gl2gpu-demo-www25-submission-6.webm
GL2GPU is a dynamic translator that boosts WebGL rendering performance by converting WebGL API calls into WebGPU at JavaScript runtime — with no need to rewrite source code or modify the browser. Our evaluations show up to 45% average frame time reduction across platforms while preserving visual consistency.
- ✅ Dynamic Translation: Automatically intercepts and translates WebGL calls to WebGPU in real time.
- 🚀 Performance Boost: Achieves significant speedups (up to 87.7% on mobile) without modifying original applications.
- 📦 No Browser or App Modifications: Works entirely via JavaScript runtime and prototype patching.
- 📊 Benchmarks Validated: Validated using MotionMark, JSGameBench, and WebGL Aquarium.
- 🧠 Adaptive Optimizations: Uses shader translation, render state caching, uniform batching, and bundle reuse for efficiency.
GL2GPU captures WebGL rendering commands, emulates WebGL state, and dynamically generates equivalent WebGPU shaders and descriptors. The system applies several optimization strategies:
- WebGL State Emulation: Tracks state changes using JavaScript prototype patching.
- Shader Translation: Converts GLSL to WGSL with shared variable merging.
- Two-Level Caching: Avoids redundant resource generation.
- Uniform Batching: Reduces GPU memory traffic.
- Render Bundle Management: Reuses WebGPU command sequences via Trie structure.
For architectural details, see our WWW 2025 paper.
You can integrate GL2GPU into your web application as a standalone JavaScript module:
<script src="path/to/gl2gpu.bundle.js"></script>Note: WebGPU support requires recent versions of Chrome (v114+) with WebGPU enabled.
| Benchmark | Avg. Frame Time Reduction |
|---|---|
| MotionMark | 73.9% – 87.7% |
| JSGameBench | 19.3% – 61.4% |
| Aquarium | 3.3% – 63.8% |
Tested across Mac, Windows, and Android devices with various GPUs (AMD, NVIDIA, Intel, Snapdragon).
📺 Live Demo
- 🌐 Live site: https://gl2gpu.hanyd.site/
- 🧪 Demo source: https://github.com/yudshj/gl2gpu-demo
If you use GL2GPU in your research, please cite:
@inproceedings{10.1145/3696410.3714785,
author = {Han, Yudong and Bi, Weichen and An, Ruibo and Tian, Deyu and Yang, Qi and Ma, Yun},
title = {GL2GPU: Accelerating WebGL Applications via Dynamic API Translation to WebGPU},
year = {2025},
isbn = {9798400712746},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3696410.3714785},
doi = {10.1145/3696410.3714785},
booktitle = {Proceedings of the ACM on Web Conference 2025},
pages = {751–762},
numpages = {12},
keywords = {api translation, graphics, web applications, webgl, webgpu},
location = {Sydney NSW, Australia},
series = {WWW '25}
}- Yudong Han, Weichen Bi, Ruibo An, Deyu Tian, Qi Yang, Yun Ma (Peking University)