[Numpy-discussion] array.sum() slower than expected along some array axes?
Keith Goodman
kwgoodman@gmail....
Sat Feb 3 19:47:08 CST 2007
On 2/3/07, Stephen Simmons <mail@stevesimmons.com> wrote:
> Does anyone know why there is an order of magnitude difference
> in the speed of numpy's array.sum() function depending on the axis
> of the matrix summed?
>
> To see this, import numpy and create a big array with two rows:
> >>> import numpy
> >>> a = numpy.ones([2,1000000], 'f4')
>
> Then using ipython's timeit function:
> Time (ms)
> sum(a) 20
> a.sum() 9
> a.sum(axis=1) 9
> a.sum(axis=0) 159
> numpy.dot(numpy.ones(a.shape[0], a.dtype), a) 15
>
> This last one using a dot product is functionally equivalent
> to a.sum(axis=0), suggesting that the slowdown is due to how
> indexing is implemented in array.sum().
I don't know how much time this would account for, but a.sum(0) has to
create a much larger array than a.sum(1) does.
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