Areas of !NumPy that have come up as possible slow points:

 * PyArray_EnsureArray. It seems to be slow for python ints. This came up as a possible culprit
   for some pow() slowness.  PyArray_EnsureArray could special-check Python scalars for a speed-up.
 
 * Default (digits=0) case of around is 10x slower than {{{(x+0.5).astype(int).astype(float)}}}. Resolved: changeset:2151 implements fast rint function and adds a round method to ndarray that is about as fast for digits=0 case.
 
 * As of changeset:2173 {{{x.fill(1)}}} is 2x slower than {{{x += 1}}}.  One should be able to set memory to a constant value faster than autoincrement. [wiki:PossibleOptimizationAreas/FillDiscussion Discussion].  Implemented in changeset:2188.

 * As of changeset:2108 {{{x.sum(0)}}} is 5x slower than {{{x[0] + x[1]}}} for {{{x = zeros((2,500), 'f')}}}. [wiki:PossibleOptimizationAreas/ReduceDiscussion Discussion]

 * As of changeset:2108 {{{x.sum()}}} is 2x slower than {{{add.reduce(x)}}} for small arrays.

 * {{{pixels[:,:,:] = r, g, b}}} is 20x slower than copying from another large array. {{{pixels = zeros((600, 600, 3), 'uint8'); r = g = b = 255}}}