GPU Puzzles
Project Description
This project is an interactive tutorial for learning beginner GPU programming using NUMBA, which maps Python code to CUDA kernels. It aims to teach the fundamentals of GPU programming and the core algorithms used in deep learning through a series of coding puzzles.
Project About
The project is about teaching GPU programming concepts in an interactive and practical way. It focuses on writing CUDA kernels using a Python-like syntax provided by NUMBA.
Problem Solved
The project addresses the difficulty of learning GPU programming by providing a hands-on, puzzle-based approach. It bridges the gap between abstract machine learning concepts and low-level GPU code, enabling users to gain intuition about GPU architectures and kernel development.
Features
- Interactive coding puzzles.
- Uses NUMBA for Python-like CUDA kernel development.
- Covers fundamental GPU programming concepts like threads, blocks, shared memory, and synchronization.
- Includes puzzles on common operations like map, zip, broadcast, pooling, dot product, convolution, prefix sum, and matrix multiplication.
- Designed for use in Google Colab with GPU acceleration.
Technologies Used
- NUMBA: A just-in-time compiler for Python that supports CUDA GPU programming.
- CUDA: A parallel computing platform and programming model developed by NVIDIA.
- Python: The primary programming language used for the puzzles and NUMBA integration.
- Numpy
- Google Colab: The recommended environment for running the interactive puzzles.
Benefits
- Provides a practical, hands-on approach to learning GPU programming.
- Simplifies CUDA kernel development with Python-like syntax.
- Builds intuition for GPU architectures and parallel processing.
- Covers essential algorithms used in deep learning.
- Easy to get started with using Google Colab.
Use Cases
- Learning the basics of GPU programming.
- Understanding the underlying principles of CUDA kernels.
- Gaining practical experience in implementing common deep learning operations on GPUs.
- Educational tool for students and researchers in machine learning and high-performance computing.
