[petsc-users] DMPlex memory problem in scaling test

Matthew Knepley knepley at gmail.com
Thu Oct 10 07:34:36 CDT 2019 

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? 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/>
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