GPU-Puzzles — Interactive CUDA Learning through Puzzles

Overview

GPU-Puzzles is an open-source project designed to teach beginner GPU programming through interactive coding exercises. It leverages Jupyter Notebooks and the NUMBA library to map Python code directly to CUDA kernels, providing a hands-on approach to learning GPU programming. This project stands out for its practical, puzzle-based learning method that helps users gain intuition about GPU architectures, which are critical for machine learning and deep learning applications.

Key Features

Interactive Learning: Engages users in coding and building GPU kernels directly within Jupyter Notebooks.
NUMBA Integration: Allows Python code to be executed as CUDA kernels, simplifying the learning curve for GPU programming.
Puzzle-Based Approach: Provides a series of puzzles that incrementally build understanding of GPU programming concepts.
Colab Compatibility: Recommended to be used with Google Colab for easy setup and GPU access.

Use Cases

Education: Educators can use GPU-Puzzles to teach students the fundamentals of GPU programming in a practical and engaging way.
Self-Learning: Individuals interested in machine learning and deep learning can use this project to learn GPU programming at their own pace.
Skill Development: Professionals looking to enhance their skills in GPU programming can use GPU-Puzzles to gain hands-on experience and deepen their understanding.

Advantages

Interactive and Engaging: The puzzle format makes learning GPU programming more engaging and less intimidating.
Quick Start: Users can get started quickly, especially with the integration of Google Colab.
Practical Application: The project focuses on real-world applications, helping users understand the algorithms that power most deep learning models today.

Limitations / Considerations

Complexity: While the project aims to simplify GPU learning, it still requires a basic understanding of Python and some familiarity with programming concepts.
Hardware Requirements: To fully utilize the project, access to a GPU is necessary, which might not be available to all users.

Tensor-Puzzles: A similar project by the same author, focusing on PyTorch puzzles for learning deep learning concepts.
CUDA Toolkit: A comprehensive suite of tools for developing GPU-accelerated applications, which is more complex and not as beginner-friendly as GPU-Puzzles.
OpenCL: An open standard for parallel programming of heterogeneous systems, which is an alternative to CUDA but has a different focus and learning curve.

Basic Information

GitHub: GPU-Puzzles
Stars: 11,478
License: Unknown
Last Commit: 2025-09-18

📊 Project Information

Project Name: GPU-Puzzles
GitHub URL: https://github.com/srush/GPU-Puzzles
Programming Language: Jupyter Notebook
⭐ Stars: 11,478
🍴 Forks: 876
📅 Created: 2022-07-11
🔄 Last Updated: 2025-09-18

🏷️ Project Topics

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📚 Documentation

NUMBA CUDA Guide

🎥 Video Tutorials

Walkthrough Guide

This article is automatically generated by AI based on GitHub project information and README content analysis

GPU-Puzzles

Project Description