I think differently. Normal C loop codes are hard to optimize using SIMD as the context and it's freedom can be the limiting factor. But in python, the vector

I think differently. Normal C loop codes are hard to optimize using SIMD as the context and it's freedom can be the limiting factor. But in python, the vector operations are normally programmed using simple array semantics. For example in NumPy: >>> a = array( [2,3,4] ) >>> b = array( [2,3,4] ) >>> a+b [4,6,8] These can be easily converted into SIMD operations. It's matter of time before PyPy's new NumPy implementation take traction and make simply beautiful optimizing JITs using that.