Offtopic:
You should use randomized data for a fair representation. Maybe the algorithm performs a lot better or a lot worse for certain values. Maybe the functionality posted in the OP functions a lot better for very large values.
I would benchmark with random data which represents the full input range that could be expected. Furthermore I would also do the for-loop around 1 instance of the function. Then store the timings in array. Compute the mean, median, variance, standard deviation and show maybe a nice histogram :-)
What is also nice to do, is by changing the input size for each iteration, 2^0, 2^1, 2^2, 2^3,... elements and plot it to determine how the computation execution scales.