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

Really strange...I confirm the results in Ubuntu 11.04 32bit, python 2.7.1, numpy 1.5.1 (official packages):

import numpy as np
def testfloat():
    s = 0
    for i in range(10000000):  
        s+= float(1)
def testfloat32():
    s = 0
    for i in range(10000000):  
        s+= np.float32(1)
def testfloat64():
    s = 0
    for i in range(10000000):  
        s+= np.float64(1)

%time testfloat()
CPU times: user 4.66 s, sys: 0.06 s, total: 4.73 s
Wall time: 4.74 s

%time testfloat64()
CPU times: user 11.43 s, sys: 0.07 s, total: 11.50 s
Wall time: 11.57 s


%time testfloat32()
CPU times: user 47.99 s, sys: 0.09 s, total: 48.08 s
Wall time: 48.23 s

I don't see why float32 should be 5 times slower that float64.

Answer 2

Perhaps, that is why you should use Numpy directly instead of using loops.

s1 = np.ones(10000000, dtype=np.float)
s2 = np.ones(10000000, dtype=np.float32)
s3 = np.ones(10000000, dtype=np.float64)

np.sum(s1) <-- 17.3 ms
np.sum(s2) <-- 15.8 ms
np.sum(s3) <-- 17.3 ms