[petsc-dev] [petsc-users] performance regression with GAMG
Mark Adams
mfadams at lbl.gov
Thu Oct 5 20:48:08 CDT 2023
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.
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 <mfadams at 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.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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