Anne Archibald wrote: > On 26/10/2007, Georg Holzmann <[EMAIL PROTECTED]> 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? -Travis _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
