# [Numpy-discussion] array.sum() slower than expected along some array axes?

Keith Goodman kwgoodman@gmail....
Sat Feb 3 20:12:39 CST 2007

```On 2/3/07, Robert Kern <robert.kern@gmail.com> wrote:
> Keith Goodman wrote:
> > 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.
>
> However, so does sum(a) and numpy.dot().

The speed difference across axis 0 and 1 is also seen in Octave and
Matlab (but it is more like a factor of 5). But in those languages
axis=0 is much faster. And numpy, if I remember, stores arrays in the
opposite way as Octave (by row or column, I forget).

So a lot of the speed difference could be in how the array is stored.

http://velveeta.che.wisc.edu/octave/lists/help-octave/2005/2195
http://velveeta.che.wisc.edu/octave/lists/help-octave/2005/1912
http://velveeta.che.wisc.edu/octave/lists/help-octave/2005/1897
```