[MRG] MNT: Use nrm2 to find the residuals squared
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Quick benchmark below for comparison. In [1]: import numpy as np
In [2]: from scipy import linalg
In [3]: a = 2 * np.random.random((100, 110)) - 1
In [4]: %timeit b = a.copy();
4.66 µs ± 181 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [5]: %timeit b = a.copy(); b **= 2; b.sum()
19.5 µs ± 371 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [6]: nrm2, = linalg.get_blas_funcs(('nrm2',), (a,))
In [7]: %timeit nrm2(a) ** 2
10.7 µs ± 66.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) |
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Using BLAS's `nrm2` is a bit faster than squaring the residuals in-place and summing them. So switch to using `nrm2` instead. Interestingly it doesn't appear necessary to flatten the array first as the BLAS function interprets the array as flat under the hood.
nrm2 to find the residuals squarednrm2 to find the residuals squared
agramfort
approved these changes
Aug 28, 2018
jnothman
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Aug 29, 2018
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And which is more readable? |
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We already use |
jnothman
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jnothman
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Using BLAS's
nrm2is a bit faster than squaring the residuals in-place and summing them. So switch to usingnrm2instead. Interestingly it doesn't appear necessary to flatten the array first as the BLAS function interprets the array as flat under the hood.