[petsc-users] Guidance on GAMG preconditioning

[Justin Chang]

Matt and Mark thank you guys for your responses.

The reason I brought up GAMG was because it seems to me that this is the
preconditioner to use for elliptic problems. However, I am using CG/Jacobi
for my larger problems and the solver converges (with -ksp_atol and
-ksp_rtol set to 1e-8). Using GAMG I get rough the same wall-clock time,
but significantly fewer solver iterations.

As I also kind of mentioned in another mail, the ultimate purpose is to
compare how this "correction" methodology using the TAO solver (with
bounded constraints) performs compared to the original methodology using
the KSP solver (without constraints). I have the A for BLMVM and CG/Jacobi
and they are roughly 0.3 and 0.2 respectively (do these sound about
right?). Although the AI is higher for TAO , the ratio of actual FLOPS/s
over the AI*STREAMS BW is smaller, though I am not sure what conclusions to
make of that. This was also partly why I wanted to see what kind of metrics
another KSP solver/preconditioner produces.

Point being, if I were to draw such comparisons between TAO and KSP, would
I get crucified if people find out I am using CG/Jacobi and not GAMG?

Thanks,
Justin

On Fri, Jun 5, 2015 at 2:02 PM, Mark Adams <mfadams at lbl.gov> wrote:

>
>>>
>> The overwhleming cost of AMG is the Galerkin triple-product RAP.
>>
>>
> That is overstating it a bit.  It can be if you have a hard 3D operator
> and coarsening slowly is best.
>
> Rule of thumb is you spend 50% time is the solver and 50% in the setup,
> which is often mostly RAP (in 3D, 2D is much faster).  That way you are
> within 2x of optimal and it often works out that way anyway.
>
> Mark
>
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