[petsc-users] Convergence of AMG

[Karin&NiKo]

Ok, sorry for my misunderstanding  and thank you for the clarification.

Le mar. 30 oct. 2018 13:55, Mark Adams <mfadams at lbl.gov> a écrit :

> Nicolas,
>
> Smoothed aggregation is fine with shells. see the original SA paper (
> https://link.springer.com/article/10.1007/BF02238511).
>
> The rotational modes, which are the non-trivial modes that must be
> supplied, are used in the interpolation.
>
> Mark
>
> On Tue, Oct 30, 2018 at 5:22 AM Karin&NiKo <niko.karin at gmail.com> wrote:
>
>> Manav,
>>
>> How are interpolated the rotational degrees of freedom? AFAIK, when using
>> smoothed aggregation, the interpolation process tries to mimic linear
>> interpolation, which can be OK for the displacement DOF but is not for the
>> rotational DOF using some plate and shell formulations.
>> This can explain poor convergence of a multilevel approach, which needs
>> to restrict and extrapolate the unknowns. In order to check this
>> hypothesis, you can try a test case with zero rotations.
>>
>> Nicolas
>>
>> Le lun. 29 oct. 2018 à 22:13, Mark Adams via petsc-users <
>> petsc-users at mcs.anl.gov> a écrit :
>>
>>> * the two level results tell us that MG is not doing well on the coarse
>>> grids. So the coarse grids are the problem.
>>>
>>> * Do not worry about timing now. Get the math correct. The two level
>>> solve is not meant to be a solution just a diagnostic so don't try to
>>> optimize it by squaring the graph. Use -pc_gamg_square_graph 0.
>>>
>>> * It looks like you don't need 4 smoothing steps but lets keep it and we
>>> can dial it back later.
>>>
>>> * This table is interesting. First, you had about 12 iterations earlier
>>> and I think your rtol was tighter than the default (so the iteration could
>>> should go down not up). Do you know what change here?
>>>
>>> Note, even though -mg_levels_ksp_max_it is not in the ksp_view it does
>>> work. It is syntactic sugar to just add it to all levels like you did
>>> manually.
>>>
>>> Anyway, these number look reasonable. It is interesting that 3 levels
>>> ran well but the 4th level ran poorly. This implies we want to slow down
>>> coarsening on these levels, but ...
>>>
>>> First can you please rerun this experiment with -pc_gamg_square_graph 0.
>>>
>>> Also, please run with -info. This is very noisy but you can grep on
>>> "GAMG" and send that output to us (about 15 lines).
>>>
>>> Thanks,
>>> Mark
>>>
>>>
>>>
>>> On Mon, Oct 29, 2018 at 3:34 PM Manav Bhatia <bhatiamanav at gmail.com>
>>> wrote:
>>>
>>>> Barry,
>>>>
>>>>    Here are some quick numbers with the following options on 4 CPUs and
>>>> 543,606 dofs:
>>>>
>>>> -mg_levels_ksp_max_it 4 -pc_gamg_square_graph 1 -pc_gamg_threshold 0.
>>>>
>>>>  #levels   |    #KSP Iters
>>>> ———————————
>>>>      2        |       18
>>>>      3        |       18
>>>>      4        |       40
>>>>      5        |       59
>>>>
>>>> -Manav
>>>>
>>>>
>>>> On Oct 29, 2018, at 2:06 PM, Smith, Barry F. <bsmith at mcs.anl.gov>
>>>> wrote:
>>>>
>>>>
>>>>  Exactly how much does it increase with number of levels? Send a chart
>>>> number of levels and number of iterations. With say -mg_levels_ksp_maxit 4
>>>>
>>>>   Thanks
>>>>
>>>>   Barry
>>>>
>>>>
>>>>
>>>>
>>>> On Oct 29, 2018, at 12:59 PM, Manav Bhatia <bhatiamanav at gmail.com>
>>>> wrote:
>>>>
>>>> Thanks for the clarification.
>>>>
>>>> I also observed that the number of KSP iterations increases with an
>>>> increase in the levels of AMG. Is this true, in general, for all/most
>>>> applications?
>>>>
>>>> -Manav
>>>>
>>>> On Oct 29, 2018, at 12:53 PM, Jed Brown <jed at jedbrown.org> wrote:
>>>>
>>>> Manav Bhatia <bhatiamanav at gmail.com> writes:
>>>>
>>>> Thanks, Jed.
>>>>
>>>> The description says: “ Square the graph, ie. compute A'*A before
>>>> aggregating it"
>>>>
>>>> What is A here?
>>>>
>>>>
>>>> The original matrix, or its "graph" (your 6x6 blocks condensed to
>>>> scalars).
>>>>
>>>> What is the impact of setting this to 0, which led to a very
>>>> significant increase in the CPU time in my case?
>>>>
>>>>
>>>> The aggregates are formed on the connectivity of your original matrix,
>>>> so root nodes are aggregated only with their first neighbors, resulting
>>>> in slower coarsening.
>>>>
>>>>
>>>>
>>>>
>>>>
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