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GPU-Puzzles is an interactive learning environment and tutorial designed for mastering CUDA GPU kernel development. It serves as an educational tool and lab where users solve coding puzzles to understand how to map high-level logic to low-level GPU hardware instructions. The platform focuses on teaching parallel computing concepts and GPU architecture. Users practice developing parallel algorithms and managing GPU memory through a series of hands-on challenges. The environment utilizes a bridge between Python and CUDA to execute kernels and provide real-time feedback by validating outputs ag
This project is a CUDA programming course and technical guide focused on writing and optimizing GPU kernels for hardware acceleration. It provides structured learning resources for using the CUDA platform to execute operations on silicon architectures. The material covers the optimization of linear algebra kernels and the analysis of machine learning deployment. It includes guidance on identifying acceleration tools, mapping the deep learning ecosystem, and evaluating the frameworks used to move models from research to production environments. The scope extends to GPU performance optimizatio
VulkanTutorial is a comprehensive educational guide and instructional resource for implementing low-level rendering and compute pipelines using the Vulkan API. It serves as a GPU programming course and a step-by-step guide for building high-performance graphics applications from scratch. The project provides detailed instruction on the full graphics pipeline, including the compilation of shaders to SPIR-V bytecode, the configuration of rasterization states, and the implementation of 3D graphics pipelines. It also covers general-purpose GPU compute programming, focusing on the execution of par
NYU-DLSP20 is a self-paced deep learning course repository that provides a complete educational curriculum covering supervised and unsupervised deep learning fundamentals. The course materials include lecture slides, Jupyter notebooks, and YouTube video recordings, all organized around PyTorch-based code exercises and neural network architecture tutorials. The course is structured as a sequential progression from fundamentals to advanced architectures, with each lecture building on previous material. Assignments are distributed as Jupyter notebooks that students complete and submit, ensuring
Material for gpu-mode lectures
The main features of gpu-mode/lectures are: Expert Lecture Series, Topic-Based Learning Paths, GPU Lecture Materials, GPU Programming Courses, Embedded Notebook Executions, Lecture-Notebook Pairs, Recorded Lecture Architectures, Technical Talks.
Open-source alternatives to gpu-mode/lectures include: srush/gpu-puzzles — GPU-Puzzles is an interactive learning environment and tutorial designed for mastering CUDA GPU kernel development. It… infatoshi/cuda-course — This project is a CUDA programming course and technical guide focused on writing and optimizing GPU kernels for… overv/vulkantutorial — VulkanTutorial is a comprehensive educational guide and instructional resource for implementing low-level rendering… paulirish/lite-youtube-embed — A faster youtube embed. datastacktv/data-engineer-roadmap — This project is a collection of specialized study guides and roadmaps centered on computer science, data engineering,… atcold/nyu-dlsp20 — NYU-DLSP20 is a self-paced deep learning course repository that provides a complete educational curriculum covering…