On Thu, Oct 22, 2009 at 11:31 AM, Sturla Molden <stu...@molden.no> wrote: > Mathieu Blondel skrev: >> 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. > I think you are confusing SIMD with Intel's MMX/SSE instruction set.
OK, I should have said "Object-oriented SIMD API that is implemented using hardware SIMD instructions". And when an ISA doesn't allow to perform a specific operation in only one instruction (say the absolute value of the differences), the operation can be implemented in terms of other instructions. > SIMD instructions in hardware for length-4 vectors are mostly useful for > 3D graphics. But they are not used a lot for that purpose, because GPUs > are getting common. SSE is mostly for rendering 3D graphics without a > GPU. There is nothing that prevents NumPy from having a Vector4f dtype, > that internally stores four float32 and is aligned at 16 byte > boundaries. But it would not be faster than the current float32 dtype. > Do you know why? Yes I know because this has already been explained in this very thread by someone before you! Mathieu _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion