Hi David, On Wed, Oct 21, 2009 at 3:56 PM, David Cournapeau <da...@ar.media.kyoto-u.ac.jp> wrote: > I am not sure how this could be applied to numpy case ? From what I can > understand, this cannot be directly applied to python: the described > changes are vm changes, and we cannot do anything at python vm level (I > would guess the python vm to be too primitive to implement this kind of > things anyway).
Yes in Mono this is realized with Just-In-Time compilation, so at the VM level. The reason I thought of Numpy rather than Cython is that Python's support for vectors/matrices is limited and Numpy has kind of become the standard for that in the Python world. I saw the video of Peter Norvig at the last Scipy conference who was suggesting to merge Numpy into Cython. The SIMD API would be an argument in favor of this too because of the possible interactions between such a SIMD API and an array API. > I don't see how the high level API at the assembly level (Mono.Simd) > would work either: the overhead of python and numpy to deal with 4 or 8 > items in python would make this API useless from a speed POV. My original idea was to write the code in C with Intel/Alvitec/Neon intrinsics and have this code binded to be able to call it from Python. So the SIMD code would be compiled already, ready to be called from Python. Like you said, there's a risk that the overhead of calling Python is bigger than the benefit of using SIMD instructions. If it's worth trying out, an experiment can be made with Vector4f to see if it's even worth continuing with other types. > This is only my opinion (read other numpy dev may disagree), but I think > that the numpy C code should be cleaned up before adding this kind of > features: there is still too much coupling between the pure C core and > the python machinery. Also, any use of SIMD code should be done at > runtime IMHO (so that one binary can be used on multiple architectures), > which has some issues on its own from a cross platform POV. I recently used SIMD instructions for a project and I realized that they cannot be activated in a standard Debian package, because the package has to remain general-purpose. So people who want to benefit the speed up have to compile my project from source... I also see that sometimes packages are available in different flavors (-msse, -msse2...). Mathieu _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
