[Numpy-discussion] f2py: ram usage

[George Nurser]

Yes, f2py is probably copying the arrays; you can check this by appending
 -DF2PY_REPORT_ON_ARRAY_COPY=1 to your call to f2py.

I normally prefer to keep the numpy arrays C-order (most efficient for
numpy) and simply pass the array transpose to the f2py-ized fortran routine.
This means that the fortran array indices are reversed, but this is the
most natural  way in any case.

--George Nurser

On 10 April 2016 at 11:53, Sebastian Berg <sebastian at sipsolutions.net>
wrote:

> On So, 2016-04-10 at 12:04 +0200, Vasco Gervasi wrote:
> > Hi all,
> > I am trying to write some code to do calculation onto an array: for
> > each row I need to do some computation and have a number as return.
> > To speed up the process I wrote a fortran subroutine that is called
> > from python [using f2py] for each row of the array, so the input of
> > this subroutine is a row and the output is a number.
> > This method works but I saw some speed advantage if I pass the entire
> > array to fortran and then, inside fortran, call the subroutine that
> > does the math; so in this case I pass an array and return a vector.
> > But I noticed that when python pass the array to fortran, the array
> > is copied and the RAM usage double.
>
> I expect that the fortran code needs your arrays to be fortran
> contiguous, so the wrappers need to copy them.
>
> The easiest solution may be to create your array in python with the
> `order="F"` flag. NumPy will have a tendency to prefer C-order and uses
> it as default though when doing something with an "F" ordered array.
>
> That said, I have never used f2py, so these are just well founded
> guesses.
>
> - Sebastian
>
>
>
> > Is there a way to "move" the array to fortran, I don't care if the
> > array is lost after the call to fortran.
> > The pyd module is generated using: python f2py.py -c --opt="-ffree
> > -form -Ofast" -m F2PYMOD F2PYMOD.f90
> >
> > Thanks
> > Vasco
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