Demystifying Parallel Programming in Python

Understanding the Hardware Basics

• A gentle introduction to modern processors: What are CPUs, GPUs, TPUs, and quantum processors?
• Essential terminology explained: cores, hyper-threading, cache memory, multithreading, multiprocessing, multitasking, SIMD, NUMA, and more—no prior knowledge required!

Parallel Programming Techniques for Beginners

A practical overview of Python’s parallel programming tools, organized by approach:

• Just-In-Time (JIT) compilation: Speed up your code without changing your workflow
• Multithreading: Do more at once, and removing the GIL with Python 3.13+
• Multiprocessing: Use all your CPU cores
• Distributed computing: Scale your code across multiple machines
• Quantum programming: A first look at the future of computing

Hands-On Examples

• JIT compilation made easy: PyPy, Numba, and JAX
• The GIL and Python 3.13: What’s changing and why it matters
• Distributed computing for everyone: Celery and Dask on HPC clusters
• GPU computing for beginners: CuPy, cuDF, and Numba
• Your first quantum “Hello World”: A taste of the quantum revolution

Conclusion

By the end of this talk, you’ll have a clear map of Python’s parallel programming landscape. No experience needed—just bring your curiosity and let’s explore together!