We are now ready for some example code and experiments. Let’s get to work!
Our test function
We first define a function that we can use to benchmark our different options. All the following examples use the same function, called heavy :
def heavy(n, myid): for x in range(1, n): for y in range(1, n): x**y print(myid, "is done")
The heavy function is a nested loop that does multiplication. It is a CPU-bound function. If you observe your system while running this, you’ll see CPU usage close to 100% (for one core). You can replace it with anything you want, but beware of race conditions — don’t use shared objects or variables.
We’ll be running this function in different ways and explore the differences between a regular, single thread Python program, multithreading, and multiprocessing.
Option 1: The baseline
Each Python program has at least one thread: the main thread. Below you’ll find the single-threaded version, which serves as our baseline in terms of speed. It runs our heavy function 80 times, sequentially:
import time # A CPU heavy calculation, just # as an example. This can be # anything you like def heavy(n, myid): for x in range(1, n): for y in range(1, n): x**y print(myid, "is done") def sequential(n): for i in range(n): heavy(500, i) if __name__ == "__main__": start = time.time() sequential(80) end = time.time() print("Took: ", end - start)
On my system, this takes about 46 seconds to run to completion.
Note that the
if __name__ == "__main__": part is required for this to work on Windows computers, but it’s good form to always use it.