[petsc-users] mixed precision

[Karl Rupp]

Hi Jim,

in addition to what Matt already said, keep in mind is that you usually 
won't see a two-fold performance gain in iterative solvers anyway, as 
the various integers used for storing the nonzeros in the sparse matrix 
don't change their size. I once played with an implementation of an 
non-preconditioned mixed-precision CG solver, and I only obtained about 
a 40 percent overall performance gain for well-conditioned systems. For 
less well-conditioned systems you may not get any better overall 
performance at all (or worse, fail to converge).

Best regards,
Karli


On 08/12/2013 12:32 PM, Matthew Knepley wrote:
> On Mon, Aug 12, 2013 at 12:24 PM, Jim Fonseca <jefonseca at gmail.com
> <mailto:jefonseca at gmail.com>> wrote:
>
>     Hi,
>     We are curious about the mixed-precision capabilities in NEMO5. I
>     see that there is a newish configure option to allow single
>     precision for linear solve. Other than that, I found this old post:
>     https://lists.mcs.anl.gov/mailman/htdig/petsc-users/2012-August/014842.html
>
>     Is there any other information about to see if we can take advantage
>     of this capability?
>
>
> Mixed-precision is hard, and especially hard in PETSc because the C type
> system is limited.
> However, it also needs to be embedded in an algorithm that can take
> advantage of it. I would
> always start out with a clear motivation:
>
>    - What would mixed precision accomplish in your code?
>
>    - What is the most possible benefit you would see?
>
> and decide if that is worth a large time investment.
>
>     Thanks,
>     Jim
>
>     --
>     Jim Fonseca, PhD
>     Research Scientist
>     Network for Computational Nanotechnology
>     Purdue University
>     765-496-6495 <tel:765-496-6495>
>     www.jimfonseca.com <http://www.jimfonseca.com>
>
>
>
>
>
> --
> 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