[Numpy-discussion] fortran array storage question

Anne Archibald peridot.faceted@gmail....
Fri Oct 26 10:27:31 CDT 2007

On 26/10/2007, Georg Holzmann <grh@mur.at> wrote:

> if in that example I also change the strides:
>    int s = tmp->strides[1];
>    tmp->strides[0] = s;
>    tmp->strides[1] = s * dim0[0];
> Then I get in python the fortran-style array in right order.

This is the usual way. More or less, at least. numpy is designed from
the start to handle arrays with arbitrary striding; this is how slices
are implemented, for example. There will be no major performance hit
from numpy code itself. The actual organization of data in memory will
of course affect the speed at which your code runs. The flags, as you
discovered, are just a performance optimization, so that code that
needs arrays organized as C- or FORTRAN-standard doesn't need to check
the strides every time.

I don't think numpy's loops - for example in ones((100,100))+eye(100)
- are smart about doing operations in an order that makes
cache-coherent use of memory. The important exception is the loops
that use ATLAS, which I think is mostly the dot() function.


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