >>>>> On Wed, May 22, 2024 at 3:39 PM Barry Smith <bsmith(a)petsc.dev <mailto:[email protected]>> wrote: >>>>>> >>>>>> Are you using any other command line options or did you hardwire any solver parameters in the code with, like, KSPSetXXX() or PCSetXXX() Please send all of them. >>>>>> >>>>>> Something funky definitely happened when the true residual norms jumped up. >>>>>> >>>>>> Could you run the same thing with -ksp_view and don't use any thing like -ksp_error_if_not_converged so we can see exactly what is being run. >>>>>> >>>>>> Barry >>>>>> >>>>>> >>>>>>> On May 22, 2024, at 3:21 PM, Colton Bryant <coltonbryant2021(a)u.northwestern.edu <mailto:[email protected]>> wrote: >>>>>>> >>>>>>> This Message Is From an External Sender >>>>>>> This message came from outside your organization. >>>>>>> Hello, >>>>>>> >>>>>>> I am solving the Stokes equations on a MAC grid discretized by finite differences using a DMSTAG object. I have tested the solver quite extensively on manufactured problems and it seems to work well. As I am still just trying to get things working and not yet worried about speed I am using the following solver options: >>>>>>> -pc_type fieldsplit >>>>>>> -pc_fieldsplit_detect_saddle_point >>>>>>> -fieldsplit_0_pc_type lu >>>>>>> -fieldsplit_1_ksp_rtol 1.e-8 >>>>>>> >>>>>>> However I am now using this solver as an inner step of a larger code and have run into issues. The code repeatedly solves the Stokes equations with varying right hand sides coming from changing problem geometry (the solver is a part of an overset grid scheme coupled to a level set method evolving in time). After a couple timesteps I observe the following output when running with -fieldsplit_1_ksp_converged_reason -fieldsplit_1_ksp_monitor_true_residual: >>>>>>> >>>>>>> Residual norms for back_fieldsplit_1_ solve. >>>>>>> 0 KSP preconditioned resid norm 2.826514299465e-02 true resid norm 2.826514299465e-02 ||r(i)||/||b|| 1.000000000000e+00 >>>>>>> 1 KSP preconditioned resid norm 7.286621865915e-03 true resid norm 7.286621865915e-03 ||r(i)||/||b|| 2.577953300039e-01 >>>>>>> 2 KSP preconditioned resid norm 1.500598474492e-03 true resid norm 1.500598474492e-03 ||r(i)||/||b|| 5.309007192273e-02 >>>>>>> 3 KSP preconditioned resid norm 3.796396924978e-04 true resid norm 3.796396924978e-04 ||r(i)||/||b|| 1.343137349666e-02 >>>>>>> 4 KSP preconditioned resid norm 8.091057439816e-05 true resid norm 8.091057439816e-05 ||r(i)||/||b|| 2.862556697960e-03 >>>>>>> 5 KSP preconditioned resid norm 3.689113122359e-05 true resid norm 3.689113122359e-05 ||r(i)||/||b|| 1.305181128239e-03 >>>>>>> 6 KSP preconditioned resid norm 2.116450533352e-05 true resid norm 2.116450533352e-05 ||r(i)||/||b|| 7.487846545662e-04 >>>>>>> 7 KSP preconditioned resid norm 3.968234031201e-06 true resid norm 3.968234031200e-06 ||r(i)||/||b|| 1.403932055801e-04 >>>>>>> 8 KSP preconditioned resid norm 6.666949419511e-07 true resid norm 6.666949419506e-07 ||r(i)||/||b|| 2.358717739644e-05 >>>>>>> 9 KSP preconditioned resid norm 1.941522884928e-07 true resid norm 1.941522884931e-07 ||r(i)||/||b|| 6.868965372998e-06 >>>>>>> 10 KSP preconditioned resid norm 6.729545258682e-08 true resid norm 6.729545258626e-08 ||r(i)||/||b|| 2.380863687793e-06 >>>>>>> 11 KSP preconditioned resid norm 3.009070131709e-08 true resid norm 3.009070131735e-08 ||r(i)||/||b|| 1.064586912687e-06 >>>>>>> 12 KSP preconditioned resid norm 7.849353009588e-09 true resid norm 7.849353009903e-09 ||r(i)||/||b|| 2.777043445840e-07 >>>>>>> 13 KSP preconditioned resid norm 2.306283345754e-09 true resid norm 2.306283346677e-09 ||r(i)||/||b|| 8.159461097060e-08 >>>>>>> 14 KSP preconditioned resid norm 9.336302495083e-10 true resid norm 9.336302502503e-10 ||r(i)||/||b|| 3.303115255517e-08 >>>>>>> 15 KSP preconditioned resid norm 6.537456143401e-10 true resid norm 6.537456141617e-10 ||r(i)||/||b|| 2.312903968982e-08 >>>>>>> 16 KSP preconditioned resid norm 6.389159552788e-10 true resid norm 6.389159550304e-10 ||r(i)||/||b|| 2.260437724130e-08 >>>>>>> 17 KSP preconditioned resid norm 6.380905134246e-10 true resid norm 6.380905136023e-10 ||r(i)||/||b|| 2.257517372981e-08 >>>>>>> 18 KSP preconditioned resid norm 6.380440605992e-10 true resid norm 6.380440604688e-10 ||r(i)||/||b|| 2.257353025207e-08 >>>>>>> 19 KSP preconditioned resid norm 6.380427156582e-10 true resid norm 6.380427157894e-10 ||r(i)||/||b|| 2.257348267830e-08 >>>>>>> 20 KSP preconditioned resid norm 6.380426714897e-10 true resid norm 6.380426714004e-10 ||r(i)||/||b|| 2.257348110785e-08 >>>>>>> 21 KSP preconditioned resid norm 6.380426656970e-10 true resid norm 6.380426658839e-10 ||r(i)||/||b|| 2.257348091268e-08 >>>>>>> 22 KSP preconditioned resid norm 6.380426650538e-10 true resid norm 6.380426650287e-10 ||r(i)||/||b|| 2.257348088242e-08 >>>>>>> 23 KSP preconditioned resid norm 6.380426649918e-10 true resid norm 6.380426645888e-10 ||r(i)||/||b|| 2.257348086686e-08 >>>>>>> 24 KSP preconditioned resid norm 6.380426649803e-10 true resid norm 6.380426644294e-10 ||r(i)||/||b|| 2.257348086122e-08 >>>>>>> 25 KSP preconditioned resid norm 6.380426649796e-10 true resid norm 6.380426649774e-10 ||r(i)||/||b|| 2.257348088061e-08 >>>>>>> 26 KSP preconditioned resid norm 6.380426649795e-10 true resid norm 6.380426653788e-10 ||r(i)||/||b|| 2.257348089481e-08 >>>>>>> 27 KSP preconditioned resid norm 6.380426649795e-10 true resid norm 6.380426646744e-10 ||r(i)||/||b|| 2.257348086989e-08 >>>>>>> 28 KSP preconditioned resid norm 6.380426649795e-10 true resid norm 6.380426650818e-10 ||r(i)||/||b|| 2.257348088430e-08 >>>>>>> 29 KSP preconditioned resid norm 6.380426649795e-10 true resid norm 6.380426649518e-10 ||r(i)||/||b|| 2.257348087970e-08 >>>>>>> 30 KSP preconditioned resid norm 6.380426652142e-10 true resid norm 6.380426652142e-10 ||r(i)||/||b|| 2.257348088898e-08 >>>>>>> 31 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426646799e-10 ||r(i)||/||b|| 2.257348087008e-08 >>>>>>> 32 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426648077e-10 ||r(i)||/||b|| 2.257348087460e-08 >>>>>>> 33 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426649048e-10 ||r(i)||/||b|| 2.257348087804e-08 >>>>>>> 34 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426648142e-10 ||r(i)||/||b|| 2.257348087483e-08 >>>>>>> 35 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426651079e-10 ||r(i)||/||b|| 2.257348088522e-08 >>>>>>> 36 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650433e-10 ||r(i)||/||b|| 2.257348088294e-08 >>>>>>> 37 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426649765e-10 ||r(i)||/||b|| 2.257348088057e-08 >>>>>>> 38 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650364e-10 ||r(i)||/||b|| 2.257348088269e-08 >>>>>>> 39 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650051e-10 ||r(i)||/||b|| 2.257348088159e-08 >>>>>>> 40 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426651154e-10 ||r(i)||/||b|| 2.257348088549e-08 >>>>>>> 41 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650246e-10 ||r(i)||/||b|| 2.257348088227e-08 >>>>>>> 42 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650702e-10 ||r(i)||/||b|| 2.257348088389e-08 >>>>>>> 43 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426651686e-10 ||r(i)||/||b|| 2.257348088737e-08 >>>>>>> 44 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650870e-10 ||r(i)||/||b|| 2.257348088448e-08 >>>>>>> 45 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426651208e-10 ||r(i)||/||b|| 2.257348088568e-08 >>>>>>> 46 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426651441e-10 ||r(i)||/||b|| 2.257348088650e-08 >>>>>>> 47 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650955e-10 ||r(i)||/||b|| 2.257348088478e-08 >>>>>>> 48 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650877e-10 ||r(i)||/||b|| 2.257348088451e-08 >>>>>>> 49 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426651240e-10 ||r(i)||/||b|| 2.257348088579e-08 >>>>>>> 50 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426650534e-10 ||r(i)||/||b|| 2.257348088329e-08 >>>>>>> 51 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426648615e-10 ||r(i)||/||b|| 2.257348087651e-08 >>>>>>> 52 KSP preconditioned resid norm 6.380426652141e-10 true resid norm 6.380426649523e-10 ||r(i)||/||b|| 2.257348087972e-08 >>>>>>> 53 KSP preconditioned resid norm 6.380426652140e-10 true resid norm 6.380426652601e-10 ||r(i)||/||b|| 2.257348089061e-08 >>>>>>> 54 KSP preconditioned resid norm 6.380426652125e-10 true resid norm 6.380427512852e-10 ||r(i)||/||b|| 2.257348393411e-08 >>>>>>> 55 KSP preconditioned resid norm 6.380426651849e-10 true resid norm 6.380603444402e-10 ||r(i)||/||b|| 2.257410636701e-08 >>>>>>> 56 KSP preconditioned resid norm 6.380426646751e-10 true resid norm 6.439925413105e-10 ||r(i)||/||b|| 2.278398313542e-08 >>>>>>> 57 KSP preconditioned resid norm 6.380426514019e-10 true resid norm 2.674218007058e-09 ||r(i)||/||b|| 9.461186902765e-08 >>>>>>> 58 KSP preconditioned resid norm 6.380425077384e-10 true resid norm 2.406759314486e-08 ||r(i)||/||b|| 8.514937691775e-07 >>>>>>> 59 KSP preconditioned resid norm 6.380406171326e-10 true resid norm 3.100137288622e-07 ||r(i)||/||b|| 1.096805803957e-05 >>>>>>> Linear back_fieldsplit_1_ solve did not converge due to DIVERGED_BREAKDOWN iterations 60 >>>>>>> >>>>>>> Any advice on steps I could take to elucidate the issue would be greatly appreciated. Thanks so much for any help in advance! >>>>>>> >>>>>>> Best, >>>>>>> Colton Bryant >>>>>> >>>> >>

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Question about DMGlobalToLocal()
by Runjian Wu 29 May '24

29 May '24

Hi all, I have two questions about this information. "DMGlobalToLocal() is a short form of DMGlobalToLocalBegin() and DMGlobalToLocalEnd()". (1) Is DMGlobalToLocal() recently added? Are DMGlobalToLocalBegin() and DMGlobalToLocalEnd() reserved for old code? (2) I can only use DMGlobalToLocal() to replace DMGlobalToLocalBegin() and DMGlobalToLocalEnd(), correct? Thanks, Runjian Wu

2 5

Question on MatMatmult
by Frank Bramkamp 29 May '24

29 May '24

3 4

Modify matrix nonzero structure
by Adrian Croucher 28 May '24

28 May '24

3 15

petsc4py and PCMGGetSmoother
by Klaij, Christiaan 28 May '24

28 May '24

I'm attempting some rapid prototyping with petsc4py. The idea is basically to read-in a matrix and rhs, setup GAMG as ksppreonly, get the smoother and overrule it with a python function of my own, similar to the demo where the Jacobi method is user-defined in python. So far I have something like this: pc = PETSc.PC().create() pc.setOperators(A) pc.setType(PETSc.PC.Type.GAMG) pc.PCMGGetSmoother(l=0,ksp=smoother) which triggers the following error: AttributeError: 'petsc4py.PETSc.PC' object has no attribute 'PCMGGetSmoother' 1) Am I doing something wrong, or is this function just not available through python? 2) How can I tell up front if a function is available, only if it is listed in libpetsc4py.pyx? 3) Given the function description in C from the manual pages, how can I deduce the python syntax? (perhaps it's supposed to be ksp = pc.PCMGGetSmoother(l=0), or something else entirely) Thanks for your help, dr. ir. Christiaan Klaij | Senior Researcher | Research & Development T +31 317 49 33 44 | C.Klaij(a)marin.nl | https://urldefense.us/v3/__http://www.marin.nl__;!!G_uCfscf7eWS!fuGSX2r2dc6…

3 2

[11 SEGV: Segmentation Violation] petsc4py.TAO
by Paganini, Alberto D.M. (Dr.) 27 May '24

27 May '24

Dear PETSc experts, I’m trying to set up a simple constrained optimization problem with petsc4py.TAO and I’m hitting a [0]PETSC ERROR: Caught signal number 11 SEGV: Segmentation Violation, probably memory access out of range Below is a minimal failing example (which also highlights how little I know about PETSc :) ). I hit this error because I was trying to understand how to assign values to the Jacobian of the equality constraint in the function myEqualityJacobian. Any advice on how to avoid the segmentation violation (and more generally, any advice about how to code this up better) are warmly welcomed. import petsc4py.PETSc as PETSc # initial guess x = PETSc.Vec().createSeq(2) x.set(0) x.assemble() print("initial guess: ", x.array_r) # create TAO solver and set solution solver = PETSc.TAO().create() solver.setType("almm") solver.setFromOptions() # add objective and gradient routines solver.setSolution(x) def myObjGrad(tao, x, g): J = x[0]**2 + x[1]**2 dJ = 2*x dJ.copy(g) return J solver.setObjectiveGradient(myObjGrad, None) # add equality constraint and its derivative def myEqualityConstraint(tao, x, ce): ce.set((x[0]-2)**2 + x[1]**2) ce = PETSc.Vec().createSeq(1) ce.assemble() solver.setEqualityConstraints(myEqualityConstraint, ce) def myEqualityJacobian(tao, x, JE, JEpre): v = PETSc.Vec().createSeq(2) v.setValue(0, 4) v.assemble() vals = 2*x - v # How should I enter vals into JE? col = JE.getColumnVector(0) vals.copy(col) #col = JE.getDenseColumnVec(0) #JE.restoreDenseColumnVec(0) solver.setJacobianEquality(myEqualityJacobian) # solve the problem solver.solve() print("found optimum at: ", solver.getSolution().array_r) solver.destroy() Many thanks in advance for your help. Best, Alberto

2 2