CUDA Programming for Machine Learning: Course Structure

A comprehensive guide for learning CUDA programming with a focus on implementing machine learning models, including Large Language Models (LLMs) and diffusion models in C++.

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1. CUDA Basics (2-3 weeks)

• Introduction to the CUDA programming model
• Understanding thread hierarchy: grids, blocks, threads
• Memory hierarchy and its importance
• Writing and launching basic kernels
• Error handling in CUDA

2. Advanced CUDA Concepts (3-4 weeks)

• Utilizing shared memory and tiling
• Implementing atomic operations
• Working with streams and asynchronous execution
• CUDA events and profiling techniques
• Warp-level primitives for optimization

3. CUDA Optimization Techniques (3-4 weeks)

• Enhancing performance with memory coalescing
• Avoiding bank conflicts and using padding
• Occupancy optimization strategies
• Leveraging loop unrolling and instruction-level parallelism
• Exploring dynamic parallelism

4. CUDA Libraries for Machine Learning (2-3 weeks)

• Working with cuBLAS (Basic Linear Algebra Subprograms)
• Utilizing cuDNN (Deep Neural Networks)
• Random number generation with cuRAND
• Parallel algorithms using the Thrust library

5. Implementing Basic ML Algorithms in CUDA (3-4 weeks)

• Implementing linear regression
• Coding logistic regression models
• Introduction to neural network basics
• Building convolutional neural networks (CNNs)

6. Advanced ML Models in CUDA (4-6 weeks)

• Recurrent Neural Networks (RNNs) implementation
• Long Short-Term Memory (LSTM) networks
• Transformer architecture and its components

7. Large Language Models (LLMs) Implementation (6-8 weeks)

• Exploring attention mechanisms
• Self-attention and multi-head attention concepts
• Implementing transformer blocks
• Understanding tokenization and embedding
• Training and inference optimizations

8. Diffusion Models in CUDA (4-6 weeks)

• The diffusion process and reverse diffusion
• Building a U-Net architecture
• Noise scheduling and sampling techniques

9. C++ Integration and Optimization (4-6 weeks)

• Working with CUDA and C++ templates
• Efficient memory management in C++
• Interfacing CUDA with C++ libraries
• Optimizing C++ code for GPU acceleration

10. Advanced Optimization and Deployment (3-4 weeks)

• Implementing mixed-precision training
• Quantization techniques for model efficiency
• Model parallelism and pipeline parallelism
• Distributed training across multiple GPUs

11. Project Work and Case Studies (4-6 weeks)

• Full implementation of an LLM or diffusion model
• Optimizing models for different GPU architectures
• Benchmarking and profiling the models

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Estimated Total Duration: 6 to 12 months

This curriculum provides a path from CUDA basics to implementing complex models like LLMs and diffusion models. The time estimates are flexible and depend on your experience and learning pace.

Tip: Hands-on coding and experimentation are key throughout this learning journey. Begin with simpler projects and scale up as you progress.

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