[petsc-users] Bad memory scaling with PETSc 3.10
Dave May
dave.mayhem23 at gmail.com
Tue Mar 26 05:10:09 CDT 2019
On Tue, 26 Mar 2019 at 09:52, Myriam Peyrounette via petsc-users <
petsc-users at mcs.anl.gov> wrote:
> How can I be sure they are indeed used? Can I print this information in
> some log file?
>
Yes. Re-run the job with the command line option
-options_left true
This will report all options parsed, and importantly, will also indicate if
any options were unused.
Thanks
Dave
Thanks in advance
>
> Myriam
>
> Le 03/25/19 à 18:24, Matthew Knepley a écrit :
>
> 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/%7Eknepley/>
>
>
> --
> Myriam Peyrounette
> CNRS/IDRIS - HLST
> --
>
>
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