On 6 Jan 2015, at 10:47, Lawrence Mitchell <lawrence.mitchell@imperial.ac.uk> wrote:

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On 06/01/15 08:52, Eike Mueller wrote:

Hi Lawrence,

thanks for the explanation, so my understanding is that in general
PyOP2 is not aware of the COFFEE optimisation, and the wrapper code
that is generated in my PyOP2 parloop can’t handle the padding.

Yes, that's right.  You can follow this issue
https://github.com/coneoproject/COFFEE/issues/14 to see if anything
will change.  I hope so, since it's not a very nice model.

Since I call a PyOP2 parloop directly, I don’t have any control
over the wrapper code and at the moment it sounds like the only
thing I can do is switch off the COFFEE optimisation (which, I
suppose, has implications everwhere in the code, so I really
shouldn’t do this).

You can do the following in build_lma:

def build_lma(...):
   parameters["coffee"]["O2"] = False
   existing_code
   parameters["coffee"]["O2"] = True


That way the optimisations are only switched off for that one section.

Isn’t then there a problem with all PyOP2 parloops, and are you
going to do something about this?

So the problem is that you're doing something that isn't explicitly
supported by the PyOP2 data model: using a Kernel designed for
assembling into a Mat to assemble into a Dat.  It happens to work in
some cases.

I think it is probably useful to properly support a "build_lma_matrix"
style abstraction, but I haven't thought properly about how to do it
right.

Lawrence
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