[petsc-users] performance regression with GAMG
Stephan Kramer
s.kramer at imperial.ac.uk
Tue Oct 3 23:30:38 CDT 2023
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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