On Wed, Oct 21, 2009 at 6:12 PM, Francesc Alted <fal...@pytables.org> wrote: > A Wednesday 21 October 2009 07:44:39 Mathieu Blondel escrigué: >> Hello, >> >> About one year ago, a high-level, objected-oriented SIMD API was added >> to Mono. For example, there is a class Vector4f for vectors of 4 >> floats and this class implements methods such as basic operators, >> bitwise operators, comparison operators, min, max, sqrt, shuffle >> directly using SIMD operations. > [clip] > > It is important to stress out that all the above operations, except probably > sqrt, are all memory-bound operations, and that implementing them for numpy > would not represent a significant improvement at all.
> This is because numpy is a package that works mainly with arrays in an > element-wise way, and in this scenario, the time to transmit data to CPU > dominates, by and large, over the time to perform operations. Is it general, or just for simple operations in numpy and ufunc ? I remember that for music softwares, SIMD used to matter a lot, even for simple bus mixing (which is basically a ax+by with a, b scalars and x y the input arrays). Do you have any interest in adding SIMD to some core numpy (transcendental functions). If so, I would try to go back to the problem of runtime SSE detection and loading of optimized shared library in a cross-platform way - that's something which should be done at some point in numpy, and people requiring it would be a good incentive. David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
