Hi,
On Tue, 23 Feb 2021 at 19.11, Neal Becker <ndbeck...@gmail.com> wrote:
> I have code that performs dot product of a 2D matrix of size (on the
> order of) [1000,16] with a vector of size [1000]. The matrix is
> float64 and the vector is complex128. I was using numpy.dot but it
> turned out to be a bottleneck.
>
> So I coded dot2x1 in c++ (using xtensor-python just for the
> interface). No fancy simd was used, unless g++ did it on it's own.
>
> On a simple benchmark using timeit I find my hand-coded routine is on
> the order of 1000x faster than numpy? Here is the test code:
> My custom c++ code is dot2x1. I'm not copying it here because it has
> some dependencies. Any idea what is going on?
I had a similar experience - albeit with an older numpy and Python 2.7, so
my comments are easily outdated and irrelevant. This was on Windows 10 64
bit, way more than plenty RAM.
It took me forever to find out that numpy.dot was the culprit, and I ended
up using fortran + f2py. Even with the overhead of having to go through
f2py bridge, the fortran dot_product was several times faster.
Sorry if It doesn’t help much.
Andrea.
>
> import numpy as np
>
> from dot2x1 import dot2x1
>
> a = np.ones ((1000,16))
> b = np.array([ 0.80311816+0.80311816j, 0.80311816-0.80311816j,
> -0.80311816+0.80311816j, -0.80311816-0.80311816j,
> 1.09707981+0.29396165j, 1.09707981-0.29396165j,
> -1.09707981+0.29396165j, -1.09707981-0.29396165j,
> 0.29396165+1.09707981j, 0.29396165-1.09707981j,
> -0.29396165+1.09707981j, -0.29396165-1.09707981j,
> 0.25495815+0.25495815j, 0.25495815-0.25495815j,
> -0.25495815+0.25495815j, -0.25495815-0.25495815j])
>
> def F1():
> d = dot2x1 (a, b)
>
> def F2():
> d = np.dot (a, b)
>
> from timeit import timeit
> print (timeit ('F1()', globals=globals(), number=1000))
> print (timeit ('F2()', globals=globals(), number=1000))
>
> In [13]: 0.013910860987380147 << 1st timeit
> 28.608758996007964 << 2nd timeit
> --
> Those who don't understand recursion are doomed to repeat it
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