On Tue, Feb 23, 2021 at 11:10 AM 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?
>
> 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
>
I'm going to guess threading, although huge pages can also be a problem on
a machine under heavy load running other processes. Call overhead may also
matter for such small matrices.
Chuck
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