dolfin team mailing list archive

[Johan Hake <747297@xxxxxxxxxxxxxxxxxx>]

On Friday April 1 2011 14:12:00 Joachim Haga wrote:
> Aha, not so simple then. I don't quite get the multiple backend use
> case, but if it's supported then it's supported.

Well, it is not prohibited. It is probably not used by anyone. The point is 
that Python is Python and any thing that is not prohibited is possible and we 
need to make sure it is not possible to multiply an EpetraMatrix with a 
PETScVector.
 
> I didn't quite understand your suggestion. Do you mean to make
> down_cast() a method on Matrix et al instead of a global method? That
> sounds nice...

Yes something like that. But I am not sure we are able to get around a check 
each time a Vector/Matrix is created. And then we are back to step one, I 
guess.

> Anyhow: I'm away for six weeks, starting tomorrow, and don't know how
> much I'll be able to communicate when away. Feel free to leave this bug
> (and others, don't know if I have time now to report more) unresolved.
> Unless you want to fix them, of course :)

Have a good vacation!

Johan

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https://bugs.launchpad.net/bugs/747297

Title:
  Python: Cache tensor types

Status in DOLFIN:
  New

Bug description:
  In a matrix-multiplication heavy Python workload, I see something like
  5-10% of the time being spent in get_tensor_type(). The attached patch
  memoizes the result, per type. Seems to work fine, but should be
  sanity checked (is per-type result ok?).