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IEEE Symposium on High-Performance Computer Architecture

作者： Wenju Zhao Pengcheng Yao Dan Chen Long Zheng Xiaofei Liao Qinggang Wang Shaobo Ma Yu Li Haifeng Liu Wenjing Xiao Yufei Sun Bing Zhu Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computing National University of Singapore Singapore School of Computer Electronics and Information Guangxi University NanNing China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of $1,032.23 \times$ and $10.51 \times$, and energy efficiencies of $1,169.03 \times$ and $9.96 \times$, respectively.

关键词： Computational modeling Neural networks Redundancy Pipelines Memory management Parallel processing Vectors Energy efficiency Hardware acceleration

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

arXiv

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arXiv 2025年

作者： Bi, Zhangqian Wan, Yao Chu, Zhaoyang Hu, Yufei Zhang, Junyi Zhang, Hongyu Xu, Guandong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Technology Sydney Sydney Australia

Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pre-training language models on a large-scale code corpus is computationally expensive. Fortunately, many off-the-shelf Pre-trained Code Models (PCMs), such as CodeBERT, CodeT5, CodeGen, and Code Llama, have been released publicly. These models acquire general code understanding and generation capability during pre-training, which enhances their performance on downstream code intelligence tasks. With an increasing number of these public pre-trained models, selecting the most suitable one to reuse for a specific task is essential. In this paper, we systematically investigate the reusability of PCMs. We first explore three intuitive model selection methods that select by size, training data, or brute-force fine-tuning. Experimental results show that these straightforward techniques either perform poorly or suffer high costs. Motivated by these findings, we explore learning-based model selection strategies that utilize pre-trained models without altering their parameters. Specifically, we train proxy models to gauge the performance of pre-trained models, and measure the distribution deviation between a model's latent features and the task's labels, using their closeness as an indicator of model transferability. We conduct experiments on 100 widely-used open-source PCMs for code intelligence tasks, with sizes ranging from 42.5 million to 3 billion parameters. The results demonstrate that learning-based selection methods reduce selection time to 100 seconds, compared to 2,700 hours with brute-force fine-tuning, with less than 6% performance degradation across related tasks. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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Hybrid followee recommendation in microblogging systems

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Science China(Information Sciences) 2017年第1期60卷 21-34页

作者： Hanhua CHEN Hai JIN Xiaolong CUI Services Computing Technology and System Laboratory Big Data Technology and System LaboratoryCluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and Technology

Followee recommendation plays an important role in information sharing over microblogging platforms. Existing followee recommendation schemes adopt either content relevance or social information for followee ranking, suffering poor performance. Based on the observation that microblogging systems have dual roles of social network and news media platform, we propose a novel followee recommendation scheme that takes into account the information sources of both tweet contents and the social structures. We set up a linear weighted model to combine the two factors and further design a simulated annealing algorithm to automatically assign the weights of both factors in order to achieve an optimized combination of them. We conduct comprehensive experiments on real-world datasets collected from Sina Weibo, the largest microblogging system in China. The results demonstrate that our scheme provides a much more accurate followee recommendation for a user compared to existing schemes.

关键词： online social networks microblogging followee recommendation simulated annealing ranking

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Exploring Semantic Association Rules Between Keyword and Visual Feature clusters for Web Image Retrieval

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电子学报(英文版) 2008年第1期17卷 59-65页

作者： JIN Hai HE Ruhan TAO Wenbing Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan 430074 China

The semantic gap is a big challenge in image retrieval area. Previous studies in web image retrieval have mainly focused on Relevance feedback (RF) and Latent semantic indexing (LSI) to alleviate the gap. This paper proposes an approach base on Frequent itemset mining (FIM) and Association rule (AR) techniques, which explores the semantic association rule between the two modalities that are represented by keyword and visual feature clusters. The rules are obtained offline based on the inverted file, and utilized in query process online to realize the integration of the two modalities of web images. Our approach improves the retrieval performance and is scalable well, as well as satisfies the requirement of the web users with no additional interactions. The experiments are carried out in our web image retrieval system named VAST (VisuAl & SemanTic image search), and the results show the effectiveness of the proposed approach.

关键词：图像处理自适应算法图像恢复语义联合规则视觉特征

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Understanding and identifying latent data races cross-thread interleaving

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Frontiers of Computer Science 2015年第4期9卷 524-539页

作者： Long ZHEN~ Xiaofei LIAO Song WU Xuepeng FAN Hai JIN Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Data races are ubiquitous in multi-threaded ap- plications, but they are by no means easy to detect. One of the most important reasons is the complexity of thread in- terleavings. A volume of research has been devoted to the interleaving-insensitive detection. However, all the previous work focuses on the uniform detection （unknown to the char- acteristics of thread interleavings）, thereby making the detec- tion defective in either reporting false positives or suffering from prohibitive overhead. To cope with the problem above, we propose an efficient, precise, and sound step-by-step res- olution based on the characteristics of thread interleavings. We first try to tease apart the categories of thread interleav- ings from the several typical sources arising from the lock synchronizations. We then conduct a brief study and find a new and complex pattern the previous work cannot detect. It is also revealed that the simple pattern with the majority of thread interleavings can be resolved by a simple processing to achieve a big profit for the previous reordering-based design. Our final experimental results demonstrate the effectiveness of our empiricism-based approach, and show that 51.0% of execution time and 52.3 % of trace size arising from the state- of-the-art reordering technique can be saved through a quick filtering of the simple pattern with a negligible （4.45%） per- formance overhead introduced on-the-fly.

关键词： data race happens-before thread interleaving

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UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs 25

UICopilot: Automating UI Synthesis via Hierarchical Code Gen...

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Proceedings of the ACM on Web Conference 2025

作者： Yi Gui Yao Wan Zhen Li Zhongyi Zhang Dongping Chen Hongyu Zhang Yi Su Bohua Chen Xing Zhou Wenbin Jiang Xiangliang Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Huazhong University of Science and Technology Wuhan China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame USA

ISBN: (纸本)9798400712746

Automating the synthesis of User Interfaces (UIs) plays a crucial role in enhancing productivity and accelerating the development lifecycle, reducing both development time and manual effort. Recently, the rapid development of Multimodal Large Language Models (MLLMs) has made it possible to generate front-end Hypertext Markup Language (HTML) code directly from webpage designs. However, real-world webpages encompass not only a diverse array of HTML tags but also complex stylesheets, resulting in significantly lengthy code. The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. To validate the effectiveness of UICopilot, we conduct experiments on a real-world dataset, i.e., WebCode2M. Experimental results demonstrate that UICopilot significantly outperforms existing baselines in both automatic evaluation metrics and human evaluations. Specifically, statistical analysis reveals that the majority of human annotators prefer the webpages generated by UICopilot over those produced by GPT-4V.

关键词： UI automation

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A Protective Mechanism for the Access Control system in the Virtual Domain

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China Communications 2016年第11期13卷 129-142页

作者： Jinan Shen Deqing Zou Hai Jin Kai Yang Bin Yuan Weiming Li Services Computing Technology and System Laboratory Cluster and Grid Computing LaboratorySchool of Computing Science and TechnologyHuazhong University of Science and Technology School of Information Engineering Hubei University for Nationalities

In traditional framework,mandatory access control(MAC) system and malicious software are run in kernel mode. Malicious software can stop MAC systems to be started and make it do invalid. This problem cannot be solved under the traditional framework if the operating system(OS) is comprised since malwares are running in ring 0 level. In this paper,we propose a novel way to use hypervisors to protect kernel integrity and the access control system in commodity operating systems. We separate the access control system into three parts: policy management(PM),security server(SS) and policy enforcement(PE). Policy management and the security server reside in the security domain to protect them against malware and the isolation feather of the hypervisor can protect them from attacks. We add an access vector cache(AVC) between SS and PE in the guest OS,in order to speed up communication between the guest OS and the security domain. The policy enforcement module is retained in the guest OS for performance. The security of AVC and PE can be ensured by using a memory protection mechanism. The goal of protecting the OS kernel is to ensure the security of the execution path. We implementthe system by a modified Xen hypervisor. The result shows that we can secure the security of the access control system in the guest OS with no overhead compared with modules in the latter. Our system offers a centralized security policy for virtual domains in virtual machine ***: hypervisor; virtualization; memo-

关键词： hypervisor virtualization memory protection guest OS access control system

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Fine-grained and heterogeneous proxy re-encryption for secure cloud storage

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Chinese Science Bulletin 2014年第32期59卷 4201-4209页

作者： Peng Xu Hongwu Chen Deqing Zou Hai Jin Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology

Cloud is an emerging computing *** has drawn extensive attention from both academia and *** its security issues have been considered as a critical obstacle in its rapid *** data owners store their data as plaintext in cloud,they lose the security of their cloud data due to the arbitrary accessibility,specially accessed by the un-trusted *** order to protect the confidentiality of data owners’cloud data,a promising idea is to encrypt data by data owners before storing them in ***,the straightforward employment of the traditional encryption algorithms can not solve the problem well,since it is hard for data owners to manage their private keys,if they want to securely share their cloud data with others in a fine-grained *** this paper,we propose a fine-grained and heterogeneous proxy re-encryption(FHPRE)system to protect the confidentiality of data owners’cloud *** applying the FH-PRE system in cloud,data owners’cloud data can be securely stored in cloud and shared in a fine-grained ***,the heterogeneity support makes our FH-PRE system more efficient than the previous ***,it provides the secure data sharing between two heterogeneous cloud systems,which are equipped with different cryptographic primitives.

关键词：安全问题数据存储加密算法细粒度异构代理计算模式数据共享

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WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs 25

WebCode2M: A Real-World Dataset for Code Generation from Web...

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Proceedings of the ACM on Web Conference 2025

作者： Yi Gui Zhen Li Yao Wan Yemin Shi Hongyu Zhang Bohua Chen Yi Su Dongping Chen Siyuan Wu Xing Zhou Wenbin Jiang Hai Jin Xiangliang Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Peking University Beijing China Chongqing University Chongqing China Huazhong University of Science and Technology Wuhan China Hubei University of Automotive Technology Shiyan China Huazhong University of Science and Technology Hubei China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame USA

ISBN: (纸本)9798400712746

Automatically generating webpage code from webpage designs can significantly reduce the workload of front-end developers, and recent Multimodal Large Language Models (MLLMs) have shown promising potential in this area. However, our investigation reveals that most existing MLLMs are constrained by the absence of high-quality, large-scale, real-world datasets, resulting in inadequate performance in automated webpage code generation. To fill this gap, this paper introduces WebCode2M, a new dataset comprising 2.56 million instances, each containing a design image along with the corresponding webpage code and layout details. Sourced from real-world web resources, WebCode2M offers a rich and valuable dataset for webpage code generation across a variety of applications. The dataset quality is ensured by a scoring model that filters out instances with aesthetic deficiencies or other incomplete elements. To validate the effectiveness of WebCode2M, we introduce a baseline model based on the Vision Transformer (ViT), named WebCoder, and establish a benchmark for fair comparison. Additionally, we introduce a new metric, TreeBLEU, to measure the structural hierarchy recall. The benchmarking results demonstrate that our dataset significantly improves the ability of MLLMs to generate code from webpage designs, confirming its effectiveness and usability for future applications in front-end design tools. Finally, we highlight several practical challenges introduced by our dataset, calling for further research. The code and dataset are publicly available at our project homepage: https://***.

关键词： code generation

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Finding and evaluating the community structure in semantic peer-to-peer overlay networks

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Science China(Information Sciences) 2011年第7期54卷 1340-1351页

作者： CHEN HanHua & JIN Hai services computing technology and system laboratory,cluster and grid computing laboratory,School of Computer Science and technology,Huazhong University of Science and technology,Wuhan 430074,China 1. Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Recently much attention has been paid to semantic overlay networks for information retrieval in large scale peer-to-peer networks,and much research work on semantic overlay protocols and searching algorithms has been done and the results indicate that semantic overlay is efficient for content searching in peer-to-peer ***,very limited work has been done to analyze and evaluate the characteristics of semantic overlay *** this paper we identify a natural property of semantic overlay networks,the community *** propose a mathematical model to evaluate the property of community structure of semantic P2P overlay networks.A heuristic algorithm is designed to optimize the community *** the evaluation model we compare the SemreX semantic overlay with the Gnutella *** demonstrate that a SemreX overlay network has the distinctive community structure feature,while a Gnutella-like network does *** also simulate a simple flooding protocol in both overlays to show that the overlay with community structure is more efficient for content searching.

关键词： peer-to-peer community structure semantic overlay

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