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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion