On 2/3/07, Robert Kern <[EMAIL PROTECTED]> wrote: > Keith Goodman wrote: > > On 2/3/07, Stephen Simmons <[EMAIL PROTECTED]> 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 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
