[petsc-users] Bad memory scaling with PETSc 3.10
Matthew Knepley
knepley at gmail.com
Mon Mar 25 12:24:41 CDT 2019
On Mon, Mar 25, 2019 at 10:54 AM Myriam Peyrounette via petsc-users <
petsc-users at mcs.anl.gov> wrote:
> Hi,
>
> thanks for the explanations. I tried the last PETSc version (commit
> fbc5705bc518d02a4999f188aad4ccff5f754cbf), which includes the patch you
> talked about. But the memory scaling shows no improvement (see scaling
> attached), even when using the "scalable" options :(
>
> I had a look at the PETSc functions MatPtAPNumeric_MPIAIJ_MPIAIJ and
> MatPtAPSymbolic_MPIAIJ_MPIAIJ (especially at the differences before and
> after the first "bad" commit), but I can't find what induced this memory
> issue.
>
> Are you sure that the option was used? It just looks suspicious to me that
they use exactly the same amount of memory. It should be different, even if
it does not solve the problem.
Thanks,
Matt
> Myriam
>
>
>
>
> Le 03/20/19 à 17:38, Fande Kong a écrit :
>
> Hi Myriam,
>
> There are three algorithms in PETSc to do PtAP ( const char
> *algTypes[3] = {"scalable","nonscalable","hypre"};), and can be specified
> using the petsc options: -matptap_via xxxx.
>
> (1) -matptap_via hypre: This call the hypre package to do the PtAP trough
> an all-at-once triple product. In our experiences, it is the most memory
> efficient, but could be slow.
>
> (2) -matptap_via scalable: This involves a row-wise algorithm plus an
> outer product. This will use more memory than hypre, but way faster. This
> used to have a bug that could take all your memory, and I have a fix at
> https://bitbucket.org/petsc/petsc/pull-requests/1452/mpiptap-enable-large-scale-simulations/diff.
> When using this option, we may want to have extra options such as
> -inner_offdiag_matmatmult_via scalable -inner_diag_matmatmult_via
> scalable to select inner scalable algorithms.
>
> (3) -matptap_via nonscalable: Suppose to be even faster, but use more
> memory. It does dense matrix operations.
>
>
> Thanks,
>
> Fande Kong
>
>
>
>
> On Wed, Mar 20, 2019 at 10:06 AM Myriam Peyrounette via petsc-users <
> petsc-users at mcs.anl.gov> wrote:
>
>> More precisely: something happens when upgrading the functions
>> MatPtAPNumeric_MPIAIJ_MPIAIJ and/or MatPtAPSymbolic_MPIAIJ_MPIAIJ.
>>
>> Unfortunately, there are a lot of differences between the old and new
>> versions of these functions. I keep investigating but if you have any idea,
>> please let me know.
>>
>> Best,
>>
>> Myriam
>>
>> Le 03/20/19 à 13:48, Myriam Peyrounette a écrit :
>>
>> Hi all,
>>
>> I used git bisect to determine when the memory need increased. I found
>> that the first "bad" commit is aa690a28a7284adb519c28cb44eae20a2c131c85.
>>
>> Barry was right, this commit seems to be about an evolution of MatPtAPSymbolic_MPIAIJ_MPIAIJ.
>> You mentioned the option "-matptap_via scalable" but I can't find any
>> information about it. Can you tell me more?
>>
>> Thanks
>>
>> Myriam
>>
>>
>> Le 03/11/19 à 14:40, Mark Adams a écrit :
>>
>> Is there a difference in memory usage on your tiny problem? I assume no.
>>
>> I don't see anything that could come from GAMG other than the RAP stuff
>> that you have discussed already.
>>
>> On Mon, Mar 11, 2019 at 9:32 AM Myriam Peyrounette <
>> myriam.peyrounette at idris.fr> wrote:
>>
>>> The code I am using here is the example 42 of PETSc (
>>> https://www.mcs.anl.gov/petsc/petsc-3.9/src/ksp/ksp/examples/tutorials/ex42.c.html).
>>> Indeed it solves the Stokes equation. I thought it was a good idea to use
>>> an example you might know (and didn't find any that uses GAMG functions). I
>>> just changed the PCMG setup so that the memory problem appears. And it
>>> appears when adding PCGAMG.
>>>
>>> I don't care about the performance or even the result rightness here,
>>> but only about the difference in memory use between 3.6 and 3.10. Do you
>>> think finding a more adapted script would help?
>>>
>>> I used the threshold of 0.1 only once, at the beginning, to test its
>>> influence. I used the default threshold (of 0, I guess) for all the other
>>> runs.
>>>
>>> Myriam
>>>
>>> Le 03/11/19 à 13:52, Mark Adams a écrit :
>>>
>>> In looking at this larger scale run ...
>>>
>>> * Your eigen estimates are much lower than your tiny test problem. But
>>> this is Stokes apparently and it should not work anyway. Maybe you have a
>>> small time step that adds a lot of mass that brings the eigen estimates
>>> down. And your min eigenvalue (not used) is positive. I would expect
>>> negative for Stokes ...
>>>
>>> * You seem to be setting a threshold value of 0.1 -- that is very high
>>>
>>> * v3.6 says "using nonzero initial guess" but this is not in v3.10.
>>> Maybe we just stopped printing that.
>>>
>>> * There were some changes to coasening parameters in going from v3.6 but
>>> it does not look like your problem was effected. (The coarsening algo is
>>> non-deterministic by default and you can see small difference on different
>>> runs)
>>>
>>> * We may have also added a "noisy" RHS for eigen estimates by default
>>> from v3.6.
>>>
>>> * And for non-symetric problems you can try -pc_gamg_agg_nsmooths 0, but
>>> again GAMG is not built for Stokes anyway.
>>>
>>>
>>> On Tue, Mar 5, 2019 at 11:53 AM Myriam Peyrounette <
>>> myriam.peyrounette at idris.fr> wrote:
>>>
>>>> I used PCView to display the size of the linear system in each level of
>>>> the MG. You'll find the outputs attached to this mail (zip file) for both
>>>> the default threshold value and a value of 0.1, and for both 3.6 and 3.10
>>>> PETSc versions.
>>>>
>>>> For convenience, I summarized the information in a graph, also attached
>>>> (png file).
>>>>
>>>> As you can see, there are slight differences between the two versions
>>>> but none is critical, in my opinion. Do you see anything suspicious in the
>>>> outputs?
>>>>
>>>> + I can't find the default threshold value. Do you know where I can
>>>> find it?
>>>>
>>>> Thanks for the follow-up
>>>>
>>>> Myriam
>>>>
>>>> Le 03/05/19 à 14:06, Matthew Knepley a écrit :
>>>>
>>>> On Tue, Mar 5, 2019 at 7:14 AM Myriam Peyrounette <
>>>> myriam.peyrounette at idris.fr> wrote:
>>>>
>>>>> Hi Matt,
>>>>>
>>>>> I plotted the memory scalings using different threshold values. The
>>>>> two scalings are slightly translated (from -22 to -88 mB) but this gain is
>>>>> neglectable. The 3.6-scaling keeps being robust while the 3.10-scaling
>>>>> deteriorates.
>>>>>
>>>>> Do you have any other suggestion?
>>>>>
>>>> Mark, what is the option she can give to output all the GAMG data?
>>>>
>>>> Also, run using -ksp_view. GAMG will report all the sizes of its grids,
>>>> so it should be easy to see
>>>> if the coarse grid sizes are increasing, and also what the effect of
>>>> the threshold value is.
>>>>
>>>> Thanks,
>>>>
>>>> Matt
>>>>
>>>>> Thanks
>>>>> Myriam
>>>>>
>>>>> Le 03/02/19 à 02:27, Matthew Knepley a écrit :
>>>>>
>>>>> On Fri, Mar 1, 2019 at 10:53 AM Myriam Peyrounette via petsc-users <
>>>>> petsc-users at mcs.anl.gov> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> I used to run my code with PETSc 3.6. Since I upgraded the PETSc
>>>>>> version
>>>>>> to 3.10, this code has a bad memory scaling.
>>>>>>
>>>>>> To report this issue, I took the PETSc script ex42.c and slightly
>>>>>> modified it so that the KSP and PC configurations are the same as in
>>>>>> my
>>>>>> code. In particular, I use a "personnalised" multi-grid method. The
>>>>>> modifications are indicated by the keyword "TopBridge" in the attached
>>>>>> scripts.
>>>>>>
>>>>>> To plot the memory (weak) scaling, I ran four calculations for each
>>>>>> script with increasing problem sizes and computations cores:
>>>>>>
>>>>>> 1. 100,000 elts on 4 cores
>>>>>> 2. 1 million elts on 40 cores
>>>>>> 3. 10 millions elts on 400 cores
>>>>>> 4. 100 millions elts on 4,000 cores
>>>>>>
>>>>>> The resulting graph is also attached. The scaling using PETSc 3.10
>>>>>> clearly deteriorates for large cases, while the one using PETSc 3.6 is
>>>>>> robust.
>>>>>>
>>>>>> After a few tests, I found that the scaling is mostly sensitive to the
>>>>>> use of the AMG method for the coarse grid (line 1780 in
>>>>>> main_ex42_petsc36.cc). In particular, the performance strongly
>>>>>> deteriorates when commenting lines 1777 to 1790 (in
>>>>>> main_ex42_petsc36.cc).
>>>>>>
>>>>>> Do you have any idea of what changed between version 3.6 and version
>>>>>> 3.10 that may imply such degradation?
>>>>>>
>>>>>
>>>>> I believe the default values for PCGAMG changed between versions. It
>>>>> sounds like the coarsening rate
>>>>> is not great enough, so that these grids are too large. This can be
>>>>> set using:
>>>>>
>>>>>
>>>>> https://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/PC/PCGAMGSetThreshold.html
>>>>>
>>>>> There is some explanation of this effect on that page. Let us know if
>>>>> setting this does not correct the situation.
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Matt
>>>>>
>>>>>
>>>>>> Let me know if you need further information.
>>>>>>
>>>>>> Best,
>>>>>>
>>>>>> Myriam Peyrounette
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Myriam Peyrounette
>>>>>> CNRS/IDRIS - HLST
>>>>>> --
>>>>>>
>>>>>>
>>>>>
>>>>> --
>>>>> What most experimenters take for granted before they begin their
>>>>> experiments is infinitely more interesting than any results to which their
>>>>> experiments lead.
>>>>> -- Norbert Wiener
>>>>>
>>>>> https://www.cse.buffalo.edu/~knepley/
>>>>> <http://www.cse.buffalo.edu/%7Eknepley/>
>>>>>
>>>>>
>>>>> --
>>>>> Myriam Peyrounette
>>>>> CNRS/IDRIS - HLST
>>>>> --
>>>>>
>>>>>
>>>>
>>>> --
>>>> What most experimenters take for granted before they begin their
>>>> experiments is infinitely more interesting than any results to which their
>>>> experiments lead.
>>>> -- Norbert Wiener
>>>>
>>>> https://www.cse.buffalo.edu/~knepley/
>>>> <http://www.cse.buffalo.edu/%7Eknepley/>
>>>>
>>>>
>>>> --
>>>> Myriam Peyrounette
>>>> CNRS/IDRIS - HLST
>>>> --
>>>>
>>>>
>>> --
>>> Myriam Peyrounette
>>> CNRS/IDRIS - HLST
>>> --
>>>
>>>
>> --
>> Myriam Peyrounette
>> CNRS/IDRIS - HLST
>> --
>>
>>
>> --
>> Myriam Peyrounette
>> CNRS/IDRIS - HLST
>> --
>>
>>
> --
> Myriam Peyrounette
> CNRS/IDRIS - HLST
> --
>
>
--
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which their
experiments lead.
-- Norbert Wiener
https://www.cse.buffalo.edu/~knepley/ <http://www.cse.buffalo.edu/~knepley/>
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