How to run the WebGPU compute rasterizer on my local machine?

Clone the repo, run `npm install` and `npm run dev`, then open localhost:3000 in a WebGPU-supported browser like Chrome Canary. You'll need Node.js installed and may need to enable WebGPU flags in your browser settings.

WebGPU compute rasterizer vs traditional WebGL for learning graphics?

The compute rasterizer teaches low-level compute shader programming and custom pipelines, while WebGL focuses on standard graphics APIs. For foundational GPU concepts, this project is better; for quick application building, WebGL libraries like three.js are preferable.

Can I add textures or lighting to this rasterizer?

No, the current code only supports distance-based shading. Adding textures or advanced lighting requires significant modifications to the compute shaders and buffer management, which isn't covered in the basic guides.

What types of glTF models does it support?

It loads triangle lists from very basic glTF files, meaning it handles simple geometry but not features like animations, materials, or complex node hierarchies—check the code for supported attributes.

Is this fast enough for games or real-time apps?

Likely not; as an educational tool, it's unoptimized and compute-based rasterization is generally slower than hardware-accelerated methods. For performance-critical applications, use traditional graphics pipelines.

How to modify the shading algorithm in this project?

Edit the compute shader files (e.g., for distance-based shading) to implement custom logic; the project's minimal structure makes it a starting point, but you'll need WebGPU and shader programming knowledge.

WebGPU compute rasterizer or shadertoy for learning shaders?

For compute shaders specifically, this project offers a WebGPU-focused, rasterization-based approach, while Shadertoy is better for fragment shaders and creative coding. Choose based on whether you want to learn WebGPU compute or GLSL graphics.

Open-Awesome

Build a compute rasterizer in WebGPU

MITJavaScript

A software rasterizer implemented with WebGPU compute shaders for educational exploration of compute-based rendering.

GitHub

266 stars9 forks0 contributors

What is Build a compute rasterizer in WebGPU?

WebGPU Compute Rasterizer is a software rasterizer that runs on WebGPU compute shaders, designed to render 3D models by processing triangle data entirely through compute operations. It solves the problem of understanding how rasterization can be implemented without traditional graphics pipelines, providing a hands-on educational tool for GPU programming.

Target Audience

Graphics programmers, students, and developers interested in learning WebGPU, compute shaders, or alternative rendering techniques beyond standard graphics APIs.

Value Proposition

Developers choose this project because it offers a clear, minimal implementation of compute-based rasterization, making advanced GPU concepts accessible for experimentation and learning in a modern WebGPU context.

Overview

A simple software rasterizer running on a WebGPU compute shader. Built for educational purposes.

Use Cases

Best For

Learning WebGPU compute shader fundamentals
Experimenting with software rasterization techniques
Understanding compute-based rendering pipelines
Educational projects on GPU programming
Prototyping custom rendering approaches with WebGPU
Exploring alternatives to traditional graphics APIs for rasterization

Not Ideal For

Production applications requiring high-performance, real-time 3D rendering with advanced effects
Projects needing textured models, complex lighting, or post-processing beyond basic shading
Teams looking for a drop-in rendering library without deep compute shader expertise
Environments without WebGPU support or where browser compatibility is critical

Pros & Cons

Pros

Compute Shader Education

Demonstrates rasterization purely with WebGPU compute shaders, offering a hands-on way to learn GPU programming beyond traditional graphics pipelines, as detailed in the accompanying guides.

Minimal and Clear Codebase

Provides a simple template with focused code, making it easy to experiment and understand compute-based rendering techniques without overwhelming complexity.

Modern WebGPU Focus

Utilizes WebGPU, the next-generation web API, giving developers exposure to cutting-edge GPU compute capabilities on the web.

Basic glTF Model Loading

Supports loading triangle data from simple glTF files, introducing 3D model handling in a compute context for educational purposes.

Cons

Extremely Limited Graphics Features

Only implements distance-based shading and lacks textures, lighting, or anti-aliasing, making it unsuitable for realistic or production-ready rendering.

WebGPU Browser Dependency

Requires browsers with WebGPU support, which is still experimental or unsupported in many environments, adding setup hurdles and compatibility issues.

Unoptimized Performance

As an educational project, it's not tuned for speed; compute shader rasterization is likely slower than hardware-accelerated pipelines for anything beyond trivial scenes.

Sparse Advanced Documentation

While basic guides exist, the codebase is minimal and lacks examples for extending features like error handling or complex shader modifications, leaving users to self-guide.

Frequently Asked Questions

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Build a compute rasterizer in WebGPU

MITJavaScript

A software rasterizer implemented with WebGPU compute shaders for educational exploration of compute-based rendering.

GitHub

266 stars9 forks0 contributors

What is Build a compute rasterizer in WebGPU?

Target Audience

Graphics programmers, students, and developers interested in learning WebGPU, compute shaders, or alternative rendering techniques beyond standard graphics APIs.

Value Proposition

Overview

A simple software rasterizer running on a WebGPU compute shader. Built for educational purposes.

Use Cases

Best For

Learning WebGPU compute shader fundamentals
Experimenting with software rasterization techniques
Understanding compute-based rendering pipelines
Educational projects on GPU programming
Prototyping custom rendering approaches with WebGPU
Exploring alternatives to traditional graphics APIs for rasterization

Not Ideal For

Production applications requiring high-performance, real-time 3D rendering with advanced effects
Projects needing textured models, complex lighting, or post-processing beyond basic shading
Teams looking for a drop-in rendering library without deep compute shader expertise
Environments without WebGPU support or where browser compatibility is critical

Pros & Cons

Pros

Compute Shader Education

Demonstrates rasterization purely with WebGPU compute shaders, offering a hands-on way to learn GPU programming beyond traditional graphics pipelines, as detailed in the accompanying guides.

Minimal and Clear Codebase

Provides a simple template with focused code, making it easy to experiment and understand compute-based rendering techniques without overwhelming complexity.

Modern WebGPU Focus

Utilizes WebGPU, the next-generation web API, giving developers exposure to cutting-edge GPU compute capabilities on the web.

Basic glTF Model Loading

Supports loading triangle data from simple glTF files, introducing 3D model handling in a compute context for educational purposes.

Cons

Extremely Limited Graphics Features

Only implements distance-based shading and lacks textures, lighting, or anti-aliasing, making it unsuitable for realistic or production-ready rendering.

WebGPU Browser Dependency

Requires browsers with WebGPU support, which is still experimental or unsupported in many environments, adding setup hurdles and compatibility issues.

Unoptimized Performance

As an educational project, it's not tuned for speed; compute shader rasterization is likely slower than hardware-accelerated pipelines for anything beyond trivial scenes.

Sparse Advanced Documentation

While basic guides exist, the codebase is minimal and lacks examples for extending features like error handling or complex shader modifications, leaving users to self-guide.