[petsc-users] GAMG scaling

Fri Dec 21 20:56:33 CST 2018

On Fri, Dec 21, 2018 at 9:44 AM Mark Adams <mfadams at lbl.gov> wrote:

> Also, you mentioned that you are using 10 levels. This is very strange
> with GAMG. You can run with -info and grep on GAMG to see the sizes and the
> number of non-zeros per level. You should coarsen at a rate of about 2^D to
> 3^D with GAMG (with 10 levels this would imply a very large fine grid
> problem so I suspect there is something strange going on with coarsening).
> Mark
>

Hi Mark,

Thanks for your email. We did not try GAMG much for our problems since we
still have troubles to figure out how to effectively use GAMG so far.
Instead, we are building our own customized  AMG  that needs to PtAP to
construct coarse matrices.  The customized AMG works pretty well for our
specifc

>
> On Fri, Dec 21, 2018 at 11:36 AM Zhang, Hong via petsc-users <
> petsc-users at mcs.anl.gov> wrote:
>
>> Fande:
>> I will explore it and get back to you.
>> Does anyone know how to profile memory usage?
>> Hong
>>
>> Thanks, Hong,
>>>
>>> I just briefly went through the code. I was wondering if it is possible
>>> to destroy "c->ptap" (that caches a lot of intermediate data) to release
>>> the memory after the coarse matrix is assembled. I understand you may still
>>> want to reuse these data structures by default but for my simulation, the
>>> preconditioner is fixed and there is no reason to keep the "c->ptap".
>>>
>>
>>> It would be great, if we could have this optional functionality.
>>>
>>> Fande Kong,
>>>
>>> On Thu, Dec 20, 2018 at 9:45 PM Zhang, Hong <hzhang at mcs.anl.gov> wrote:
>>>
>>>> We use nonscalable implementation as default, and switch to scalable
>>>> for matrices over finer grids. You may use option '-matptap_via scalable'
>>>> to force scalable PtAP  implementation for all PtAP. Let me know if it
>>>> works.
>>>> Hong
>>>>
>>>> On Thu, Dec 20, 2018 at 8:16 PM Smith, Barry F. <bsmith at mcs.anl.gov>
>>>> wrote:
>>>>
>>>>>
>>>>>   See MatPtAP_MPIAIJ_MPIAIJ(). It switches to scalable automatically
>>>>> for "large" problems, which is determined by some heuristic.
>>>>>
>>>>>    Barry
>>>>>
>>>>>
>>>>> > On Dec 20, 2018, at 6:46 PM, Fande Kong via petsc-users <
>>>>> petsc-users at mcs.anl.gov> wrote:
>>>>> >
>>>>> >
>>>>> >
>>>>> > On Thu, Dec 20, 2018 at 4:43 PM Zhang, Hong <hzhang at mcs.anl.gov>
>>>>> wrote:
>>>>> > Fande:
>>>>> > Hong,
>>>>> > Thanks for your improvements on PtAP that is critical for MG-type
>>>>> algorithms.
>>>>> >
>>>>> > On Wed, May 3, 2017 at 10:17 AM Hong <hzhang at mcs.anl.gov> wrote:
>>>>> > Mark,
>>>>> > Below is the copy of my email sent to you on Feb 27:
>>>>> >
>>>>> > I implemented scalable MatPtAP and did comparisons of three
>>>>> implementations using ex56.c on alcf cetus machine (this machine has small
>>>>> memory, 1GB/core):
>>>>> > - nonscalable PtAP: use an array of length PN to do dense axpy
>>>>> > - scalable PtAP:       do sparse axpy without use of PN array
>>>>> >
>>>>> > What PN means here?
>>>>> > Global number of columns of P.
>>>>> >
>>>>> > - hypre PtAP.
>>>>> >
>>>>> > The results are attached. Summary:
>>>>> > - nonscalable PtAP is 2x faster than scalable, 8x faster than hypre
>>>>> PtAP
>>>>> > - scalable PtAP is 4x faster than hypre PtAP
>>>>> > - hypre uses less memory (see job.ne399.n63.np1000.sh)
>>>>> >
>>>>> > I was wondering how much more memory PETSc PtAP uses than hypre? I
>>>>> am implementing an AMG algorithm based on PETSc right now, and it is
>>>>> working well. But we find some a bottleneck with PtAP. For the same P and
>>>>> A, PETSc PtAP fails to generate a coarse matrix due to out of memory, while
>>>>> hypre still can generates the coarse matrix.
>>>>> >
>>>>> > I do not want to just use the HYPRE one because we had to duplicate
>>>>> matrices if I used HYPRE PtAP.
>>>>> >
>>>>> > It would be nice if you guys already have done some compassions on
>>>>> these implementations for the memory usage.
>>>>> > Do you encounter memory issue with  scalable PtAP?
>>>>> >
>>>>> > By default do we use the scalable PtAP?? Do we have to specify some
>>>>> options to use the scalable version of PtAP?  If so, it would be nice to
>>>>> use the scalable version by default.  I am totally missing something here.
>>>>> >
>>>>> > Thanks,
>>>>> >
>>>>> > Fande
>>>>> >
>>>>> >
>>>>> > Karl had a student in the summer who improved MatPtAP(). Do you use
>>>>> the latest version of petsc?
>>>>> > HYPRE may use less memory than PETSc because it does not save and
>>>>> reuse the matrices.
>>>>> >
>>>>> > I do not understand why generating coarse matrix fails due to out of
>>>>> memory. Do you use direct solver at coarse grid?
>>>>> > Hong
>>>>> >
>>>>> > Based on above observation, I set the default PtAP algorithm as
>>>>> 'nonscalable'.
>>>>> > When PN > local estimated nonzero of C=PtAP, then switch default to
>>>>> 'scalable'.
>>>>> > User can overwrite default.
>>>>> >
>>>>> > For the case of np=8000, ne=599 (see job.ne599.n500.np8000.sh), I
>>>>> get
>>>>> > MatPtAP                   3.6224e+01 (nonscalable for small mats,
>>>>> scalable for larger ones)
>>>>> > scalable MatPtAP     4.6129e+01
>>>>> > hypre                        1.9389e+02
>>>>> >
>>>>> > This work in on petsc-master. Give it a try. If you encounter any
>>>>> problem, let me know.
>>>>> >
>>>>> > Hong
>>>>> >
>>>>> > On Wed, May 3, 2017 at 10:01 AM, Mark Adams <mfadams at lbl.gov> wrote:
>>>>> > (Hong), what is the current state of optimizing RAP for scaling?
>>>>> >
>>>>> > Nate, is driving 3D elasticity problems at scaling with GAMG and we
>>>>> are working out performance problems. They are hitting problems at ~1.5B
>>>>> dof problems on a basic Cray (XC30 I think).
>>>>> >
>>>>> > Thanks,
>>>>> > Mark
>>>>> >
>>>>>
>>>>>
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