Hi everyone, I have a question concerning performance of numpy. I'm using it for heavy image processing tasks and often need more speed than the stock numpy delivers. Especially in numeric calculations with big arrays (in my current case shape = (8,4,1200,1600), 'float'). So I often rely on self written special modules which do use the IPP (Intel Performance Primitives) to speed up simple tasks like dividing a big array by a number.
I realize that it was not really feasible to support a proprietary library like the IPP in a beautifully crafted Open Source Project, but quite recently, AMD came up with two very interesting projects (SSEPlus and Framewave, links provided below) which are more or less a direct response to intels IPP. And the best: They are OpenSource (under a Apache license, afaik). My question is now: Is it intended/Is there interest to get this performance gain into numpy? Are their any political restrictions (license/project identity)? Is there already work underway? I for one would consider helping in a effort like that, because it would probably safe me time in the long run. (Sidenote: I'm aware that this optimization would only help INTEL/AMD boxes, but hardware acceleration is so common these days that it is a shame NOT to use it in a numbercrunching library. Implementing such a library on one architecture might make it easier to implement something similar on others too with other libs. Maybe we see CUDA support in numpy in the future....) Here are the links: http://sseplus.sourceforge.net/ http://framewave.sourceforge.net/ Greetings, Holger _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
