Welcome to CUDA Technical Notes! This project is an ongoing endeavor aimed at providing comprehensive technical notes and tutorials for learning CUDA programming. From basic thread operations to advanced topics like multi-GPU cooperation, this repository covers various aspects of CUDA development, including cuBLAS, cuFFT, cuDNN, and more.

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA. It allows developers to harness the power of NVIDIA GPUs (Graphics Processing Units) for general-purpose processing, significantly accelerating computational tasks. CUDA Technical Notes serves as a valuable resource for individuals looking to deepen their understanding of CUDA programming and unlock the potential of GPU-accelerated computing.

Explore the fundamental concepts of CUDA programming, covering thread management, memory allocation, kernel invocation, and synchronization. Gain a solid foundation in GPU parallelism and learn essential techniques for optimizing CUDA applications.

Dive into the cuBLAS library, which provides optimized routines for basic linear algebra operations on NVIDIA GPUs. Discover how to leverage cuBLAS to accelerate matrix and vector computations in scientific and engineering applications.

Learn about the cuFFT library, offering efficient implementations of Fast Fourier Transform (FFT) algorithms on GPU architectures. Explore how cuFFT can be utilized for signal and image processing tasks requiring FFT computations.

Explore the capabilities of cuDNN, a GPU-accelerated library for deep neural network operations. Understand how cuDNN facilitates high-performance training and inference for deep learning models, including convolutional neural networks (CNNs).

Discover advanced techniques for leveraging multiple GPUs in parallel computing tasks. Learn about workload distribution, data synchronization, and communication strategies for achieving scalable performance across multiple GPU devices.

To get started with CUDA Technical Notes, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/patricxu/cuda-technical-notes.git

2. Explore the sections by navigating to the respective directories.

3. Read the README file in each section for detailed explanations, code examples, and best practices.

4. Experiment with the provided code samples, modify them according to your requirements, and run them on your CUDA-enabled GPU.

Contributions to CUDA Technical Notes are highly encouraged! Whether you want to fix a typo, improve existing content, or add new sections, your contributions are valuable in enhancing the learning experience for others. Feel free to submit pull requests or open issues on GitHub to contribute to the project.

CUDA Technical Notes is licensed under the MIT License, allowing for both personal and commercial use with proper attribution.

https://docs.nvidia.com/cuda/cuda-c-programming-guide

https://docs.nvidia.com/cuda/cuda-c-best-practices-guide