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.

Graphics rendering on web browsers serves as the foundation for numerous web applications. Compared with the widely employed WebGL, the next-generation web graphics API, WebGPU, demonstrates an enhanced capacity to adapt to modern GPU features, boasting more significant potential. However, our experiment shows that the performance of current graphics rendering frameworks based on WebGPU lags behind those built on WebGL. Such discrepancy primarily arises from an incomplete alignment with WebGPU's distinctive features. The individual rendering of each graphic leads to redundant communication between the CPU and GPU. To enhance the graphics performance on the web, we introduce the FusionRender to harness the power of WebGPU. To mitigate redundant communication, FusionRender assigns a unique signature to each object and employs these signatures for grouping, enabling the consolidation of graphics rendering whenever possible. In simulated experiments involving the rendering of multiple objects, FusionRender improves the rendering performance by 29.3%-122.1% compared with the existing optimal baseline. In real cases with more complex features, performance improvement ranges from 9.4% to 39.7%. Additionally, FusionRender exhibits robust performance enhancement across various devices and browsers.

Online service providers rely on customer service to enhance the experience of troubled users who encounter problems when interacting with online services. Nowadays, the customer service usually follows a reactive style, i.e., users with problems actively resort to help, and the customer service passively solves problems. But a more ideal style pursued by online service providers is proactive customer service, where users with problems can be detected in advance and notified of possible solutions before they resort to help. However, is it possible to detect users with problems? To answer the question, in this paper, we collect user traces of page access logs and problem feedback through customer service from a commercial online service provider Fliggy. We first verify an intuition that the page access logs are a good indicator to detect users with problems. Based on this verification, we design ADPal, an approach to detecting users with problems. Given a user’s page access log, ADPal outputs whether he/she encounters problems. ADPal leverages the capability of Transformer to extract relationships between pages, achieving high effectiveness and high efficiency. Evaluations on real-world data sets show that ADPal can achieve P@1000 74.70%, outperforming state-of-the-art anomaly detection approaches.

Mobile extended reality (XR) has developed rapidly in recent years. Compared with the app-based XR, XR in web browsers has the advantages of being lightweight and cross-platform, providing users with a pervasive experience. Therefore, many frameworks are emerging to support the development of XR in web browsers. However, little has been known about how well these frameworks perform and how complex XR apps modern web browsers can support on mobile devices. To fill the knowledge gap, in this paper, we conduct an empirical study of mobile XR in web browsers. We select seven most popular web-based XR frameworks and investigate their runtime performance, including 3D rendering, camera capturing, and real-world understanding. We find that current frameworks have the potential to further enhance their performance by increasing GPU utilization or improving computing parallelism. Besides, for 3D scenes with good rendering performance, developers can feel free to add camera capturing with little influence on performance to support augmented reality (AR) and mixed reality (MR) applications. Based on our findings, we draw several practical implications to provide better XR support in web browsers.

Mobile Web apps are emerging to leverage DNN models to provide intelligent user experience. But the limited functionalities for heterogeneous hardware provided by mobile Web browsers challenge the Web apps to perform DNN inference efficiently. In this paper, we propose a novel DNN inference engine, named PipeEngine, to parallelize the DNN inference process on CPU and GPU in mobile Web browsers. The design of PipeEngine enables pipeline parallelism between two adjacent DNN inference tasks with heterogeneous hardware. Evaluation results show that PipeEngine can increase the inference throughput by up to 2.77×.

Modern mobile OSes support to display Web pages in the native apps, which we call embedded Web pages. In this paper, we conduct, to the best of our knowledge, the first measurement study on browsing embedded Web pages on Android. Our study on 22,521 popular Android apps shows that 57.9% and 73.8% of apps embed Web pages on two popular app markets: Google Play and Wandoujia, respectively. To analyze the embedded Web browsing performance at scale, we design and implement EWProfiler, a tool that can automatically search for embedded Web pages inside apps, trigger page loads, and retrieve performance metrics. Based on 445 embedded Web pages obtained by EWProfiler in 99 popular apps from the two app markets, we investigate the characteristics and performance of embedded Web pages, and find that embedded Web pages significantly impede the app user experience. To optimize the performance of embedded Web browsing, we investigate the effectiveness of three techniques, i.e., separating the browser kernel to a different process, loading pages from local storage, and pre-rendering. We believe that our findings could draw attentions to Web developers, browser vendors, app developers, and mobile OS vendors together towards better performance of embedded Web browsing.

The web browser is one of the major channels to access the Internet on mobile devices. Based on the smartphone usage logs from millions of real-world Android users, it is interesting to find that about 38% users have more than one browser on their devices. However, it is unclear whether the quality of browsing experiences are different when visiting the same webpage on different browsers. In this paper, we collect 3-week consecutive traces of 337 popular webpages on three popular mobile browsers: Chrome, Firefox, and Opera. We first use a list of metrics and conduct an empirical study to measure the differences of these metrics on different browsers. Then, we explore the variety of loading time and cache performance of different browsers when visiting the same webpage, which has a great impact on the browsing experience. Furthermore, we try to find which metrics have significant effect on the differences, investigating the possible causes. Finally, according to our findings, we give some recommendations to web developers, browser vendors, and end users.

Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers. However, little is known on what and how well we can do with these frameworks for deep learning in browsers. To bridge the knowledge gap, in this paper, we conduct the first empirical study of deep learning in browsers. We survey 7 most popular JavaScript-based deep learning frameworks, investigating to what extent deep learning tasks have been supported in browsers so far. Then we measure the performance of different frameworks when running different deep learning tasks. Finally, we dig out the performance gap between deep learning in browsers and on native platforms by comparing the performance of TensorFlow.js and TensorFlow in Python. Our findings could help application developers, deep-learning framework vendors and browser vendors to improve the efficiency of deep learning in browsers.

Given v sets and an incoming item e, multi-set membership query is to report which set contains item e. Multi-set membership query is a fundamental problem in computer systems and applications. All existing data structures cannot achieve small memory usage, fast query speed and high accuracy at the same time. In this paper, we propose a novel probabilistic data structure named Difference Bloom Filter (DBF) for fast multi-set membership query, which not only is more accurate than the state-of-the-art, but has a faster query speed. There are two key design principles for DBF. The first one is to make the representation of the membership of elements exclusive by writing different number of 1s and 0s in the same filter, and the second one is to use the slow but cheap DRAM memory to improve the accuracy of the filter on the fast but expensive SRAM memory. Experimental results show that in terms of accuracy, DBF has a great advantage compared to state-of-the-art, being hundreds of times more accurate than the state-of-the-art vBF and ShBF. Furthermore, we have made the source code of our DBF available at our homepage [1] and GitHub [2].