CUDA Programming Tutorials

Welcome to the CUDA Programming Tutorials repository! 🎉 This repository is designed to help developers, students, and enthusiasts learn NVIDIA CUDA programming through well-structured and practical examples. Each tutorial focuses on specific topics in CUDA, ranging from basic concepts to advanced GPU programming techniques.

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Purpose

The primary goal of this repository is to provide an organized and comprehensive collection of CUDA programming examples and tutorials. By exploring this repository, you will:

• Gain a solid understanding of GPU programming using CUDA.
• Learn to optimize code for parallel execution on NVIDIA GPUs.
• Explore real-world CUDA applications in fields like image processing, numerical computation, and neural networks.
• Develop skills to write efficient, reusable, and modular CUDA code.
• Benchmark and debug CUDA kernels effectively.

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Who Is This For?

This repository is suitable for:

• Beginners: Who are just starting with CUDA and want to learn the basics of GPU programming.
• Intermediate Programmers: Looking to enhance their knowledge with advanced topics like shared memory, multi-GPU programming, and dynamic parallelism.
• Experienced Developers: Seeking optimization techniques and best practices for high-performance computing.

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What’s Inside?

The repository is divided into several sections, each covering a specific topic in CUDA programming. Below is an overview of the tutorials:

1. Getting Started

• Introduction to CUDA programming.
• Setting up the environment for CUDA development.
• Your first CUDA kernel: "Hello, World!" on the GPU.

2. CUDA Basics

• Understanding threads, blocks, and grids.
• Memory management in CUDA: cudaMalloc, cudaMemcpy, and unified memory.
• Synchronization techniques: __syncthreads().

3. Advanced Topics

• Data parallelism: Vector addition, matrix multiplication, and parallel reduction.
• Memory optimization: Shared memory, coalesced access, and atomic operations.
• Multi-GPU programming: Distributing workloads and peer-to-peer memory access.

4. Real-World Applications

• Image processing: Grayscale conversion and Sobel filters.
• Numerical computations: Solving linear equations with Jacobi iteration.
• Simulations: Particle movement in 3D space.
• Neural networks: Training simple neural networks with CUDA.

5. Debugging and Profiling

• Debugging CUDA kernels with cuda-memcheck.
• Benchmarking kernel performance with cudaEvent API.

6. Python for CUDA

• Using PyCUDA for rapid development.
• Matrix multiplication and memory management with Python.
• Integration with Python libraries like NumPy and Matplotlib.

7. Custom CUDA Libraries

• Writing reusable CUDA functions.
• Debugging custom CUDA libraries.

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How to Use This Repository

1. Clone the repository:

   git clone https://github.com/your-username/cuda-programming-tutorials.git
   cd cuda-programming-tutorials

2. Compile and run examples:

   • For C++ examples:

     nvcc vector_addition.cu -o vector_addition
     ./vector_addition

   • For Python examples:

     python3 vector_addition.py

3. Explore comments and explanations in each example to understand the code.

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Resources

To supplement these tutorials, you may find the following resources helpful:

• NVIDIA CUDA Toolkit Documentation
• CUDA Programming Guide
• PyCUDA Documentation
• cuBLAS and cuFFT Libraries

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License

This repository is licensed under the MIT License. See the LICENSE file for details.