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
--
Those who don't understand recursion are doomed to repeat it