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Xuyan Hu

dblp:160/0576 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2025
—ORCID · none

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

Software engineering, systems software and programming languages · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 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 architecture, parallel and distributed computing, and storage systems
2 papers
Cloud and datacenter computing · 54% GPUs and heterogeneous computing · 46%
Computer graphics and multimedia
2 papers
Rendering · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering › parallel rendering
distributed rendering
0.912025
Sudoku: Scalable High-Density Cloud Rendering Multi-Client Architecture · IEEE Trans. Serv. Comput. 2025
Cloud and datacenter computing › cloud applications
cloud rendering
0.912025
Sudoku: Scalable High-Density Cloud Rendering Multi-Client Architecture · IEEE Trans. Serv. Comput. 2025
GPUs and heterogeneous computing › GPU sharing
GPU pooling
0.812024
gVulkan: Scalable GPU Pooling for Pixel-Grained Rendering in Ray Tracing · USENIX ATC 2024
Rendering
ray tracing
0.212024
gVulkan: Scalable GPU Pooling for Pixel-Grained Rendering in Ray Tracing · USENIX ATC 2024
YearPublicationVenuePosition
2025 Sudoku: Scalable High-Density Cloud Rendering Multi-Client Architecture
Yun Wang 0039, Bing Deng, Xia Jiang, Xuyan Hu, Dongjie Tang, Randy Xu, Yijin Sun, Zhengwei Qi
IEEE Trans. Serv. Comput.4
2024 gHermes: Application-Unaware Acceleration for Cloud Rendering and Computing with Efficient GPU Utilization
abstract
GPUs, as crucial tools for intensive computing tasks, are primarily used for rendering and computation.However, due to the high cost of GPUs and their frequent updates, owning powerful local GPUs is considered a luxury.Offloading tasks to the cloud is an effective solution.This method allows users to leverage the powerful capabilities of cloud-based high-performance computing resources to perform complex rendering tasks more quickly and cost-effectively.However, there has not yet been a solution that simultaneously considers both rendering and computation without requiring any modifications to local applications.This paper introduces gHermes, an application-unaware acceleration solution that automatically generates code to intercept rendering and computing tasks, overcoming the limitations of existing technologies.Additionally, for computational tasks, gHermes also enables fine-grained control of GPU utilization.Experimental results show that gHermes can handle both computing and rendering tasks efficiently.While ensuring the Quality of Service (QoS) requirements for rendering tasks (30-60 FPS), it effectively utilizes GPU resources for computation services.
Xuyan Hu, Yicheng Gu, Zhengwei Qi
SEKE1
2024 gVulkan: Scalable GPU Pooling for Pixel-Grained Rendering in Ray Tracing
Yicheng Gu, Yun Wang 0039, Yunfan Sun, Yuxin Xiang, Xuyan Hu, Zhengwei Qi, Haibing Guan
USENIX ATC5