I was trying out xtensor-python and started by writing a very simple sum function, after using the cookiecutter setup and enabling SIMD intrinsics with xsimd.
inline double sum_pytensor(xt::pytensor<double, 1> &m)
{
return xt::sum(m)();
}
inline double sum_pyarray(xt::pyarray<double> &m)
{
return xt::sum(m)();
}
Used setup.py to build my Python module, then tested out the summation function on NumPy arrays constructed from np.random.randn of different sizes, comparing to np.sum.
import timeit
def time_each(func_names, sizes):
setup = f'''
import numpy; import xtensor_basics
arr = numpy.random.randn({sizes})
'''
tim = lambda func: min(timeit.Timer(f'{func}(arr)',
setup=setup).repeat(7, 100))
return [tim(func) for func in func_names]
from functools import partial
sizes = [10 ** i for i in range(9)]
funcs = ['numpy.sum',
'xtensor_basics.sum_pyarray',
'xtensor_basics.sum_pytensor']
sum_timer = partial(time_each, funcs)
times = list(map(sum_timer, sizes))
This (possibly flawed) benchmark seemed to indicate that performance of xtensor for this basic function degraded for larger arrays as compared to NumPy.
numpy.sum xtensor_basics.sum_pyarray xtensor_basics.sum_pytensor
1 0.000268 0.000039 0.000039
10 0.000258 0.000040 0.000039
100 0.000247 0.000048 0.000049
1000 0.000288 0.000167 0.000164
10000 0.000568 0.001353 0.001341
100000 0.003087 0.013033 0.013038
1000000 0.045171 0.132150 0.132174
10000000 0.434112 1.313274 1.313434
100000000 4.180580 13.129517 13.129058
Any idea on why I'm seeing this? I'm guessing it's something NumPy utilizes that xtensor does not (yet), but I wasn't sure what it could be for a reduction as simple as this. I dug through xmath.hpp but didn't see anything obvious, and nothing like this is referenced in the documentation.
Versions
numpy 1.13.3
openblas 0.2.20
python 3.6.3
xtensor 0.12.1
xtensor-python 0.14.0