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This project serves as a comprehensive educational resource for learning parallel programming and high-performance computing using graphics processing units. It provides technical guidance on the fundamental paradigms required to offload computationally intensive tasks from a host system to specialized hardware accelerators. The materials cover the core methodologies for managing data-parallel operations, including the orchestration of memory between host and device spaces and the organization of threads into structured grids and blocks. It details the execution models necessary to distribute
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
This library is a web-based framework designed for general-purpose parallel processing and high-performance computation on graphics hardware. It provides a toolkit for executing complex mathematical operations, physics simulations, and procedural visualizations directly within the browser by leveraging the graphics processor for data-intensive tasks. The framework distinguishes itself through automated shader management, which ensures consistent execution across diverse hardware and browser environments. It achieves this by transpiling modern shader syntax into legacy versions and automatical
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 main features of srush/gpu-puzzles are: GPU Programming Courses, CUDA Programming Tutorials, GPU Programming Educational Tools, GPU-Accelerated Computation, Parallel Algorithm Training, GPU Architecture Education, Hardware-to-Logic Mapping Exercises, Concurrency Labs.
Open-source alternatives to srush/gpu-puzzles include: packtpublishing/learn-cuda-programming — This project serves as a comprehensive educational resource for learning parallel programming and high-performance… overv/vulkantutorial — VulkanTutorial is a comprehensive educational guide and instructional resource for implementing low-level rendering… infatoshi/cuda-course — This project is a CUDA programming course and technical guide focused on writing and optimizing GPU kernels for… amandaghassaei/gpu-io — This library is a web-based framework designed for general-purpose parallel processing and high-performance… arrayfire/arrayfire — ArrayFire is a hardware-agnostic compute framework and JIT-compiled tensor engine designed for high-performance… gpu-mode/lectures — Material for gpu-mode lectures.