Getting Started With CUDA for Python Programmers

I used to find writing CUDA code rather terrifying. But then I discovered a couple of tricks that actually make it quite accessible. In this video I introduce CUDA in a way that will be accessible to Python folks, & I even show how to do it all for free in Colab!

## Notebooks

## GPT4 auto-generated summary

The tutorial is structured in a hands-on manner, encouraging viewers to follow along in a Colab notebook. Jeremy uses practical examples, starting with converting an RGB image to grayscale using CUDA, demonstrating the process step-by-step. He further explains the memory layout in GPUs, emphasizing the differences from CPU memory structures, and introduces key CUDA concepts like streaming multi-processors and CUDA cores.

Jeremy then delves into more advanced topics, such as matrix multiplication, a critical operation in deep learning. He demonstrates how to implement matrix multiplication in Python first and then translates it to CUDA, highlighting the significant performance gains achievable with GPU programming. The tutorial also covers CUDA's intricacies, such as shared memory, thread blocks, and optimizing CUDA kernels.

The tutorial also includes a section on setting up the CUDA environment on various systems using Conda, making it accessible for a wide range of users.

## Timestamps

- 00:00 Introduction to CUDA Programming
- 00:32 Setting Up the Environment
- 01:43 Recommended Learning Resources
- 02:39 Starting the Exercise
- 03:26 Image Processing Exercise
- 06:08 Converting RGB to Grayscale
- 07:50 Understanding Image Flattening
- 11:04 Executing the Grayscale Conversion
- 12:41 Performance Issues and Introduction to CUDA Cores
- 14:46 Understanding Cuda and Parallel Processing
- 16:23 Simulating Cuda with Python
- 19:04 The Structure of Cuda Kernels and Memory Management
- 21:42 Optimizing Cuda Performance with Blocks and Threads
- 24:16 Utilizing Cuda's Advanced Features for Speed
- 26:15 Setting Up Cuda for Development and Debugging
- 27:28 Compiling and Using Cuda Code with PyTorch
- 28:51 Including Necessary Components and Defining Macros
- 29:45 Ceiling Division Function
- 30:10 Writing the CUDA Kernel
- 32:19 Handling Data Types and Arrays in C
- 33:42 Defining the Kernel and Calling Conventions
- 35:49 Passing Arguments to the Kernel
- 36:49 Creating the Output Tensor
- 38:11 Error Checking and Returning the Tensor
- 39:01 Compiling and Linking the Code
- 40:06 Examining the Compiled Module and Running the Kernel
- 42:57 Cuda Synchronization and Debugging
- 43:27 Python to Cuda Development Approach
- 44:54 Introduction to Matrix Multiplication
- 46:57 Implementing Matrix Multiplication in Python
- 50:39 Parallelizing Matrix Multiplication with Cuda
- 51:50 Utilizing Blocks and Threads in Cuda
- 58:21 Kernel Execution and Output
- 58:28 Introduction to Matrix Multiplication with CUDA
- 1:00:01 Executing the 2D Block Kernel
- 1:00:51 Optimizing CPU Matrix Multiplication
- 1:02:35 Conversion to CUDA and Performance Comparison
- 1:07:50 Advantages of Shared Memory and Further Optimizations
- 1:08:42 Flexibility of Block and Thread Dimensions
- 1:10:48 Encouragement and Importance of Learning CUDA
- 1:12:30 Setting Up CUDA on Local Machines
- 1:12:59 Introduction to Conda and its Utility
- 1:14:00 Setting Up Conda
- 1:14:32 Configuring Cuda and PyTorch with Conda
- 1:15:35 Conda's Improvements and Compatibility
- 1:16:05 Benefits of Using Conda for Development
- 1:16:40 Conclusion and Next Steps

Thanks to @wolpumba4099 for the chapter timestamps. Summary description provided by GPT4.

[wadejohnson]

Jeremy Howard: a true hero of the common man. Thank you for this.

[wolpumba]

Chapter titles:

- 00:01 Introduction to CUDA Programming
- 00:32 Setting Up the Environment
- 01:43 Recommended Learning Resources
- 02:39 Starting the Exercise
- 03:26 Image Processing Exercise
- 06:08 Converting RGB to Grayscale
- 07:50 Understanding Image Flattening
- 11:04 Executing the Grayscale Conversion
- 12:41 Performance Issues and Introduction to CUDA Cores
- 14:46 Understanding Cuda and Parallel Processing
- 16:23 Simulating Cuda with Python
- 19:04 The Structure of Cuda Kernels and Memory Management
- 21:42 Optimizing Cuda Performance with Blocks and Threads
- 24:16 Utilizing Cuda's Advanced Features for Speed
- 26:15 Setting Up Cuda for Development and Debugging
- 27:28 Compiling and Using Cuda Code with PyTorch
- 28:51 Including Necessary Components and Defining Macros
- 29:45 Ceiling Division Function
- 30:10 Writing the CUDA Kernel
- 32:19 Handling Data Types and Arrays in C
- 33:42 Defining the Kernel and Calling Conventions
- 35:49 Passing Arguments to the Kernel
- 36:49 Creating the Output Tensor
- 38:11 Error Checking and Returning the Tensor
- 39:01 Compiling and Linking the Code
- 40:06 Examining the Compiled Module and Running the Kernel
- 42:57 Cuda Synchronization and Debugging
- 43:27 Python to Cuda Development Approach
- 44:54 Introduction to Matrix Multiplication
- 46:57 Implementing Matrix Multiplication in Python
- 50:39 Parallelizing Matrix Multiplication with Cuda
- 51:50 Utilizing Blocks and Threads in Cuda
- 58:21 Kernel Execution and Output
- 58:28 Introduction to Matrix Multiplication with CUDA
- 1:00:01 Executing the 2D Block Kernel
- 1:00:51 Optimizing CPU Matrix Multiplication
- 1:02:35 Conversion to CUDA and Performance Comparison
- 1:07:50 Advantages of Shared Memory and Further Optimizations
- 1:08:42 Flexibility of Block and Thread Dimensions
- 1:10:48 Encouragement and Importance of Learning CUDA
- 1:12:30 Setting Up CUDA on Local Machines
- 1:12:59 Introduction to Conda and its Utility
- 1:14:00 Setting Up Conda
- 1:14:32 Configuring Cuda and PyTorch with Conda
- 1:15:35 Conda's Improvements and Compatibility
- 1:16:05 Benefits of Using Conda for Development
- 1:16:40 Conclusion and Next Steps

I think youtube kind of shadow banned me and I can't post Summary 1/2

[boydrh]

I ran this notebook on a Jetson Nano DevKit (from 2015) and it took 6 seconds for the CPU greyscale conversion and 8ms for the CUDA Kernel. This was a really cool tutorial!!

[ilia_zaitsev]

I am following works that Jeremy Howard publishes for a while, starting when the fastai library used Keras. And since that time, each year or two, great content is published, new ideas shared, new projects started. It is CUDA time! (Always wanted to learn; never had a good starting point.) No doubt, a true pillar of the Machine Learning community :)

[dahiruibrahimdahiru]

What better way to spend a Sunday than a Jeremy Howard video

[AmputeerMeneer]

Quite brilliant to do this in a notebook because it avoid the normal hassle of setting up a CUDA environment. Even if you have your own GPU, setting up CUDA can be a real pain (eg getting the versions right). Well done Jeremy!

[markozege]

This is amazing, thank you Jeremy! So happy you are continuing with making educational videos. And thanks to all 'Cuda Mode' folks as well...

[oceanograf]

Amazing, thank you for taking the time to put this stuff out, Jeremy, despite doing for-profit work right now!

[JustSayin]

30:24 is why I love this channel. Why learn low-level GPU programming when ChatGPT can do it for us? A no-fuss, genuinekly useful tutorial. thank you Jeremy.

[mochalatte]

Outstanding - Your work always impresses me and part of me tells me you are indeed a great teacher.

[Kwolf]

I love that magic is open-source, thanks, Jeremy!

[AM-ykyd]

Really interesting approach to use python for prototyping CUDA.
Translation back to C++ without chatgpt can probably by automated using AST traversal (as if trl and torchscript is not enough) as number of available operations is self-limited.

[JaySingh-gvrm]

Wow...thanks for this Jeremy. Yet to complete this video but I know, as always it will be awesome

[pkn]

Until and unless, educators like Jeremy are present, no closed source company can have a lock on knowledge.
Thanks for doing what you do, so consistently.

One question though, even though there's so much chaos in education field, what motivates you to do it consistently? Doing great is okay, doing great consistently is really hard in this distraction prone world.
Anyways as always Thank you and your team for your contribution

[letrillion]

Been looking for something like this for so long

[sayakpaul]

Who would have thought writing CUDA kernels like this!?

[alinour]

Thank you for the amazing tutorial.

Is it possible in the future, when mojo is released, to recreate this tutorial using it?

[godiswatching_]

Thanks as usual for the great video. Also I see you got a new camera haha :]

[KiejlAArmistice]

Thanks for the excellect course! Very helpful

[JonathanEyre]

If chatGPT can convert Python to C code, then surely it must be possible to write a notebook plugin (or whatever) in Python that takes a Python cell and creates an adjacent cell in CUDA C so the process is automated, thus allowing everyone to code in Python with its attendant advantages for the GPU native target. This is exactly like the old days of writing code in C and using a cross compiler to generate Motorola assembler code for burning EPROM chips.