Data Science Special Topics · NC State University Libraries

Parallelization with Python

Tuesday, October 27, 2026, 6:00–7:30 pm · Online (Zoom)

Use more of your computer’s cores to get the same work done sooner.

What we’ll do

• Learn the types of parallelization and where your own work fits.
• Spread an “embarrassingly parallel” task across cores with Python’s multiprocessing.
• Time it across core counts to see where the gains level off.
• See a case where parallel is slower, and why.

Get the notebook

• Google Colab, nothing to install: open the learner notebook in Colab, click Copy to Drive, and run the two Setup cells.
• JupyterLab on your laptop, all your cores: follow the Setup steps, about 15 minutes.

The filled notebook has every answer. Follow along in the slides.