Python is great for tasks like training machine learning models, performing numerical simulations, and quickly developing proof-of-concept solutions without setting up development tools and installing several dependencies. When performing these tasks, you also want to use your underlying hardware as much as possible for quick results. Parallelizing Python code enables this. However, using the standard CPython implementation means you cannot fully use the underlying hardware because of the global interpreter lock (GIL) that prevents running the bytecode from multiple threads simultaneously.
For each technique, this article lists some advantages and disadvantages and shows a code sample to help you understand what it’s like to use.
There are several common ways to parallelize Python code. You can launch several application instances or a script to perform jobs in parallel. This approach is great when you don’t need to exchange data between parallel jobs. Otherwise, sharing data between processes significantly reduces performance when aggregating data.
Starting multiple threads within the same process allows you to share data between jobs more efficiently. In this case, thread-based parallelization can offload some work to the background. However, the standard CPython implementation’s global interpreter lock (GIL) prevents running the bytecode in multiple threads simultaneously.