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Ruibo An

dblp:402/7233 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0005-3061-442XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Rendering · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 100%
Software engineering, system software, and programming languages
1 paper
Runtime systems and virtual machines · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Runtime systems and virtual machines › binary translation
dynamic translation
0.312025
GL2GPU: Accelerating WebGL Applications via Dynamic API Translation to WebGPU · WWW 2025

Methods — techniques the papers use, named apart from their topics

rendering state emulation · 2.6dynamic API translation · 2.6
YearPublicationVenuePosition
2025 GL2GPU: Accelerating WebGL Applications via Dynamic API Translation to WebGPU
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.
Yudong Han 0001, Weichen Bi, Ruibo An, Deyu Tian, Yun Ma 0002
WWW3