On Tue, Dec 25, 2018 at 12:10 AM Jed Brown wrote:
> Mark Adams writes:
>
> > On Mon, Dec 24, 2018 at 4:56 PM Jed Brown wrote:
> >
> >> Mark Adams via petsc-users writes:
> >>
> >> > Anyway, my data for this is in my SC 2004 paper (MakeNextMat_private
> in
> >> > attached, NB, this is code that
Mark Adams writes:
> On Mon, Dec 24, 2018 at 4:56 PM Jed Brown wrote:
>
>> Mark Adams via petsc-users writes:
>>
>> > Anyway, my data for this is in my SC 2004 paper (MakeNextMat_private in
>> > attached, NB, this is code that I wrote in grad school). It is memory
>> > efficient and simple, jus
On Mon, Dec 24, 2018 at 4:56 PM Jed Brown wrote:
> Mark Adams via petsc-users writes:
>
> > Anyway, my data for this is in my SC 2004 paper (MakeNextMat_private in
> > attached, NB, this is code that I wrote in grad school). It is memory
> > efficient and simple, just four nested loops i,j,I,J:
Mark Adams via petsc-users writes:
> Anyway, my data for this is in my SC 2004 paper (MakeNextMat_private in
> attached, NB, this is code that I wrote in grad school). It is memory
> efficient and simple, just four nested loops i,j,I,J: C(I,J) =
> P(i,I)*A(i,j)*P(j,J). In eyeballing the numbers a
Wow, this is an old thread.
Sorry if I sound like an old fart talking about the good old days but I
originally did RAP. in Prometheus, in a non work optimal way that might be
of interest. Not hard to implement. I bring this up because we continue to
struggle with this damn thing. I think this appr
OK, so this thread has drifted, see title :)
On Fri, Dec 21, 2018 at 10:01 PM Fande Kong wrote:
> Sorry, hit the wrong button.
>
>
>
> On Fri, Dec 21, 2018 at 7:56 PM Fande Kong wrote:
>
>>
>>
>> On Fri, Dec 21, 2018 at 9:44 AM Mark Adams wrote:
>>
>>> Also, you mentioned that you are using 10
Sorry, hit the wrong button.
On Fri, Dec 21, 2018 at 7:56 PM Fande Kong wrote:
>
>
> On Fri, Dec 21, 2018 at 9:44 AM Mark Adams 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
>>
Thanks so much, Hong,
If any new finding, please let me know.
On Fri, Dec 21, 2018 at 9:36 AM Zhang, Hong wrote:
> Fande:
> I will explore it and get back to you.
> Does anyone know how to profile memory usage?
>
We are using gperftools
https://gperftools.github.io/gperftools/heapprofile.html
On Fri, Dec 21, 2018 at 12:55 PM Zhang, Hong wrote:
> Matt:
>
>> Does anyone know how to profile memory usage?
>>>
>>
>> The best serial way is to use Massif, which is part of valgrind. I think
>> it might work in parallel if you
>> only look at one process at a time.
>>
>
> Can you give an examp
Matt:
Does anyone know how to profile memory usage?
The best serial way is to use Massif, which is part of valgrind. I think it
might work in parallel if you
only look at one process at a time.
Can you give an example of using Massif?
For example, how to use it on petsc/src/ksp/ksp/examples/tut
On Fri, Dec 21, 2018 at 11:36 AM Zhang, Hong via petsc-users <
petsc-users@mcs.anl.gov> wrote:
> Fande:
> I will explore it and get back to you.
> Does anyone know how to profile memory usage?
>
The best serial way is to use Massif, which is part of valgrind. I think it
might work in parallel if
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
proble
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 a
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 simul
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.
mailto:bsm...@mcs.anl
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
> wrote:
>
>
>
> On Thu, Dec 20, 2018 at 4:43 PM Zhang, Hong wrote:
> Fande:
> Hong,
> Tha
> On Dec 20, 2018, at 5:51 PM, Zhang, Hong via petsc-users
> 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 wrote:
> Mark,
> Below is the copy of my email sent to you on Feb 27:
>
> I imple
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
mailto:hzh...@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 implementation
Mark,
Fixed
https://bitbucket.org/petsc/petsc/commits/68eacb73b84ae7f3fd7363217d47f23a8f967155
Run ex56 gives
mpiexec -n 8 ./ex56 -ne 13 ... -h |grep via
-mattransposematmult_via Algorithmic approach (choose one of)
scalable nonscalable matmatmult (MatTransposeMatMult)
-matmatmult_via Algori
Mark:
>
> I am not seeing these options with -help ...
>
Hmm, this might be a bug - I'll check it.
Hong
>
> On Wed, May 3, 2017 at 10:05 PM, Hong wrote:
>
>> I basically used 'runex56' and set '-ne' be compatible with np.
>> Then I used option
>> '-matptap_via scalable'
>> '-matptap_via hypre'
>
Thanks Hong,
I am not seeing these options with -help ...
On Wed, May 3, 2017 at 10:05 PM, Hong wrote:
> I basically used 'runex56' and set '-ne' be compatible with np.
> Then I used option
> '-matptap_via scalable'
> '-matptap_via hypre'
> '-matptap_via nonscalable'
>
> I attached a job script
I basically used 'runex56' and set '-ne' be compatible with np.
Then I used option
'-matptap_via scalable'
'-matptap_via hypre'
'-matptap_via nonscalable'
I attached a job script below.
In master branch, I set default as 'nonscalable' for small - medium size
matrices, and automatically switch to
Hong,the input files do not seem to be accessible. What are the command
line option? (I don't see a "rap" or "scale" in the source).
On Wed, May 3, 2017 at 12:17 PM, Hong wrote:
> Mark,
> Below is the copy of my email sent to you on Feb 27:
>
> I implemented scalable MatPtAP and did comparison
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:
(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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