[Numpy-discussion] numpy 10x slower than native Python arrays for simple operations?

[Charles R Harris]

On Sat, Feb 6, 2010 at 2:21 PM, Joseph Turian <turian at gmail.com> wrote:

> I have done some profiling, and the results are completely
> counterintuitive. For simple array access operations, numpy and
> array.array are 10x slower than native Python arrays.
>
> I am using numpy 1.3.0, the standard Ubuntu 9.03 package.
>
> Why am I getting such slow access speeds?
> Note that for "array access", I am doing operations of the form:
>    a[i] += 1
>
> Profiles:
>
> [0] * 20000000
>   Access: 2.3M / sec
>   Initialization: 0.8s
>
> numpy.zeros(shape=(20000000,), dtype=numpy.int32)
>   Access: 160K/sec
>   Initialization: 0.2s
>
> array.array('L', [0] * 20000000)
>   Access: 175K/sec
>   Initialization: 2.0s
>
> array.array('L', (0 for i in range(20000000)))
>   Access: 175K/sec, presumably, based upon the profile for the other
> array.array
>   Initialization: 6.7s
>
> Any idea why my numpy array access is so slow?
>
>
Without seeing the whole script it is hard to tell. But numpy indexing is
slow and should be avoided when possible.

Chuck
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