[Numpy-discussion] Why is numpy.abs so much slower on complex64 than complex128 under windows 32-bit?

[Henry Gomersall]

Here is the body of a post I made on stackoverflow, but it seems to be a 
non-obvious issue. I was hoping someone here might be able to shed light 
on it...

On my 32-bit Windows Vista machine I notice a significant (5x) slowdown 
when taking the absolute values of a fairly large |numpy.complex64| 
array when compared to a |numpy.complex128| array.

|>>>  import  numpy
>>>  a=  numpy.random.randn(256,2048)  +  1j*numpy.random.randn(256,2048)
>>>  b=  numpy.complex64(a)
>>>  timeit c=  numpy.float32(numpy.abs(a))
10  loops,  best of3:  27.5  ms per loop
>>>  timeit c=  numpy.abs(b)
1  loops,  best of3:  143  ms per loop
|

Obviously, the outputs in both cases are the same (to operating precision).

I do not notice the same effect on my Ubuntu 64-bit machine (indeed, as 
one might expect, the double precision array operation is a bit slower).

Is there a rational explanation for this?

Is this something that is common to all windows?

In a related note of confusion, the times above are notably (and 
consistently) different (shorter) to that I get doing a naive `st = 
time.time(); numpy.abs(a); print time.time()-st`. Is this to be expected?

Cheers,

Henry


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