Re: [Numpy-discussion] Speed performance on array constant set

[Christopher B. <Chr...@no...>]

Sasha wrote:

>>python -m timeit  -s "import numpy as na; a = na.zeros((1000,1000))" "a.fill(7)"

or use a += 7:

$ python2.4 -m timeit  -s "import numpy as na; a = 
na.zeros((1000,1000))" "a.fill(7)"

100 loops, best of 3: 6.95 msec per loop

$ python2.4 -m timeit  -s "import numpy as na; a = 
na.zeros((1000,1000))" "a += 7"

100 loops, best of 3: 3.24 msec per loop

A factor of 2 speedup for me. I don't know why fill is slower.


> So IPP gives me 3X, which leads me to ask about plans for IPP / SSE for 
> NumPy, no offense intended to non Intel users.

I"ve wondered about this as as well, though not necessarily  IPP / SEE. 
It seems that BLAS should provide some optimizations that could be used 
outside of the strictly linear algebra functions, like element-wise 
multiplication, array copying, etc. However, I haven't looked into it, 
and I suppose it would make for a lot of special-case code.

-Chris


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Chr...@no...