[petsc-users] performance regression with GAMG
Mark Adams
mfadams at lbl.gov
Thu Oct 5 19:51:38 CDT 2023
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.kramer at 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.kramer at 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 at 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.kramer at 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 <mfadams at 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 <mfadams at lbl.gov> wrote:
> >>>>>>
> >>>>>>> On Mon, Aug 14, 2023 at 11:03 AM Stephan Kramer <
> >>>> s.kramer at 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
> >>
>
>
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