The main goal of this tutorial is to give a broader audience an overview of how high-performance libraries in Python are developed. Attendees will work with ready-made code templates and fill in missing parts to learn by doing.

Session Outline

• (5 min) Motivation: Problems with performance in Python and what to do with them
• (10 min) Understanding Linear Regression & Normal Equations: A brief introduction to the mathematical background, deriving the formulas that would be used for training
• (10 min) Implementing a Python Baseline: Writing a reference implementation on Python
• (20 min) Optimizing with Cython: Implementing a fast Cython-based kernel
• (20 min) Example of pybind11 use: Example of pybind11 usage for integrating kernels implemented in C++
• (15 min) Performance Comparison: Benchmarking Python vs. Cython vs. C++ & pybind11 implementations
• (10 min) Q&A and Discussion

By the end of this session, attendees will:

• Understand how Python libraries achieve high performance using Cython and C++
• Learn how to write and integrate custom Cython or C++ kernels
• Know when to optimize Python code and when to rely on existing libraries

We also hope that this tutorial will inspire more people to contribute to open-source high-performance libraries.