I have a matrix and I want to compute the norm of each column. I tried using TimeIt N = 10^6 mat = randn(3, N)
@timeit mapslices(norm, mat, 1) 1 loops, best of 3: 915.13 ms per loop Dog slow, 100times slower then numpy: import numpy as np N = 10**6 mat = np.random.randn(3, N) %timeit np.linalg.norm(mat, axis=0) 100 loops, best of 3: 9.71 ms per loop If I understand it correctly, the reason is that mapslices can not really optimize on the norm argument, e.g. inline norm. I had two ideas how to solve this problem, both of which are problematic: 1) There is the NumericExtensions <https://github.com/lindahua/NumericExtensions.jl> package, which seems to be aimed at this kind of problem, however this package is deprecated, so I don't want to use it. 2) Write some code by hand. This gives speedup, but is still slower then numpy: N = 10^6 mat = randn(3, N) function f(mat) return sqrt(mat[1,:].^2 + mat[2,:].^2 + mat[3,:].^2) end @timeit f(mat) 10 loops, best of 3: 38.51 ms per loop So how to make this computation fast? And more generally what are ways speed up mapslices without using NumericExtensions?
