gpu.cpp

Introduction to gpu.cpp

gpu.cpp is a remarkable lightweight library designed to simplify the inclusion of GPU compute capabilities within C++ projects. It achieves this by building upon the WebGPU specification, which serves as a universal, low-level interface for interfacing with GPUs. This design choice allows developers to embed GPU code in their C++ projects seamlessly, ensuring compatibility across various hardware platforms equipped with Nvidia, Intel, AMD, and other GPUs. Whether it's sleek laptops, powerful workstations, or versatile mobile devices, as long as Vulkan, Metal, or DirectX is supported, gpu.cpp enables efficient GPU compute.

Technical Goals: Simplicity, Speed, and Minimalism

The primary vision behind gpu.cpp is to provide a high-impact yet low-complexity tool that empowers individual developers and researchers to harness the power of GPU computation using just a standard C++ compiler. Here are the library's core objectives:

• High-efficiency API: Crafting the API to cover diverse GPU compute tasks with minimal complexity.
• Rapid Compilation and Execution: Aiming for project builds that complete in less than five seconds on modern laptops.
• Minimal Dependencies: Relying solely on the standard clang C++ compiler without other external dependencies, except for the WebGPU native implementation.

The design of gpu.cpp ensures that only essential operations are included, allowing developers to perform a wide range of low-level GPU tasks. The library maintains transparency by minimizing abstraction layers, thereby maintaining a clear link to the raw WebGPU API when needed.

In keeping with the theme of rapid prototyping, gpu.cpp ensures that the builds for C++ projects are almost instantaneous, even on basic computers. The core library is lean, with the header-only implementation consisting of approximately 1000 lines of code, facilitating quick compilation and minimal maintenance overhead.

Getting Started: Compiling and Running Projects

To begin working with gpu.cpp, ensure that the following are installed on your system:

• clang++ with C++17 support.
• python3 to run the script for downloading the Dawn shared library.
• make for project build operations.
• On Linux, Vulkan drivers (libvulkan1, mesa-vulkan-drivers, vulkan-tools) are essential if not already installed.

The only requirement for gpu.cpp is a WebGPU implementation. Dawn is currently the supported native backend, with plans for future expansions to other targets and implementations. Once you clone the repository, navigate to the top-level directory and use:

make

The initial build downloads the prebuilt Dawn library, placing it in the third_party/lib directory. The process only happens once, enabling subsequent builds to be rapid.

Practical Example: Implementing a GELU Kernel

To understand gpu.cpp in action, consider a simple use case involving the Gaussian Error Linear Unit (GELU) function, widely used in neural networks. This example demonstrates GPU computation setup using gpu.cpp, covering:

1. GPU-side code in WebGPU Shading Language (WGSL) for the compute operation.
2. CPU-side C++ code for resource preparation and allocation.
3. CPU-side C++ code for dispatching GPU computation and handling results.

The example highlights how gpu.cpp serves as a toolkit, providing essential GPU components (“nouns”) and actions (“verbs”) to manage GPU resources and operations efficiently. From setting up the GPU device context to creating tensors and dispatching kernel operations, gpu.cpp streamlines the entire process.

Additional Examples: Matrix Multiplications and More

The examples/ directory showcases various projects illustrating gpu.cpp’s capabilities. They include:

• Tiled Matrix Multiplication in examples/matmul.
• Physics Simulations for ensemble simulations of double pendulums in examples/physics.
• Signed Distance Function (SDF) Rendering in examples/render and examples/shadertui.

These examples offer insight into more complex use cases and demonstrate gpu.cpp's versatility for handling compute-intensive tasks.

Who Benefits from gpu.cpp?

gpu.cpp is designed for developers intending to implement GPU algorithms on various personal computing devices. Whether for standalone implementations of neural networks, physics simulations, multimedia applications, or complex data processing, gpu.cpp offers portability and simplicity in a traditionally complex domain.

Boundaries and Future Outlook

As a library in continuous development, gpu.cpp plans to refine its API, expand browser compatibility, and eventually create a reusable kernel library. By iterating on use cases and gathering community input, gpu.cpp aims to further simplify GPU computation and broaden its utility beyond specialized developer circles.

Join the community discussions on Discord or explore contributing to the project to help shape its future evolution.