[petsc-users] performance regression with GAMG
Stephan Kramer
s.kramer at imperial.ac.uk
Fri Sep 1 06:04:58 CDT 2023
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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