numpy float: 10x slower than builtin in arithmetic operations?

Question

I am getting really weird timings for the following code:

import numpy as np
s = 0
for i in range(10000000):
    s += np.float64(1) # replace with np.float32         


        

Answer 1

The answer is quite simple: the memory allocation might be part of it, but the biggest problem is that arithmetic operations for numpy scalars is done using "ufuncs" which are meant to be fast for several hundred values not just 1. There is some overhead in choosing the correct function to call and setting up the loops. Overhead which is un-necessary for scalars.

It was easier to just have the scalars be converted to 0-d arrays and then passed to the corresponding numpy ufunc then write separate calculation methods for each of the many different scalar types that NumPy supports.

The intent was that optimized versions of the scalar math would be added to the type-objects in C. This could still happen, but it never has happened because no-one has been motivated enough to do it. Possibly because the work-around is to convert numpy scalars to Python scalars which do have optimized arithmetic.