Going Further with CUDA for Python Programmers

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

## 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.

[pinchedsquare]

@Jeremy love you so so much for being such an amazing educator. I am a professional SWE with 15+ years of experience and I find myself learning from every single video of yours. I am sure I and thousand other are grateful for what you are doing. 🙏

[paxdriver]

Very much appreciate the edit for the explanation and demo at ~50:00 👏

[gfickel]

Excelent video! And the explenation of the runtime differences between static and dynamic shared memory at ~ 54:00 was great, specially how to somewhat circumvent it using the template/switch/lambda approach.

[wolpumba]

*Abstract*

This technical talk 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. 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.

The presenter compares pure Python, Python with simulated 'shared
memory', Numba, and raw CUDA implementations. While providing code
examples, the speaker underscores the value of debugging with simpler
models and using Pythonic constructs to simulate CUDA-like concurrency
when possible. They also discuss 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.

In its final segment, the video discusses the evolving role of AI in
software development, compares approaches like Numba and Triton for
CUDA programming, and emphasizes the continued importance of
understanding core CUDA concepts even as increasingly sophisticated
tools emerge.

*Keywords:* CUDA, Python, shared memory, performance optimization,
Numba, ChatGPT


*Chapter Titles*


*I. Introduction to Optimized Matrix Multiplication (**0:00**)*

- 0:00 Introduction
- 1:03 Understanding Shared Memory vs. Global Memory
- 6:21 Pure Python Matrix Multiplication

*II. Shared Memory Techniques for CUDA (**12:04**)*

- 12:04 Tiling for Shared Memory Optimization
- 15:26 CUDA Matrix Multiplication Using Shared Memory
- 15:35 Shared Memory for Tiling Optimization
- 19:23 Shared Memory vs Views in Python

*III. Implementing Shared Memory Optimization in Python (**20:12**)*

- 20:12 Python Implementation of Shared Memory Optimization
- 30:49 Debugging Tip
- 31:08 Code Refactoring
- 31:35 Managing Concurrent Threads
- 34:40 CUDA Execution Model
- 35:14 Python Threading (Simulating CUDA)
- 38:27 Final Kernel Runner (Python)

*IV. Translating Python to CUDA and Performance Considerations (**42:15**)*

- 42:15 ChatGPT: Automated Python to CUDA conversion
- 42:47 CUDA-Specific Syntax
- 45:11 CUDA Code Structure & Shared Memory
- 46:53 CUDA Execution, Compilation, and the Mystery of Dynamic Shared Memory

*V. Numba: Bringing Python and CUDA Together (**55:55**)*

- 55:55 Introducing Numba for Python-Based CUDA
- 59:26 Advantages of Using Numba
- 1:00:38 Numba's CUDA Simulator
- 1:01:36 Optimizing Performance
- 1:02:41 ChatGPT's Capabilities

*VI. The Future of AI in Coding (**1:11:46**)*

- 1:11:46 The Future of Developers and Tools like ChatGPT
- 1:13:46 The Future of AI in Software Development
- 1:13:59 Comparing Numba and Triton
- 1:15:51 The Value of Learning CUDA
- 1:16:47 Additional Notes


*Summary 1/3*

*Introduction*

- *0:00* This video demonstrates advanced CUDA techniques for Python programmers, building upon previous CUDA knowledge.
- *0:34* Focuses on optimizing memory usage with incredibly fast shared memory in CUDA.

*Understanding Shared Memory vs. Global Memory*

- *1:03* Global memory: Default type used in basic CUDA (slower but larger capacity).
- *1:47* Shared memory: Limited to threads within a single block (10x faster than global).
- *2:32* Using shared memory effectively is crucial for optimizing CUDA code execution.

[alinour]

Thank you for the Great video!

Are there any plans for a practical deeplearning for coders for 2024?

[mwaqze]

Hello Jeremy. i would like to inform you that your content is great but your github repo contains typos. Kindly share all of your colab notebooks so we may follow along your youtube lecture notes.
I do not mean to be rude but I have to inform you what's missing.

[derekcarday]

Is Jeremy done with AI and focused on HTML?

[EvanBurnetteMusic]

I'm having trouble with Ninja installation on colab

[philippmuller]

Hi Jeremy, your kaggle notebook from the first lesson (practical deep learning for coders) doesnt work. is your course from fastai outdated or still relevant?

[dangomushi]

why do you write if or for statements in one line frequently? it's not recommended coding style...