Going Further with CUDA for Python Programmers
Channel: Jeremy Howard
Date: 2024-02-12

This technical talk by Jeremy Howard explores advanced programming techniques for maximizing performance when using CUDA with Python. The focus is on optimizing memory usage with a specific emphasis on effectively leveraging fast shared memory in CUDA. It assumes you have already watched this "Getting Started" video: https://youtu.be/nOxKexn3iBo

The video begins with foundational concepts by comparing shared memory to global memory and demonstrates strategies like tiling to address shared memory capacity limitations. It demonstrates core ideas through a matrix multiplication example.

Jeremy compares pure Python, Python with simulated 'shared memory', Numba, and raw CUDA implementations, using ChatGPT for guided code conversion. While initial Numba-based code may exhibit some overhead, it serves as a fast development pathway compared to raw CUDA.

## Resources

The notebook for this lesson is in the "lecture5" folder at: https://github.com/cuda-mode/lectures. Special thanks to Kerem Turgutlu for help preparing it.

See also this video for more information about GPU memory optimisation: https://www.youtube.com/watch?v=lTmYrKwjSOU .

## Timestamps

- 0:00 Introduction to Optimized Matrix Multiplication
- 12:04 Shared Memory Techniques for CUDA
- 20:12 Implementing Shared Memory Optimization in Python
- 42:15 Translating Python to CUDA and Performance Considerations
- 55:55 Numba: Bringing Python and CUDA Together
- 1:11:46 The Future of AI in Coding

Thanks to @wolpumba4099 for initial summary and timestamps.