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CUDA for AI — Intuitively and Exhaustively Explained

🌈 Abstract

The article discusses how to train an AI model on a GPU using CUDA, implementing AI from scratch with virtually no prior knowledge. It covers the core components of modern computers, the GPU and its advantages for AI, an introduction to CUDA programming, and the implementation of a neural network in CUDA from the ground up, including a loss function, activation functions, and a full training pipeline.

🙋 Q&A

[01] Core Components of Modern Computers

1. What are the key components of a modern computer that are relevant for the article? The key components discussed are:

  • Motherboard: The backbone that allows components to communicate
  • CPU: The central processing unit responsible for executing calculations
  • RAM: The working memory of the CPU
  • GPU: The graphics processing unit designed to help the CPU on certain calculations

2. How do the CPU and GPU differ in their approach to computation? The CPU is designed to optimize single calculations and run a program as quickly as possible, while the GPU is designed to optimize running many calculations in parallel, even if each individual calculation is slower than the CPU.

3. Why is the GPU useful for AI applications? AI models involve a lot of simple, independent calculations, which is a perfect use case for the parallel processing capabilities of the GPU.

[02] CUDA Programming

1. What is CUDA and how does it allow programming applications that leverage both the CPU and GPU? CUDA is NVIDIA's parallel computing platform that allows building applications that run on the CPU and can interface with the GPU to do parallel math. It provides a set of tools, primarily in C++, to program the GPU.

2. How are CUDA kernels organized and launched? CUDA kernels are functions that run many times in parallel across the GPU. They are organized into "thread blocks" which can have up to 1024 threads, and multiple thread blocks can be launched at once on the GPU.

3. How is data transferred between the CPU (host) and GPU (device) memory? Data is transferred using functions like cudaMalloc to allocate memory on the device, cudaMemcpy to copy data between host and device, and cudaFree to free device memory.

[03] Implementing a Neural Network in CUDA

1. What are the key components implemented to build the neural network? The key components implemented are:

  • A base NNLayer class that defines the forward and backpropagation functionality
  • A LinearLayer that implements the linear transformation of a neural network layer
  • Activation functions like ReLUActivation and SigmoidActivation
  • A BCECost class that implements the binary cross-entropy loss function and its derivative

2. How is the neural network model defined and trained? The NeuralNetwork class defines the model architecture by adding the various layers. The training process involves iterating through batches of data, performing forward propagation, calculating the loss, and then backpropagating the gradients to update the model parameters.

3. How is the dataset of 2D points and their labels generated and used to train the model? The CoordinatesDataset class generates random 2D points and labels them as 0 or 1 based on which quadrant they fall in. This dataset is then used to train the neural network to predict the quadrant of a given 2D point.

Shared by Daniel Chen ·
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