EasyCuda is a repository that aims to provide learning resources for programming GPUs using CUDA C/C++. It contains multiple courses that cover the basics of CUDA and various advanced topics.
CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA for performing general-purpose computing on GPUs. It allows developers to exploit the performance benefits of GPUs for computationally intensive tasks, such as machine learning, scientific simulations, and video rendering.
Learning CUDA can be challenging, especially if you're new to parallel programming or GPU architecture. The goal of EasyCuda is to provide a comprehensive learning experience that helps you understand the basics of CUDA and build your skills through various practical examples and projects.
EasyCuda contains multiple courses that cover different aspects of CUDA programming. Here's an overview of the available courses:
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Deep Learning Institute Nvidia - composing with multiple courses like: Fundamental CUDA programming with C/C++, Concurrent Streams, and Multi-GPUs scaling.
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CUDA for Engineers: An Introduction to High-Performance Parallel Computing - Covering topics like Reduction or Atomic Functions, 3D data.
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Programming in Parallel with CUDA: Pratical Guide - Covering hard topics such as Warps and Cooperative Groups, Tensor Cores, Profiling.
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CUDA Projects - This course provides hands-on projects that allow you to apply your CUDA skills to real-world problems. You'll learn how to optimize image processing algorithms, implement machine learning models, and perform parallel simulations.
To get started with EasyCuda, follow these steps:
- Clone the repository to your local machine.
- Choose a course that you're interested in and navigate to the course folder.
- Read the readme file in the course folder to understand the requirements and objectives.
- Follow the instructions in the readme file to complete the exercises.
- Repeat steps 3 to 4 for each course that you want to take.
In addition to the course materials, EasyCuda also contains a collection of useful links and resources in the "Resources.md" file. You can use this file to find additional learning materials, such as books, tutorials, and online courses.
If you want to contribute to EasyCuda, please follow these guidelines:
- Fork the repository and make your changes on your local machine.
- Create a new branch for your changes and use a descriptive name.
- Write clear commit messages that describe your changes.
- Make sure your changes don't break any existing code or tests.
- Push your changes to your forked repository and create a new pull request.
EasyCuda is a valuable resource for anyone who wants to learn CUDA programming or improve their existing skills. With its comprehensive courses, practical examples, and useful resources, you'll be able to master GPU programming and boost your productivity.