Stephan Kramer has a reproducer (see this thread) with 1500 processors. They have lots of tests and this seemed to be the only, or the smallest, one that failed.
I added a switch in GAMG to use your "low memory filter" to use this. Stephan is accommodating and would test a fix. Stephan has stack traces (4 Oct) in this thread and one, as I recall, hung waiting for MPI receives on an "nrecv" member of Mat. I think "nrecv" has to be recomputed because, my guess, the filter removed a processor edge and the send processor did not have any data to send anymore and did not send an empty message. Just a guess. Thanks, Mark On Fri, Oct 6, 2023 at 12:30 AM Pierre Jolivet <pie...@joliv.et> wrote: > > > On Oct 6, 2023, at 3:48 AM, Mark Adams <mfad...@lbl.gov> wrote: > > > Pierre, (moved to dev) > > It looks like there is a subtle bug in the new MatFilter. > My guess is that after the compression/filter the communication buffers > and lists need to be recomputed because the graph has changed. > > > Maybe an issue with MatHeaderReplace()? > Do you have a reproducer? > I use this routine for AIJ, BAIJ, and SBAIJ and never ran into this > (though the subsequent Mat is not involved in the same kind of operations > as in GAMG). > > Thanks, > Pierre > > And, the Mat-Mat Mults failed or hung because the communication > requirements, as seen in the graph, did not match the cached communication > lists. > The old way just created a whole new matrix, which took care of that. > > Mark > > > > On Thu, Oct 5, 2023 at 8:51 PM Mark Adams <mfad...@lbl.gov> wrote: > >> Fantastic, it will get merged soon. >> >> Thank you for your diligence and patience. >> This would have been a time bomb waiting to explode. >> >> Mark >> >> On Thu, Oct 5, 2023 at 7:23 PM Stephan Kramer <s.kra...@imperial.ac.uk> >> wrote: >> >>> Great, that seems to fix the issue indeed - i.e. on the branch with the >>> low memory filtering switched off (by default) we no longer see the >>> "inconsistent data" error or hangs, and going back to the square graph >>> aggressive coarsening brings us back the old performance. So we'd be >>> keen to have that branch merged indeed >>> Many thanks for your assistance with this >>> Stephan >>> >>> On 05/10/2023 01:11, Mark Adams wrote: >>> > Thanks Stephan, >>> > >>> > It looks like the matrix is in a bad/incorrect state and parallel >>> Mat-Mat >>> > is waiting for messages that were not sent. A bug. >>> > >>> > Can you try my branch, which is ready to merge, adams/gamg-fast-filter. >>> > We added a new filtering method in main that uses low memory but I >>> found it >>> > was slow, so this branch brings back the old filter code, used by >>> default, >>> > and keeps the low memory version as an option. >>> > It is possible this low memory filtering messed up the internals of >>> the Mat >>> > in some way. >>> > I hope this is it, but if not we can continue. >>> > >>> > This MR also makes square graph the default. >>> > I have found it does create better aggregates and on GPUs, with Kokkos >>> bug >>> > fixes from Junchao, Mat-Mat is fast. (it might be slow on CPUs) >>> > >>> > Mark >>> > >>> > >>> > >>> > >>> > On Wed, Oct 4, 2023 at 12:30 AM Stephan Kramer < >>> s.kra...@imperial.ac.uk> >>> > wrote: >>> > >>> >> Hi Mark >>> >> >>> >> Thanks again for re-enabling the square graph aggressive coarsening >>> >> option which seems to have restored performance for most of our cases. >>> >> Unfortunately we do have a remaining issue, which only seems to occur >>> >> for the larger mesh size ("level 7" which has 6,389,890 vertices and >>> we >>> >> normally run on 1536 cpus): we either get a "Petsc has generated >>> >> inconsistent data" error, or a hang - both when constructing the >>> square >>> >> graph matrix. So this is with the new >>> >> -pc_gamg_aggressive_square_graph=true option, without the option >>> there's >>> >> no error but of course we would get back to the worse performance. >>> >> >>> >> Backtrace for the "inconsistent data" error. Note this is actually >>> just >>> >> petsc main from 17 Sep, git 9a75acf6e50cfe213617e - so after your >>> merge >>> >> of adams/gamg-add-old-coarsening into main - with one unrelated commit >>> >> from firedrake >>> >> >>> >> [0]PETSC ERROR: --------------------- Error Message >>> >> -------------------------------------------------------------- >>> >> [0]PETSC ERROR: Petsc has generated inconsistent data >>> >> [0]PETSC ERROR: j 8 not equal to expected number of sends 9 >>> >> [0]PETSC ERROR: Petsc Development GIT revision: >>> >> v3.4.2-43104-ga3b76b71a1 GIT Date: 2023-09-18 10:26:04 +0100 >>> >> [0]PETSC ERROR: stokes_cubed_sphere_7e3_A3_TS1.py on a named >>> >> gadi-cpu-clx-0241.gadi.nci.org.au by sck551 Wed Oct 4 14:30:41 2023 >>> >> [0]PETSC ERROR: Configure options --prefix=/tmp/firedrake-prefix >>> >> --with-make-np=4 --with-debugging=0 --with-shared-libraries=1 >>> >> --with-fortran-bindings=0 --with-zlib --with-c2html=0 >>> >> --with-mpiexec=mpiexec --with-cc=mpicc --with-cxx=mpicxx >>> >> --with-fc=mpifort --download-hdf5 --download-hypre >>> >> --download-superlu_dist --download-ptscotch --download-suitesparse >>> >> --download-pastix --download-hwloc --download-metis >>> --download-scalapack >>> >> --download-mumps --download-chaco --download-ml >>> >> CFLAGS=-diag-disable=10441 CXXFLAGS=-diag-disable=10441 >>> >> [0]PETSC ERROR: #1 PetscGatherMessageLengths2() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/sys/utils/mpimesg.c:270 >>> >> [0]PETSC ERROR: #2 MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ() at >>> >> >>> /jobfs/95504034.gadi-pbs/petsc/src/mat/impls/aij/mpi/mpimatmatmult.c:1867 >>> >> [0]PETSC ERROR: #3 MatProductSymbolic_AtB_MPIAIJ_MPIAIJ() at >>> >> >>> /jobfs/95504034.gadi-pbs/petsc/src/mat/impls/aij/mpi/mpimatmatmult.c:2071 >>> >> [0]PETSC ERROR: #4 MatProductSymbolic() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/mat/interface/matproduct.c:795 >>> >> [0]PETSC ERROR: #5 PCGAMGSquareGraph_GAMG() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/impls/gamg/gamg.c:489 >>> >> [0]PETSC ERROR: #6 PCGAMGCoarsen_AGG() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/impls/gamg/agg.c:969 >>> >> [0]PETSC ERROR: #7 PCSetUp_GAMG() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/impls/gamg/gamg.c:645 >>> >> [0]PETSC ERROR: #8 PCSetUp() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/interface/precon.c:1069 >>> >> [0]PETSC ERROR: #9 PCApply() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/interface/precon.c:484 >>> >> [0]PETSC ERROR: #10 PCApply() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/interface/precon.c:487 >>> >> [0]PETSC ERROR: #11 KSP_PCApply() at >>> >> /jobfs/95504034.gadi-pbs/petsc/include/petsc/private/kspimpl.h:383 >>> >> [0]PETSC ERROR: #12 KSPSolve_CG() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/ksp/impls/cg/cg.c:162 >>> >> [0]PETSC ERROR: #13 KSPSolve_Private() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/ksp/interface/itfunc.c:910 >>> >> [0]PETSC ERROR: #14 KSPSolve() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/ksp/interface/itfunc.c:1082 >>> >> [0]PETSC ERROR: #15 PCApply_FieldSplit_Schur() at >>> >> >>> >> >>> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/impls/fieldsplit/fieldsplit.c:1175 >>> >> [0]PETSC ERROR: #16 PCApply() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/interface/precon.c:487 >>> >> [0]PETSC ERROR: #17 KSP_PCApply() at >>> >> /jobfs/95504034.gadi-pbs/petsc/include/petsc/private/kspimpl.h:383 >>> >> [0]PETSC ERROR: #18 KSPSolve_PREONLY() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/ksp/impls/preonly/preonly.c:25 >>> >> [0]PETSC ERROR: #19 KSPSolve_Private() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/ksp/interface/itfunc.c:910 >>> >> [0]PETSC ERROR: #20 KSPSolve() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/ksp/interface/itfunc.c:1082 >>> >> [0]PETSC ERROR: #21 SNESSolve_KSPONLY() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/snes/impls/ksponly/ksponly.c:49 >>> >> [0]PETSC ERROR: #22 SNESSolve() at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/snes/interface/snes.c:4635 >>> >> >>> >> Last -info :pc messages: >>> >> >>> >> [0] <pc:gamg> PCSetUp(): Setting up PC for first time >>> >> [0] <pc:gamg> PCSetUp_GAMG(): Stokes_fieldsplit_0_assembled_: level 0) >>> >> N=152175366, n data rows=3, n data cols=6, nnz/row (ave)=191, np=1536 >>> >> [0] <pc:gamg> PCGAMGCreateGraph_AGG(): Filtering left 100. % edges in >>> >> graph (1.588710e+07 1.765233e+06) >>> >> [0] <pc:gamg> PCGAMGSquareGraph_GAMG(): >>> Stokes_fieldsplit_0_assembled_: >>> >> Square Graph on level 1 >>> >> [0] <pc:gamg> fixAggregatesWithSquare(): isMPI = yes >>> >> [0] <pc:gamg> PCGAMGProlongator_AGG(): Stokes_fieldsplit_0_assembled_: >>> >> New grid 380144 nodes >>> >> [0] <pc:gamg> PCGAMGOptProlongator_AGG(): >>> >> Stokes_fieldsplit_0_assembled_: Smooth P0: max eigen=4.489376e+00 >>> >> min=9.015236e-02 PC=jacobi >>> >> [0] <pc:gamg> PCGAMGOptProlongator_AGG(): >>> >> Stokes_fieldsplit_0_assembled_: Smooth P0: level 0, cache spectra >>> >> 0.0901524 4.48938 >>> >> [0] <pc:gamg> PCGAMGCreateLevel_GAMG(): >>> Stokes_fieldsplit_0_assembled_: >>> >> Coarse grid reduction from 1536 to 1536 active processes >>> >> [0] <pc:gamg> PCSetUp_GAMG(): Stokes_fieldsplit_0_assembled_: 1) >>> >> N=2280864, n data cols=6, nnz/row (ave)=503, 1536 active pes >>> >> [0] <pc:gamg> PCGAMGCreateGraph_AGG(): Filtering left 36.2891 % edges >>> in >>> >> graph (5.310360e+05 5.353000e+03) >>> >> [0] <pc:gamg> PCGAMGSquareGraph_GAMG(): >>> Stokes_fieldsplit_0_assembled_: >>> >> Square Graph on level 2 >>> >> >>> >> The hang (on a slightly different model configuration but on the same >>> >> mesh and n/o cores) seems to occur in the same location. If I use gdb >>> to >>> >> attach to the running processes, it seems on some cores it has somehow >>> >> manages to fall out of the pcsetup and is waiting in the first norm >>> >> calculation in the outside CG iteration: >>> >> >>> >> #0 0x000014cce9999119 in >>> >> hmca_bcol_basesmuma_bcast_k_nomial_knownroot_progress () from >>> >> /apps/hcoll/4.7.3202/lib/hcoll/hmca_bcol_basesmuma.so >>> >> #1 0x000014ccef2c2737 in _coll_ml_allreduce () from >>> >> /apps/hcoll/4.7.3202/lib/libhcoll.so.1 >>> >> #2 0x000014ccef5dd95b in mca_coll_hcoll_allreduce (sbuf=0x1, >>> >> rbuf=0x7fff74ecbee8, count=1, dtype=0x14cd26ce6f80 <ompi_mpi_double>, >>> >> op=0x14cd26cfbc20 <ompi_mpi_op_sum>, comm=0x3076fb0, module=0x43a0110) >>> >> at >>> >> >>> >> >>> /jobfs/35226956.gadi-pbs/0/openmpi/4.0.7/source/openmpi-4.0.7/ompi/mca/coll/hcoll/coll_hcoll_ops.c:228 >>> >> #3 0x000014cd26a1de28 in PMPI_Allreduce (sendbuf=0x1, >>> >> recvbuf=<optimized out>, count=1, datatype=<optimized out>, >>> >> op=0x14cd26cfbc20 <ompi_mpi_op_sum>, comm=0x3076fb0) at >>> pallreduce.c:113 >>> >> #4 0x000014cd271c9889 in VecNorm_MPI_Default (xin=<optimized out>, >>> >> type=<optimized out>, z=<optimized out>, VecNorm_SeqFn=<optimized >>> out>) >>> >> at >>> >> >>> >> >>> /jobfs/95504034.gadi-pbs/petsc/include/../src/vec/vec/impls/mpi/pvecimpl.h:168 >>> >> #5 VecNorm_MPI (xin=0x14ccee1ddb80, type=3924123648, z=0x22d) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/vec/vec/impls/mpi/pvec2.c:39 >>> >> #6 0x000014cd2718cddd in VecNorm (x=0x14ccee1ddb80, type=3924123648, >>> >> val=0x22d) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/vec/vec/interface/rvector.c:214 >>> >> #7 0x000014cd27f5a0b9 in KSPSolve_CG (ksp=0x14ccee1ddb80) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/ksp/impls/cg/cg.c:163 >>> >> etc. >>> >> >>> >> but with other cores still stuck at: >>> >> >>> >> #0 0x000015375cf41e8a in ucp_worker_progress () from >>> >> /apps/ucx/1.12.0/lib/libucp.so.0 >>> >> #1 0x000015377d4bd57b in opal_progress () at >>> >> >>> >> >>> /jobfs/35226956.gadi-pbs/0/openmpi/4.0.7/source/openmpi-4.0.7/opal/runtime/opal_progress.c:231 >>> >> #2 0x000015377d4c3ba5 in ompi_sync_wait_mt >>> >> (sync=sync@entry=0x7ffd6aedf6f0) at >>> >> >>> >> >>> /jobfs/35226956.gadi-pbs/0/openmpi/4.0.7/source/openmpi-4.0.7/opal/threads/wait_sync.c:85 >>> >> #3 0x000015378bf7cf38 in ompi_request_default_wait_any (count=8, >>> >> requests=0x8d465a0, index=0x7ffd6aedfa60, status=0x7ffd6aedfa10) at >>> >> >>> >> >>> /jobfs/35226956.gadi-pbs/0/openmpi/4.0.7/source/openmpi-4.0.7/ompi/request/req_wait.c:124 >>> >> #4 0x000015378bfc1b4b in PMPI_Waitany (count=8, requests=0x8d465a0, >>> >> indx=0x7ffd6aedfa60, status=<optimized out>) at pwaitany.c:86 >>> >> #5 0x000015378c88ef2c in MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ >>> >> (P=0x2cc7500, A=0x1, fill=2.1219957934356005e-314, C=0xc0fe132c) at >>> >> >>> /jobfs/95504034.gadi-pbs/petsc/src/mat/impls/aij/mpi/mpimatmatmult.c:1884 >>> >> #6 0x000015378c88dd4f in MatProductSymbolic_AtB_MPIAIJ_MPIAIJ >>> >> (C=0x2cc7500) at >>> >> >>> /jobfs/95504034.gadi-pbs/petsc/src/mat/impls/aij/mpi/mpimatmatmult.c:2071 >>> >> #7 0x000015378cc665b8 in MatProductSymbolic (mat=0x2cc7500) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/mat/interface/matproduct.c:795 >>> >> #8 0x000015378d294473 in PCGAMGSquareGraph_GAMG (a_pc=0x2cc7500, >>> >> Gmat1=0x1, Gmat2=0xc0fe132c) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/impls/gamg/gamg.c:489 >>> >> #9 0x000015378d27b83e in PCGAMGCoarsen_AGG (a_pc=0x2cc7500, >>> >> a_Gmat1=0x1, agg_lists=0xc0fe132c) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/impls/gamg/agg.c:969 >>> >> #10 0x000015378d294c73 in PCSetUp_GAMG (pc=0x2cc7500) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/impls/gamg/gamg.c:645 >>> >> #11 0x000015378d215721 in PCSetUp (pc=0x2cc7500) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/interface/precon.c:1069 >>> >> #12 0x000015378d216b82 in PCApply (pc=0x2cc7500, x=0x1, y=0xc0fe132c) >>> at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/interface/precon.c:484 >>> >> #13 0x000015378eb91b2f in __pyx_pw_8petsc4py_5PETSc_2PC_45apply >>> >> (__pyx_v_self=0x2cc7500, __pyx_args=0x1, __pyx_nargs=3237876524, >>> >> __pyx_kwds=0x1) at src/petsc4py/PETSc.c:259082 >>> >> #14 0x000015379e0a69f7 in method_vectorcall_FASTCALL_KEYWORDS >>> >> (func=0x15378f302890, args=0x83b3218, nargsf=<optimized out>, >>> >> kwnames=<optimized out>) at ../Objects/descrobject.c:405 >>> >> #15 0x000015379e11d435 in _PyObject_VectorcallTstate (kwnames=0x0, >>> >> nargsf=<optimized out>, args=0x83b3218, callable=0x15378f302890, >>> >> tstate=0x23e0020) at ../Include/cpython/abstract.h:114 >>> >> #16 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, >>> >> args=0x83b3218, callable=0x15378f302890) at >>> >> ../Include/cpython/abstract.h:123 >>> >> #17 call_function (kwnames=0x0, oparg=<optimized out>, >>> >> pp_stack=<synthetic pointer>, trace_info=0x7ffd6aee0390, >>> >> tstate=<optimized out>) at ../Python/ceval.c:5867 >>> >> #18 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized >>> out>, >>> >> throwflag=<optimized out>) at ../Python/ceval.c:4198 >>> >> #19 0x000015379e11b63b in _PyEval_EvalFrame (throwflag=0, f=0x83b3080, >>> >> tstate=0x23e0020) at ../Include/internal/pycore_ceval.h:46 >>> >> #20 _PyEval_Vector (tstate=<optimized out>, con=<optimized out>, >>> >> locals=<optimized out>, args=<optimized out>, argcount=4, >>> >> kwnames=<optimized out>) at ../Python/ceval.c:5065 >>> >> #21 0x000015378ee1e057 in __Pyx_PyObject_FastCallDict (func=<optimized >>> >> out>, args=0x1, _nargs=<optimized out>, kwargs=<optimized out>) at >>> >> src/petsc4py/PETSc.c:548022 >>> >> #22 __pyx_f_8petsc4py_5PETSc_PCApply_Python (__pyx_v_pc=0x2cc7500, >>> >> __pyx_v_x=0x1, __pyx_v_y=0xc0fe132c) at src/petsc4py/PETSc.c:31979 >>> >> #23 0x000015378d216cba in PCApply (pc=0x2cc7500, x=0x1, y=0xc0fe132c) >>> at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/pc/interface/precon.c:487 >>> >> #24 0x000015378d4d153c in KSP_PCApply (ksp=0x2cc7500, x=0x1, >>> >> y=0xc0fe132c) at >>> >> /jobfs/95504034.gadi-pbs/petsc/include/petsc/private/kspimpl.h:383 >>> >> #25 0x000015378d4d1097 in KSPSolve_CG (ksp=0x2cc7500) at >>> >> /jobfs/95504034.gadi-pbs/petsc/src/ksp/ksp/impls/cg/cg.c:162 >>> >> >>> >> Let me know if there is anything further we can try to debug this >>> issue >>> >> >>> >> Kind regards >>> >> Stephan Kramer >>> >> >>> >> >>> >> On 02/09/2023 01:58, Mark Adams wrote: >>> >>> Fantastic! >>> >>> >>> >>> I fixed a memory free problem. You should be OK now. >>> >>> I am pretty sure you are good but I would like to wait to get any >>> >> feedback >>> >>> from you. >>> >>> We should have a release at the end of the month and it would be >>> nice to >>> >>> get this into it. >>> >>> >>> >>> Thanks, >>> >>> Mark >>> >>> >>> >>> >>> >>> On Fri, Sep 1, 2023 at 7:07 AM Stephan Kramer < >>> s.kra...@imperial.ac.uk> >>> >>> wrote: >>> >>> >>> >>>> Hi Mark >>> >>>> >>> >>>> Sorry took a while to report back. We have tried your branch but >>> hit a >>> >>>> few issues, some of which we're not entirely sure are related. >>> >>>> >>> >>>> First switching off minimum degree ordering, and then switching to >>> the >>> >>>> old version of aggressive coarsening, as you suggested, got us back >>> to >>> >>>> the coarsening behaviour that we had previously, but then we also >>> >>>> observed an even further worsening of the iteration count: it had >>> >>>> previously gone up by 50% already (with the newer main petsc), but >>> now >>> >>>> was more than double "old" petsc. Took us a while to realize this >>> was >>> >>>> due to the default smoother changing from Cheby+SOR to Cheby+Jacobi. >>> >>>> Switching this also back to the old default we get back to very >>> similar >>> >>>> coarsening levels (see below for more details if it is of interest) >>> and >>> >>>> iteration counts. >>> >>>> >>> >>>> So that's all very good news. However, we were also starting seeing >>> >>>> memory errors (double free or corruption) when we switched off the >>> >>>> minimum degree ordering. Because this was at an earlier version of >>> your >>> >>>> branch we then rebuild, hoping this was just an earlier bug that had >>> >>>> been fixed, but then we were having MPI-lockup issues. We have now >>> >>>> figured out the MPI issues are completely unrelated - some >>> combination >>> >>>> with a newer mpi build and firedrake on our cluster which also occur >>> >>>> using main branches of everything. So switching back to an older MPI >>> >>>> build we are hoping to now test your most recent version of >>> >>>> adams/gamg-add-old-coarsening with these options and see whether the >>> >>>> memory errors are still there. Will let you know >>> >>>> >>> >>>> Best wishes >>> >>>> Stephan Kramer >>> >>>> >>> >>>> Coarsening details with various options for Level 6 of the test >>> case: >>> >>>> >>> >>>> In our original setup (using "old" petsc), we had: >>> >>>> >>> >>>> rows=516, cols=516, bs=6 >>> >>>> rows=12660, cols=12660, bs=6 >>> >>>> rows=346974, cols=346974, bs=6 >>> >>>> rows=19169670, cols=19169670, bs=3 >>> >>>> >>> >>>> Then with the newer main petsc we had >>> >>>> >>> >>>> rows=666, cols=666, bs=6 >>> >>>> rows=7740, cols=7740, bs=6 >>> >>>> rows=34902, cols=34902, bs=6 >>> >>>> rows=736578, cols=736578, bs=6 >>> >>>> rows=19169670, cols=19169670, bs=3 >>> >>>> >>> >>>> Then on your branch with minimum_degree_ordering False: >>> >>>> >>> >>>> rows=504, cols=504, bs=6 >>> >>>> rows=2274, cols=2274, bs=6 >>> >>>> rows=11010, cols=11010, bs=6 >>> >>>> rows=35790, cols=35790, bs=6 >>> >>>> rows=430686, cols=430686, bs=6 >>> >>>> rows=19169670, cols=19169670, bs=3 >>> >>>> >>> >>>> And with minimum_degree_ordering False and >>> use_aggressive_square_graph >>> >>>> True: >>> >>>> >>> >>>> rows=498, cols=498, bs=6 >>> >>>> rows=12672, cols=12672, bs=6 >>> >>>> rows=346974, cols=346974, bs=6 >>> >>>> rows=19169670, cols=19169670, bs=3 >>> >>>> >>> >>>> So that is indeed pretty much back to what it was before >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> On 31/08/2023 23:40, Mark Adams wrote: >>> >>>>> Hi Stephan, >>> >>>>> >>> >>>>> This branch is settling down. adams/gamg-add-old-coarsening >>> >>>>> < >>> >> >>> https://gitlab.com/petsc/petsc/-/commits/adams/gamg-add-old-coarsening> >>> >>>>> I made the old, not minimum degree, ordering the default but kept >>> the >>> >> new >>> >>>>> "aggressive" coarsening as the default, so I am hoping that just >>> adding >>> >>>>> "-pc_gamg_use_aggressive_square_graph true" to your regression >>> tests >>> >> will >>> >>>>> get you back to where you were before. >>> >>>>> Fingers crossed ... let me know if you have any success or not. >>> >>>>> >>> >>>>> Thanks, >>> >>>>> Mark >>> >>>>> >>> >>>>> >>> >>>>> On Tue, Aug 15, 2023 at 1:45 PM Mark Adams <mfad...@lbl.gov> >>> wrote: >>> >>>>> >>> >>>>>> Hi Stephan, >>> >>>>>> >>> >>>>>> I have a branch that you can try: adams/gamg-add-old-coarsening >>> >>>>>> < >>> >> >>> https://gitlab.com/petsc/petsc/-/commits/adams/gamg-add-old-coarsening >>> >>>>>> Things to test: >>> >>>>>> * First, verify that nothing unintended changed by reproducing >>> your >>> >> bad >>> >>>>>> results with this branch (the defaults are the same) >>> >>>>>> * Try not using the minimum degree ordering that I suggested >>> >>>>>> with: -pc_gamg_use_minimum_degree_ordering false >>> >>>>>> -- I am eager to see if that is the main problem. >>> >>>>>> * Go back to what I think is the old method: >>> >>>>>> -pc_gamg_use_minimum_degree_ordering >>> >>>>>> false -pc_gamg_use_aggressive_square_graph true >>> >>>>>> >>> >>>>>> When we get back to where you were, I would like to try to get >>> modern >>> >>>>>> stuff working. >>> >>>>>> I did add a -pc_gamg_aggressive_mis_k <2> >>> >>>>>> You could to another step of MIS coarsening with >>> >>>> -pc_gamg_aggressive_mis_k >>> >>>>>> 3 >>> >>>>>> >>> >>>>>> Anyway, lots to look at but, alas, AMG does have a lot of >>> parameters. >>> >>>>>> >>> >>>>>> Thanks, >>> >>>>>> Mark >>> >>>>>> >>> >>>>>> On Mon, Aug 14, 2023 at 4:26 PM Mark Adams <mfad...@lbl.gov> >>> wrote: >>> >>>>>> >>> >>>>>>> On Mon, Aug 14, 2023 at 11:03 AM Stephan Kramer < >>> >>>> s.kra...@imperial.ac.uk> >>> >>>>>>> wrote: >>> >>>>>>> >>> >>>>>>>> Many thanks for looking into this, Mark >>> >>>>>>>>> My 3D tests were not that different and I see you lowered the >>> >>>>>>>> threshold. >>> >>>>>>>>> Note, you can set the threshold to zero, but your test is >>> running >>> >> so >>> >>>>>>>> much >>> >>>>>>>>> differently than mine there is something else going on. >>> >>>>>>>>> Note, the new, bad, coarsening rate of 30:1 is what we tend to >>> >> shoot >>> >>>>>>>> for >>> >>>>>>>>> in 3D. >>> >>>>>>>>> >>> >>>>>>>>> So it is not clear what the problem is. Some questions: >>> >>>>>>>>> >>> >>>>>>>>> * do you have a picture of this mesh to show me? >>> >>>>>>>> It's just a standard hexahedral cubed sphere mesh with the >>> >> refinement >>> >>>>>>>> level giving the number of times each of the six sides have been >>> >>>>>>>> subdivided: so Level_5 mean 2^5 x 2^5 squares which is extruded >>> to >>> >> 16 >>> >>>>>>>> layers. So the total number of elements at Level_5 is 6 x 32 x >>> 32 x >>> >>>> 16 = >>> >>>>>>>> 98304 hexes. And everything doubles in all 3 dimensions (so >>> 2^3) >>> >>>> going >>> >>>>>>>> to the next Level >>> >>>>>>>> >>> >>>>>>> I see, and I assume these are pretty stretched elements. >>> >>>>>>> >>> >>>>>>> >>> >>>>>>>>> * what do you mean by Q1-Q2 elements? >>> >>>>>>>> Q2-Q1, basically Taylor hood on hexes, so (tri)quadratic for >>> >> velocity >>> >>>>>>>> and (tri)linear for pressure >>> >>>>>>>> >>> >>>>>>>> I guess you could argue we could/should just do good old >>> geometric >>> >>>>>>>> multigrid instead. More generally we do use this solver >>> >> configuration >>> >>>> a >>> >>>>>>>> lot for tetrahedral Taylor Hood (P2-P1) in particular also for >>> our >>> >>>>>>>> adaptive mesh runs - would it be worth to see if we have the >>> same >>> >>>>>>>> performance issues with tetrahedral P2-P1? >>> >>>>>>>> >>> >>>>>>> No, you have a clear reproducer, if not minimal. >>> >>>>>>> The first coarsening is very different. >>> >>>>>>> >>> >>>>>>> I am working on this and I see that I added a heuristic for thin >>> >> bodies >>> >>>>>>> where you order the vertices in greedy algorithms with minimum >>> degree >>> >>>> first. >>> >>>>>>> This will tend to pick corners first, edges then faces, etc. >>> >>>>>>> That may be the problem. I would like to understand it better >>> (see >>> >>>> below). >>> >>>>>>> >>> >>>>>>>>> It would be nice to see if the new and old codes are similar >>> >> without >>> >>>>>>>>> aggressive coarsening. >>> >>>>>>>>> This was the intended change of the major change in this time >>> frame >>> >>>> as >>> >>>>>>>> you >>> >>>>>>>>> noticed. >>> >>>>>>>>> If these jobs are easy to run, could you check that the old >>> and new >>> >>>>>>>>> versions are similar with "-pc_gamg_square_graph 0 ", ( and >>> you >>> >>>> only >>> >>>>>>>> need >>> >>>>>>>>> one time step). >>> >>>>>>>>> All you need to do is check that the first coarse grid has >>> about >>> >> the >>> >>>>>>>> same >>> >>>>>>>>> number of equations (large). >>> >>>>>>>> Unfortunately we're seeing some memory errors when we use this >>> >> option, >>> >>>>>>>> and I'm not entirely clear whether we're just running out of >>> memory >>> >>>> and >>> >>>>>>>> need to put it on a special queue. >>> >>>>>>>> >>> >>>>>>>> The run with square_graph 0 using new PETSc managed to get >>> through >>> >> one >>> >>>>>>>> solve at level 5, and is giving the following mg levels: >>> >>>>>>>> >>> >>>>>>>> rows=174, cols=174, bs=6 >>> >>>>>>>> total: nonzeros=30276, allocated nonzeros=30276 >>> >>>>>>>> -- >>> >>>>>>>> rows=2106, cols=2106, bs=6 >>> >>>>>>>> total: nonzeros=4238532, allocated >>> nonzeros=4238532 >>> >>>>>>>> -- >>> >>>>>>>> rows=21828, cols=21828, bs=6 >>> >>>>>>>> total: nonzeros=62588232, allocated >>> nonzeros=62588232 >>> >>>>>>>> -- >>> >>>>>>>> rows=589824, cols=589824, bs=6 >>> >>>>>>>> total: nonzeros=1082528928, allocated >>> >> nonzeros=1082528928 >>> >>>>>>>> -- >>> >>>>>>>> rows=2433222, cols=2433222, bs=3 >>> >>>>>>>> total: nonzeros=456526098, allocated >>> nonzeros=456526098 >>> >>>>>>>> >>> >>>>>>>> comparing with square_graph 100 with new PETSc >>> >>>>>>>> >>> >>>>>>>> rows=96, cols=96, bs=6 >>> >>>>>>>> total: nonzeros=9216, allocated nonzeros=9216 >>> >>>>>>>> -- >>> >>>>>>>> rows=1440, cols=1440, bs=6 >>> >>>>>>>> total: nonzeros=647856, allocated nonzeros=647856 >>> >>>>>>>> -- >>> >>>>>>>> rows=97242, cols=97242, bs=6 >>> >>>>>>>> total: nonzeros=65656836, allocated >>> nonzeros=65656836 >>> >>>>>>>> -- >>> >>>>>>>> rows=2433222, cols=2433222, bs=3 >>> >>>>>>>> total: nonzeros=456526098, allocated >>> nonzeros=456526098 >>> >>>>>>>> >>> >>>>>>>> and old PETSc with square_graph 100 >>> >>>>>>>> >>> >>>>>>>> rows=90, cols=90, bs=6 >>> >>>>>>>> total: nonzeros=8100, allocated nonzeros=8100 >>> >>>>>>>> -- >>> >>>>>>>> rows=1872, cols=1872, bs=6 >>> >>>>>>>> total: nonzeros=1234080, allocated >>> nonzeros=1234080 >>> >>>>>>>> -- >>> >>>>>>>> rows=47652, cols=47652, bs=6 >>> >>>>>>>> total: nonzeros=23343264, allocated >>> nonzeros=23343264 >>> >>>>>>>> -- >>> >>>>>>>> rows=2433222, cols=2433222, bs=3 >>> >>>>>>>> total: nonzeros=456526098, allocated >>> nonzeros=456526098 >>> >>>>>>>> -- >>> >>>>>>>> >>> >>>>>>>> Unfortunately old PETSc with square_graph 0 did not complete a >>> >> single >>> >>>>>>>> solve before giving the memory error >>> >>>>>>>> >>> >>>>>>> OK, thanks for trying. >>> >>>>>>> >>> >>>>>>> I am working on this and I will give you a branch to test, but >>> if you >>> >>>> can >>> >>>>>>> rebuild PETSc here is a quick test that might fix your problem. >>> >>>>>>> In src/ksp/pc/impls/gamg/agg.c you will see: >>> >>>>>>> >>> >>>>>>> PetscCall(PetscSortIntWithArray(nloc, degree, permute)); >>> >>>>>>> >>> >>>>>>> If you can comment this out in the new code and compare with the >>> old, >>> >>>>>>> that might fix the problem. >>> >>>>>>> >>> >>>>>>> Thanks, >>> >>>>>>> Mark >>> >>>>>>> >>> >>>>>>> >>> >>>>>>>>> BTW, I am starting to think I should add the old method back >>> as an >>> >>>>>>>> option. >>> >>>>>>>>> I did not think this change would cause large differences. >>> >>>>>>>> Yes, I think that would be much appreciated. Let us know if we >>> can >>> >> do >>> >>>>>>>> any testing >>> >>>>>>>> >>> >>>>>>>> Best wishes >>> >>>>>>>> Stephan >>> >>>>>>>> >>> >>>>>>>> >>> >>>>>>>>> Thanks, >>> >>>>>>>>> Mark >>> >>>>>>>>> >>> >>>>>>>>> >>> >>>>>>>>> >>> >>>>>>>>> >>> >>>>>>>>>> Note that we are providing the rigid body near nullspace, >>> >>>>>>>>>> hence the bs=3 to bs=6. >>> >>>>>>>>>> We have tried different values for the gamg_threshold but it >>> >> doesn't >>> >>>>>>>>>> really seem to significantly alter the coarsening amount in >>> that >>> >>>> first >>> >>>>>>>>>> step. >>> >>>>>>>>>> >>> >>>>>>>>>> Do you have any suggestions for further things we should >>> try/look >>> >>>> at? >>> >>>>>>>>>> Any feedback would be much appreciated >>> >>>>>>>>>> >>> >>>>>>>>>> Best wishes >>> >>>>>>>>>> Stephan Kramer >>> >>>>>>>>>> >>> >>>>>>>>>> Full logs including log_view timings available from >>> >>>>>>>>>> https://github.com/stephankramer/petsc-scaling/ >>> >>>>>>>>>> >>> >>>>>>>>>> In particular: >>> >>>>>>>>>> >>> >>>>>>>>>> >>> >>>>>>>>>> >>> >> >>> https://github.com/stephankramer/petsc-scaling/blob/main/before/Level_5/output_2.dat >>> >> >>> https://github.com/stephankramer/petsc-scaling/blob/main/after/Level_5/output_2.dat >>> >> >>> https://github.com/stephankramer/petsc-scaling/blob/main/before/Level_6/output_2.dat >>> >> >>> https://github.com/stephankramer/petsc-scaling/blob/main/after/Level_6/output_2.dat >>> >> >>> https://github.com/stephankramer/petsc-scaling/blob/main/before/Level_7/output_2.dat >>> >> >>> https://github.com/stephankramer/petsc-scaling/blob/main/after/Level_7/output_2.dat >>> >> >>> >>>