Multiprocessing basics

The implementation of Python that we are using, CPython, has a mechanism called global interpreter lock (GIL). GIL's purpose is to make CPython thread-safe; it works by preventing the code from being executed by more than one thread at once.

With that limitation, multiprocessing in Python works by forking the running program (for example, making a copy of the state of the program) and sending it to another computer core. As a consequence, the new process comes with an overhead.

Let's try a simple code:

1. First, make a copy of the previous chapter folder in your geopy project and rename it to Chapter10.

2. Clean the Chapter10/output folder (delete all files in it).

3. Expand the Chapter10/experiments folder, right-click on it, and create a new Python file. Name it parallel.py.

4. Add this code to this new file:

   # coding=utf-8
   
   from datetime import datetime
   import multiprocessing as mp
   
   def an_expensive_function(text):
       for i in range(500):
           out = "{} {} {}"
           out.format...