[petsc-users] Problem with solving Poisson eqn for some cases
Matthew Knepley
knepley at gmail.com
Mon Mar 19 05:32:46 CDT 2018
On Mon, Mar 19, 2018 at 5:19 AM, TAY wee-beng <zonexo at gmail.com> wrote:
>
> On 17/3/2018 1:15 AM, Matthew Knepley wrote:
>
> On Fri, Mar 16, 2018 at 12:54 PM, TAY wee-beng <zonexo at gmail.com> wrote:
>
>>
>> On 15/3/2018 6:21 PM, Matthew Knepley wrote:
>>
>> On Thu, Mar 15, 2018 at 3:51 PM, TAY wee-beng <zonexo at gmail.com> wrote:
>>
>>> Hi,
>>>
>>> I'm running a CFD code which solves the momentum and Poisson eqns.
>>>
>>> Due to poor scaling with HYPRE at higher cpu no., I decided to try using
>>> PETSc with boomeramg and gamg.
>>>
>>> I tested for some small cases and it work well. However, for the large
>>> problem which has poor scaling, it gives an error when I change my Poisson
>>> solver from pure HYPRE to PETSc with boomeramg and gamg.
>>>
>>> The error is :
>>>
>>> Caught signal number 11 SEGV: Segmentation Violation, probably memory
>>> access out of range
>>>
>>> I tried using:
>>>
>>> -poisson_ksp_type richardson -poisson_pc_type hypre
>>> -poisson_pc_type_hypre boomeramg
>>>
>>> -poisson_ksp_type gmres -poisson_pc_type hypre -poisson_pc_type_hypre
>>> boomeramg
>>>
>>> -poisson_pc_type gamg -poisson_pc_gamg_agg_nsmooths 1
>>>
>>> but they all gave similar error.
>>>
>>> So why is this so? How should I troubleshoot? I am now running a debug
>>> ver of PETSc to check the error msg.
>>
>>
>> 1) For anything like this, we would like to see a stack trace from the
>> debugger or valgrind output.
>>
>> 2) We do have several Poisson examples. Does it fail for you on those?
>>
>> Hi,
>>
>> Can you recommend me some suitable egs? Esp in Fortran?
>>
>
> Here is 2D Poisson
>
> https://bitbucket.org/petsc/petsc/src/4b6141395f14f0c7d1415a2ff0158e
> ec75a27d63/src/snes/examples/tutorials/ex5f.F90?at=master&
> fileviewer=file-view-default
>
>
>>
>> 3) You can also try ML, which is the same type of MG as GAMG.
>> (--download-ml).
>>
>> I have recompiled PETSc with ML. Is there an example command line options
> which I can use for ML?
>
-pc_type ml
> Another question is generally speaking, is geometric multigrid (GMG)
> faster than algebraic?
>
No, only for the setup time.
> I tested on a small problem and the time taken varies from 1.15min (HYPRE,
> geometric) to 3.25 (GAMG). BoomerAMG is 1.45min.
>
I am not sure what you are running when you say Hypre geometric.
> Besides HYPRE, is there any other GMG I can use?
>
As I said above, it does not converge any faster and the solve is not
faster, its all setup time, so small problems will look faster.
You should be doing 10-20 iterates. If you are doing more, MG is not
working.
If you truly have a structured grid, then use DMDA in PETSc and you can use
GMG with
-pc_type mg -pc_mg_nlevels <n>
Matt
> My cluster can't connect to the internet. Where can I 1st download it?
>>
>> Similarly, how can I find out the location of the ext software by myself?
>>
>
> The locations are all in the configure Python modules:
>
> https://bitbucket.org/petsc/petsc/src/4b6141395f14f0c7d1415a2ff0158e
> ec75a27d63/config/BuildSystem/config/packages/ml.py?at=
> master&fileviewer=file-view-default
>
> Thanks,
>
> Matt
>
>
>> Thanks,
>>
>> Matt
>>
>>
>>>
>>> --
>>> Thank you very much.
>>>
>>> Yours sincerely,
>>>
>>> ================================================
>>> TAY Wee-Beng (Zheng Weiming) 郑伟明
>>> Personal research webpage: http://tayweebeng.wixsite.com/website
>>> Youtube research showcase: https://www.youtube.com/channe
>>> l/UC72ZHtvQNMpNs2uRTSToiLA
>>> linkedin: www.linkedin.com/in/tay-weebeng
>>> ================================================
>>>
>>>
>>
>>
>> --
>> 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.caam.rice.edu/%7Emk51/>
>>
>>
>>
>
>
> --
> 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.caam.rice.edu/%7Emk51/>
>
>
>
--
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.caam.rice.edu/~mk51/>
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