On 18/04/07, Robert Kern <[EMAIL PROTECTED]> wrote: > Sebastian Haase wrote: > > > Hi, > > I don't know much about ATLAS -- would there be other numpy functions > > that *could* or *should* be implemented using ATLAS !? > > Any ? > > Not really, no.
ATLAS is a library designed to implement linear algebra functions efficiently on many machines. It does things like reorder the multiplications and additions in matrix multiplication to make the best possible use of your cache, as measured by empirical testing. (FFTW does something similar for the FFT.) But ATLAS is only designed for linear algebra. If what you want to do is linear algebra, look at scipy for a full selection of linear algebra routines that make fairly good use of ATLAS where applicable. It would be perfectly possible, in principle, to implement an ATLAS-like library that handled a variety (perhaps all) of numpy's basic operations in platform-optimized fashion. But implementing ATLAS is not a simple process! And it's not clear how much gain would be available - it would almost certainly be noticeably faster only for very large numpy objects (where the python overhead is unimportant), and those objects can be very inefficient because of excessive copying. And the scope of improvement would be very limited; an expression like A*B+C*D would be much more efficient, probably, if the whole expression were evaluated at once for each element (due to memory locality and temporary allocation). But it is impossible for numpy, sitting inside python as it does, to do that. Anne M. Archibald _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
