By: Bradley Setzler
Re-posted from: https://juliaeconomics.com/2014/06/19/revisited-julia-vs-python-speed-comparison-bootstrapping-the-ols-mle/
I originally switched to Julia because Julia was estimating a complicated MLE about 100-times faster than Python. Yesterday, I demonstrated how to bootstrap the OLS MLE in parallel using Julia. I presented the amount of time required on my laptop to bootstrap 1,000 times: about 21.3 seconds on a single processor, 8.7 seconds using four processors.
For comparison, I translated this code into Python, using only NumPy and SciPy for the calculations, and Multiprocessing for the parallelization. The Python script is available here. For this relatively simple script, I find that Python requires 110.9 seconds on a single processor, 66.0 seconds on four processors.
Thus, Julia performed more than 5-times faster than Python on a single processor, and about 7.5-times faster on four processors.
I also considered increasing the number of bootstrap samples from 1,000 to 10,000. Julia requires 211 seconds on a single processor and 90 seconds on four processors. Python requires 1135 seconds on a single processor and 598 seconds on four processors. Thus, even as the size of the task became greater, Julia remained more than 5-times faster on one processor and around 7-times faster on four processors.
In this simple case, Julia is between 5- and 7.5-times faster than Python, depending on configuration.
Bradley J. Setzler
