This project provides a comprehensive toolset for WebGPU, serving as a graphics API wrapper, compute shader framework, resource manager, and shader toolchain. It enables browser-based GPU acceleration by offloading memory-intensive tasks and data processing from the CPU to the GPU. The framework manages the full lifecycle of GPU operations, from requesting physical hardware adapters and initializing logical devices to configuring programmable render and compute pipelines. It specifically supports the coordination of parallel workgroups and collective subgroup operations for general-purpose co
This repository is a collection of reference implementations and programming examples for the CUDA Toolkit. It serves as a GPGPU implementation guide and a parallel computing reference, providing code for using graphics hardware to perform general-purpose calculations and high-performance parallel processing. The project provides specific samples for GPU kernel development and resource management. These include demonstrations of multi-GPU communication, peer-to-peer memory access, and system hardware inspection to coordinate distributed GPU resources. The codebase covers a wide range of capa
gfx is a hardware-agnostic graphics API abstraction that translates a unified set of graphics and compute commands into native instructions for multiple GPU drivers. It provides a common interface for cross-platform rendering and general-purpose GPU compute programming. The project features an intermediate-representation shader translation system that converts source code and SPIR-V into target-specific languages. It employs a data-driven reference test framework to verify that graphics output remains consistent across different hardware platforms. Capabilities include parallel command buffe
This project is a cross-platform graphics and compute framework that provides a unified, hardware-agnostic abstraction layer for rendering and parallel processing. It enables developers to build high-performance applications that execute consistently across diverse operating systems and hardware backends, including Vulkan, Metal, and DirectX. By mapping high-level graphics commands to native APIs, it serves as a portable foundation for both real-time 3D rendering and general-purpose GPU computing. The framework distinguishes itself through a robust architecture that supports both native deskt
gpu.cpp 是一个轻量级的 C++ 库,用于跨不同硬件供应商和操作系统执行底层通用 GPU 计算。它作为一个便携式 GPU 包装器、内核编排器和张量管理系统,利用 WebGPU 规范来抽象设备初始化、缓冲区传输和计算着色器调度。
The main features of answerdotai/gpu.cpp are: WebGPU Implementations, GPU Hardware Abstraction Layers, Compute Shader Programming, Tensor Lifecycle Management, GPU Compute Frameworks, GPU Resource Management, Compute Shader Dispatchers, Tensor Memory Lifetime Management.
Open-source alternatives to answerdotai/gpu.cpp include: gpuweb/gpuweb — This project provides a comprehensive toolset for WebGPU, serving as a graphics API wrapper, compute shader framework,… nvidia/cuda-samples — This repository is a collection of reference implementations and programming examples for the CUDA Toolkit. It serves… gfx-rs/gfx — gfx is a hardware-agnostic graphics API abstraction that translates a unified set of graphics and compute commands… gfx-rs/wgpu — This project is a cross-platform graphics and compute framework that provides a unified, hardware-agnostic abstraction… rust-gpu/rust-cuda — rust-cuda is a GPU programming framework and device compiler that allows for the development and execution of… nvidia/cuda-python — cuda-python provides low-level Python bindings for the CUDA Driver and Runtime APIs. It serves as a programmatic…