[Numpy-discussion] fortran array storage question

Travis E. Oliphant oliphant@enthought....
Fri Oct 26 10:38:59 CDT 2007

Anne Archibald wrote:
> 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.
There is an optimization where-in the inner-loops are done over the 
dimension with the smallest stride. 

What other cache-coherent optimizations do you recommend?


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