GL2GPU: Accelerating WebGL Applications via Dynamic API Translation to WebGPU

WebGL has long been the prevalent API for GPU-accelerated graphics in web browsers, boosting 2D/3D graphical web applications. Despite widespread adoption, WebGL's programming model hinders its rendering performance on modern GPU hardware. To this end, WebGPU has been proposed as the next-generation API of GPU-accelerated processing in web browsers, exhibiting higher performance than WebGL. However, considering the complex logic of WebGL applications and the still-evolving WebGPU specification, statically migrating existing WebGL applications to WebGPU from source code is labor-intensive. To address this issue, we propose GL2GPU, an intermediate layer that dynamically translates WebGL to WebGPU at JavaScript runtime to improve rendering performance. GL2GPU addresses the inconsistencies between the WebGL and WebGPU programming models by emulating WebGL rendering states and leverages performance optimization mechanisms introduced by WebGPU to reduce the overhead of dynamic translation. Evaluation of three representative WebGL benchmarks shows that GL2GPU significantly enhances end-to-end rendering performance while maintaining visual consistency, achieving an average frame time reduction of 45.05% across different devices and operating systems.

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Publication Details

Published
2025-04-22
DOI
https://doi.org/10.1145/3696410.3714785
Citations
2
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
2.97

Funders

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article

GL2GPU: Accelerating WebGL Applications via Dynamic API Translation to WebGPU

Deyu Tian, Qi Yang, Weichen Bi, Yun Ma et al.
2 citations
Distributed and Parallel Computing Systems
2.97
article

GL2GPU: Accelerating WebGL Applications via Dynamic API Translation to WebGPU

Deyu Tian, Qi Yang, Weichen Bi, Yun Ma, Yudong Han, R. An
article en
2 citations

Abstract

WebGL has long been the prevalent API for GPU-accelerated graphics in web browsers, boosting 2D/3D graphical web applications. Despite widespread adoption, WebGL's programming model hinders its rendering performance on modern GPU hardware. To this end, WebGPU has been proposed as the next-generation API of GPU-accelerated processing in web browsers, exhibiting higher performance than WebGL. However, considering the complex logic of WebGL applications and the still-evolving WebGPU specification, statically migrating existing WebGL applications to WebGPU from source code is labor-intensive. To address this issue, we propose GL2GPU, an intermediate layer that dynamically translates WebGL to WebGPU at JavaScript runtime to improve rendering performance. GL2GPU addresses the inconsistencies between the WebGL and WebGPU programming models by emulating WebGL rendering states and leverages performance optimization mechanisms introduced by WebGPU to reduce the overhead of dynamic translation. Evaluation of three representative WebGL benchmarks shows that GL2GPU significantly enhances end-to-end rendering performance while maintaining visual consistency, achieving an average frame time reduction of 45.05% across different devices and operating systems.

Peking University (CN), Beijing Academy of Artificial Intelligence (CN)
Peking University
Openalex Percentile: Top 9%
Distributed and Parallel Computing Systems
2.97
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