These IPython notebooks are great. I had some setup issues getting started
on my computer (conflicting MPI versions), but that is solved. There
appear to be some subtle differences between the version of pandas I have
and what you made the notebook with. I can't plot the results (NaN values
in the dataframes result in some errors), but this is not an important
issue right now.
e.g., there is an issue with this line:
runtimes = runtimes.append({'N' : N, 'iterations' : iterations, 'mode' :
mode, 'suite' : suite, 'run time' : runtime})
pandas complains about needing "ignore_index=True" to do this operation
I know this is not the place to ask about pandas, but I thought I would
point that out.
Thanks for providing these as an example. I just need to spend some more
time trying to understand the IPython notebook and parallel-calculation
framework, to see how I can apply things from these notebooks to what I
want to do. It is all pretty slick, but I am like a fish out of water when
I can't use my emacs keybindings :)
Kris
On Fri, Feb 21, 2014 at 11:40 AM, Daniel Wheeler
<daniel.wheel...@gmail.com>wrote:
> On Wed, Jan 22, 2014 at 2:16 PM, Kris Kuhlman
> <kristopher.kuhl...@gmail.com> wrote:
> > I am trying to get a fipy problem to run faster in parallel. I have
> > successfully installed version 3.1 of fipy and tested it with trilinos
> and
> > pysparse. I have run the tests suggested at
>
> Hi Kris,
>
> I looked into using FiPy in parallel a little bit more, just with a
> very simple problem in 3D. The results are in two IPython notebooks.
> See
>
>
> http://nbviewer.ipython.org/github/wd15/fipy-efficiency/tree/master/notebooks/
>
> Parallel efficiency is anything between 0.25 and 0.6 for 48 processes.
>
>
> --
> Daniel Wheeler
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