Parallel Processing with Python

Parallel Processing with Python

Modern software often needs to do many things at the same time to run faster and scale better. This includes data processing, web services, and machine learning workloads. Understanding parallel and concurrent execution is now an important skill for Python developers.
This session gives a clear and practical introduction to parallel processing in Python. It focuses on the main ideas and shows when and how to use different approaches correctly.

Who is this for?

Students, developers, and anyone who wants to understand how Python programs can run faster by doing work in parallel. This session is useful if you want to speed up Python programs, understand the difference between threads and processes, and build more efficient and scalable applications.

Who is leading the session?

The session is led by Dr. Stelios Sotiriadis, CEO of Warestack and Associate Professor and MSc Programme Director at Birkbeck, University of London.

He works in distributed systems, cloud computing, operating systems, and Python-based data processing. He holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked with Huawei, IBM, Autodesk, and several startups. Since 2018, he has been teaching at Birkbeck and founded Warestack in 2021.
What we will cover

Requirements

A laptop with Python installed (Windows, macOS, or Linux), Visual Studio Code, and Python pip. Lab computers can be used if needed.

Format

This is a hands-on introduction with examples and short exercises. Topics include what concurrency and parallelism mean, threads vs processes in Python, the Global Interpreter Lock explained simply, using threading for I/O-heavy tasks, using multiprocessing for CPU-heavy tasks, basic use of concurrent.futures, common problems like race conditions, and when parallelism is not the right choice.

A 1.5-hour live session with short theory explanations, live coding, and guided exercises. The session runs in person, with streaming available for remote participants.

Prerequisites

Basic to intermediate Python knowledge, including functions, loops, and basic data structures.