Some more context over what Francesc said: If you mean using SSE for simple things like addition and multiplication, then you must be aware that NumPy's way of working means that it lends itself very badly to such optimizations. For small arrays, the Python interpreter overhead tends to dominate and for large arrays it's all about memory bus speef. There's a video online of a talk Francesc gave at PyData this year that explains this and the current options. People are working on it (e.g. right now in Numba and Cython) and down the road perhaps NumPy 3.0 or 4.0 can have better performance. But it's a pretty complicated work, it'd be difficult to dive in without learning more first. (Mark Florisson is currently working on a library that is reusable across projects which will bring SSE/vectorization to Cython (it beats Intel Fortran in some benchmarks! :-)) Dag -- Sent from my Android phone with K-9 Mail. Please excuse my brevity. Fode wrote: I am interested in adding SSE optimizations to numpy, where should I start? Fode