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

This is lecture 3 of the "CUDA Mode" series (but you don't need to watch the others first). The notebook is available in the lecture3 folder here: https://github.com/cuda-mode/lectures . Or access it directly via Colab here: https://colab.research.google.com/dri...

Here's a link to the thread that shows how to install CUDA on Linux or WSL: Twitter: 1697435241152127369

GPT4 auto-generated summary

In this comprehensive video tutorial, Jeremy Howard from answer.ai demystifies the process of programming NVIDIA GPUs using CUDA, and simplifies the perceived complexities of CUDA programming. Jeremy emphasizes the accessibility of CUDA, especially when combined with PyTorch's capabilities, allowing for programming directly in notebooks rather than traditional compilers and terminals. To make CUDA more approachable to Python programmers, Jeremy shows step by step how to start with Python implementations, and then convert them largely automatically to CUDA. This approach, he argues, simplifies debugging and development.

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.

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• #deep_learning
• #fastai