Awesome GitHub RepositoriesGPU Programming Courses

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

Material for gpu-mode lectures

Shader School هي منصة تعليمية تفاعلية ودورة برمجة رسومات مصممة لإتقان تطوير تظليل GLSL. توفر منهجاً منظماً لتعلم برمجة GPU والأسس التقنية للتظليلات القابلة للبرمجة من خلال سلسلة من تمارين البرمجة القائمة على المتصفح. تعمل المنصة كصندوق رمل تفاعلي للتظليل ودرس تعليمي لرسومات WebGL، مما يسمح للمستخدمين بكتابة واختبار الكود داخل واجهة في الوقت الفعلي. توفر هذه البيئة ملاحظات مرئية فورية، مما يتيح ممارسة تطوير الرسومات التفاعلية وورش عمل التظليل القائمة على الويب دون الحاجة إلى إعداد تطوير محلي.

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

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