[petsc-users] DMPlex memory problem in scaling test

Matthew Knepley knepley at gmail.com
Thu Oct 10 08:04:33 CDT 2019 

On Thu, Oct 10, 2019 at 8:41 AM Dave May <dave.mayhem23 at gmail.com> wrote:

> On Thu 10. Oct 2019 at 14:34, Matthew Knepley <knepley at gmail.com> wrote:
>
>> On Thu, Oct 10, 2019 at 8:31 AM Dave May <dave.mayhem23 at gmail.com> wrote:
>>
>>> On Thu, 10 Oct 2019 at 13:21, Matthew Knepley via petsc-users <
>>> petsc-users at mcs.anl.gov> wrote:
>>>
>>>> On Wed, Oct 9, 2019 at 5:10 PM Danyang Su via petsc-users <
>>>> petsc-users at mcs.anl.gov> wrote:
>>>>
>>>>> Dear All,
>>>>>
>>>>> I have a question regarding the maximum memory usage for the scaling
>>>>> test. My code is written in Fortran with support for both structured grid
>>>>> (DM) and unstructured grid (DMPlex). It looks like memory consumption is
>>>>> much larger when DMPlex is used and finally causew out_of_memory problem.
>>>>>
>>>>> Below are some test using both structured grid and unstructured grid.
>>>>> The memory consumption by the code is estimated based on all allocated
>>>>> arrays and PETSc memory consumption is estimated based on
>>>>> PetscMemoryGetMaximumUsage.
>>>>>
>>>>> I just wonder why the PETSc memory consumption does not decrease when
>>>>> number of processors increases. For structured grid (scenario 7-9), the
>>>>> memory consumption decreases as number of processors increases. However,
>>>>> for unstructured grid case (scenario 14-16), the memory for PETSc part
>>>>> remains unchanged. When I run a larger case, the code crashes because
>>>>> memory is ran out. The same case works on another cluster with 480GB memory
>>>>> per node. Does this make sense?
>>>>>
>>>> We would need a finer breakdown of where memory is being used. I did
>>>> this for a paper:
>>>>
>>>>   https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/jgrb.50217
>>>>
>>>> If the subdomains, the halo sizes can overwhelm the basic storage. It
>>>> looks like the subdomains are big here,
>>>> but things are not totally clear to me. It would be helpful to send the
>>>> output of -log_view for each case since
>>>> PETSc tries to keep track of allocated memory.
>>>>
>>>
>>> Matt - I'd guess that there is a sequential (non-partitioned) mesh
>>> hanging around in memory.
>>> Is it possible that he's created the PLEX object which is loaded
>>> sequentially (stored and retained in memory and never released), and then
>>> afterwards distributed?
>>> This can never happen with the DMDA and the table verifies this.
>>> If his code using the DMDA and DMPLEX are as identical as possible
>>> (albeit the DM used), then a sequential mesh held in memory seems the
>>> likely cause.
>>>
>>
>> Dang it, Dave is always right.
>>
>> How to prevent this?
>>
>
> I thought you/Lawrence/Vaclav/others... had developed and provided support
>  for a parallel DMPLEX load via a suitably defined plex specific H5 mesh
> file.
>

We have, but these tests looked like generated meshes.

  Thanks,

    Matt


> Since it looks like you are okay with fairly regular meshes, I would
>> construct the
>> coarsest mesh you can, and then use
>>
>>   -dm_refine <k>
>>
>> which is activated by DMSetFromOptions(). Make sure to call it after
>> DMPlexDistribute(). It will regularly
>> refine in parallel and should show good memory scaling as Dave says.
>>
>>   Thanks,
>>
>>      Matt
>>
>>
>>>
>>>>   Thanks,
>>>>
>>>>      Matt
>>>>
>>>>> scenario no. points cell type DMPLex nprocs no. nodes mem per node GB
>>>>> solver Rank 0 memory MB Rank 0 petsc memory MB Runtime (sec)
>>>>> 1 2121 rectangle no 40 1 200 GMRES,Hypre preconditioner 0.21 41.6
>>>>> 2 8241 rectangle no 40 1 200 GMRES,Hypre preconditioner 0.59 51.84
>>>>> 3 32481 rectangle no 40 1 200 GMRES,Hypre preconditioner 1.95 59.1
>>>>> 4 128961 rectangle no 40 1 200 GMRES,Hypre preconditioner 7.05 89.71
>>>>> 5 513921 rectangle no 40 1 200 GMRES,Hypre preconditioner 26.76 110.58
>>>>> 6 2051841 rectangle no 40 1 200 GMRES,Hypre preconditioner 104.21
>>>>> 232.05
>>>>> *7* *8199681* *rectangle* *no* *40* *1* *200* *GMRES,Hypre
>>>>> preconditioner* *411.26* *703.27* *140.29*
>>>>> *8* *8199681* *rectangle* *no* *80* *2* *200* *GMRES,Hypre
>>>>> preconditioner* *206.6* *387.25* *62.04*
>>>>> *9* *8199681* *rectangle* *no* *160* *4* *200* *GMRES,Hypre
>>>>> preconditioner* *104.28* *245.3* *32.76*
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> 10 2121 triangle yes 40 1 200 GMRES,Hypre preconditioner 0.49 61.78
>>>>> 11 15090 triangle yes 40 1 200 GMRES,Hypre preconditioner 2.32 96.61
>>>>> 12 59847 triangle yes 40 1 200 GMRES,Hypre preconditioner 8.28 176.14
>>>>> 13 238568 triangle yes 40 1 200 GMRES,Hypre preconditioner 31.89
>>>>> 573.73
>>>>> *14* *953433* *triangle* *yes* *40* *1* *200* *GMRES,Hypre
>>>>> preconditioner* *119.23* *2102.54* *44.11*
>>>>> *15* *953433* *triangle* *yes* *80* *2* *200* *GMRES,Hypre
>>>>> preconditioner* *72.99* *2123.8* *24.36*
>>>>> *16* *953433* *triangle* *yes* *160* *4* *200* *GMRES,Hypre
>>>>> preconditioner* *48.65* *2076.25* *14.87*
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> 17 55770 prism yes 40 1 200 GMRES,Hypre preconditioner 18.46 219.39
>>>>> 18 749814 prism yes 40 1 200 GMRES,Hypre preconditioner 149.86 2412.39
>>>>> 19 7000050 prism yes 40 to 640 1 to 16 200 GMRES,Hypre preconditioner
>>>>> out_of_memory
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> *20* *7000050* *prism* *yes* *64* *2* *480* *GMRES,Hypre
>>>>> preconditioner* *890.92* *17214.41*
>>>>>
>>>>> The error information of scenario 19 is shown below:
>>>>>
>>>>> kernel messages produced during job executions:
>>>>> [Oct 9 10:41] mpiexec.hydra invoked oom-killer: gfp_mask=0x200da,
>>>>> order=0, oom_score_adj=0
>>>>> [  +0.010274] mpiexec.hydra cpuset=/ mems_allowed=0-1
>>>>> [  +0.006680] CPU: 2 PID: 144904 Comm: mpiexec.hydra Tainted:
>>>>> G           OE  ------------   3.10.0-862.14.4.el7.x86_64 #1
>>>>> [  +0.013365] Hardware name: Lenovo ThinkSystem SD530
>>>>> -[7X21CTO1WW]-/-[7X21CTO1WW]-, BIOS -[TEE124N-1.40]- 06/12/2018
>>>>> [  +0.012866] Call Trace:
>>>>> [  +0.003945]  [<ffffffffb3313754>] dump_stack+0x19/0x1b
>>>>> [  +0.006995]  [<ffffffffb330e91f>] dump_header+0x90/0x229
>>>>> [  +0.007121]  [<ffffffffb2cfa982>] ? ktime_get_ts64+0x52/0xf0
>>>>> [  +0.007451]  [<ffffffffb2d5141f>] ? delayacct_end+0x8f/0xb0
>>>>> [  +0.007393]  [<ffffffffb2d9ac94>] oom_kill_process+0x254/0x3d0
>>>>> [  +0.007592]  [<ffffffffb2d9a73d>] ? oom_unkillable_task+0xcd/0x120
>>>>> [  +0.007978]  [<ffffffffb2d9a7e6>] ? find_lock_task_mm+0x56/0xc0
>>>>> [  +0.007729]  [<ffffffffb2d9b4d6>] *out_of_memory+0x4b6/0x4f0*
>>>>> [  +0.007358]  [<ffffffffb330f423>] __alloc_pages_slowpath+0x5d6/0x724
>>>>> [  +0.008190]  [<ffffffffb2da18b5>] __alloc_pages_nodemask+0x405/0x420
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Danyang
>>>>>
>>>>
>>>>
>>>> --
>>>> 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/>
>>>>
>>>
>>
>> --
>> 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/>
>>
>

-- 
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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