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33rd ACM Web Conference, WWW 2024

作者： Hu, Haichuan Liu, Fangming Pei, Qiangyu Yuan, Yongjie Xu, Zichen Wang, Lin National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab in the School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan China Peng Cheng Laboratory Huazhong University of Science and Technology China School of Mathematics and Computer Science Nanchang University China Paderborn University Paderborn Germany

ISBN: (纸本)9798400701719

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and intricate GNN operations. Serverless computing, thanks to its flexibility and agility, offers on-demand serving of GNN inference requests. Alas, the request-centric serverless model is still too coarse-grained to avoid resource waste. Observing the significant data locality in computation graphs of requests, we propose λGrapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. "Grapher features the following designs: (1) adaptive timeout for request buffering to balance resource efficiency and inference latency, (2) graph-centric scheduling to minimize computation and memory redundancy, and (3) resource-centric function management with fine-grained resource allocation catered to the resource sensitivities of GNN operations and function orchestration optimized to hide communication latency. We implement a prototype of λGrapher based on the representative open-source serverless platform Knative and evaluate it with real-world traces from various web applications. Our results show that λGrapher can achieve an average savings of 61.5% in memory resource and 47.2% in computing resource compared with the state of the arts while ensuring GNN inference latency. © 2024 ACM.

关键词： Efficiency

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd International Conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent systems.

关键词： Reinforcement learning

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Toward High-Performance Delta-Based Iterative Processing with a Group-Based Approach

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Journal of Computer Science & technology 2022年第4期37卷 797-813页

作者： Hui Yu Xin-Yu Jiang Jin Zhao Hao Qi Yu Zhang Xiao-Fei Liao Hai-Kun Liu Fu-Bing Mao Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations;while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.

关键词： iterative algorithm delta-based execution model efficiency

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RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network 57

RAHP: A Redundancy-aware Accelerator for High-performance Hy...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Yu, Hui Zhang, Yu He, Ligang Zhao, Yingqi Li, Xintao Xin, Ruida Zhao, Jin Liao, Xiaofei Liu, Haikun He, Bingsheng Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Natl. Eng. Res. Ctr. for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China University of Warwick Department of Computer Science United Kingdom National University of Singapore Singapore

ISBN: (纸本)9798350350579

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： accelerator HyperGNN redundancy-aware

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Discovering Cohesive Temporal Subgraphs with Temporal Density Aware Exploration

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Journal of Computer Science & technology 2022年第5期37卷 1068-1085页

作者： Chun-Xue Zhu Long-Long Lin Ping-Peng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Real-world networks,such as social networks,cryptocurrency networks,and e-commerce networks,always have occurrence time of interactions between *** networks are typically modeled as temporal *** cohesive subgraphs from temporal graphs is practical and essential in numerous data mining applications,since mining cohesive subgraphs gets insights into the time-varying nature of temporal ***,existing studies on mining cohesive subgraphs,such as Densest-Exact and k-truss,are mainly tailored for static graphs(whose edges have no temporal information).Therefore,those cohesive subgraph models cannot indicate both the temporal and the structural characteristics of *** this end,we explore the model of cohesive temporal subgraphs by incorporating both the evolving and the structural characteristics of temporal ***,the volume of time intervals in a temporal network is *** a result,the time complexity of mining temporal cohesive subgraphs is *** efficiently address the problem,we first mine the temporal density distribution of temporal *** by the distribution,we can safely prune many unqualified time intervals with the linear time ***,the remaining time intervals where cohesive temporal subgraphs fall in are examined using the greedy *** results of the experiments on nine real-world temporal graphs indicate that our model outperforms state-of-the-art solutions in efficiency and ***,our model only takes less than two minutes on a million-vertex DBLP and has the highest overall average ranking in EDB and TC metrics.

关键词： temporal network temporal feature distribution cohesive subgraph convex property

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EdgeThemis: Ensuring Model Integrity for Edge Intelligence 25

EdgeThemis: Ensuring Model Integrity for Edge Intelligence

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34th ACM Web Conference, WWW 2025

作者： Yang, Jiyu He, Qiang Zhou, Zheyu Dai, Xiaohai Chen, Feifei Tian, Cong Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia Xidian University Xi’an China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400712746

Machine learning (ML) models are widely deployed on edge nodes, such as mobile phones and edge servers, to power a wide range of AI applications over the web. Ensuring the integrity of these edge models is paramount, as they are subject to corruption caused by software/hardware exceptions and malicious tampering, which may undermine model performance, incur economic losses, and pose health risks. Existing data integrity mechanisms designed for files stored on disks cannot properly verify the integrity of models running in GPUs or mitigate the new integrity threats against edge models. This paper proposes EdgeThemis, a novel mechanism for verifying the integrity of edge models through sentinel verification. To enable verifiability for a model M, EdgeThemis embeds a sentinel backdoor and a verification module into M. Then, a challenger can send verification requests to the edge node hosting M to verify its integrity. Next, the sentinel activates the verification module to generate a unique integrity proof tied to the identity of the edge node for verification. Finally, the challenger can verify the integrity proof to detect model corruption. Theoretical analysis proves that EdgeThemis can properly mitigate potential integrity threats against edge models. Experiments demonstrate that EdgeThemis achieves a verification accuracy of 100.00% across various models and different types of model corruption with robustness against replay attacks, theft attacks, and replacement attacks. © 2025 Copyright held by the owner/author(s).

关键词： Backpropagation

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TransRec++:Translation-based sequential recommendation with heterogeneous feedback

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Frontiers of Computer Science 2022年第2期16卷 190-192页

作者： Zhuo-Xin ZHAN Ming-Kai HE Wei-Ke PAN Zhong MING National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China

1 Introduction Nowadays,recommender systems are widely used because of the information overload problem on the *** are a variety of feedback in recommender systems,such as explicit feedback,implicit feedback,sequential feedback,*** them,modeling of heterogeneous sequential feedback,which contains not only different types of feedback such as examinations and purchases but also the sequential information,is an emerging and important problem receiving more and more *** sequential feedback is relatively easy to be collected in a deployed system and is also able to provide more information than the homogeneous feedback,which is thus expected to be helpful in improving the recommendation accuracy.

关键词： feedback recommendation sequential

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FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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MeG2: In-Memory Acceleration for Genome Graphs Analysis 23

MeG2: In-Memory Acceleration for Genome Graphs Analysis

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Yu Huang Long Zheng Haifeng Liu Zhuoran Zhou Dan Chen Pengcheng Yao Qinggang Wang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China and Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350323481

Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augment the genetic variations and diversity information, significantly improving the quality of genotyping. The in-depth characterization of genome graphs analysis uncovers that it is bottlenecked by the irregular seed index access and the intensive alignment operation, stressing both the memory system and computing *** on these observations, we propose MeG2, a lightweight, commodity DRAM-compliant, processing-in-memory architecture to accelerate genome graphs analysis. MeG2 is specifically integrated with the capabilities of both near-memory processing and bitwise in-situ computation. Specifically, MeG2 leverages the low access latency of near-memory processing with the index-centric offload mechanism to alleviate the irregular memory access in the seeding procedure, and harnesses the row-parallel capacity of in-situ computation with the distance-aware technique to exploit the intensive computational parallelism in the alignment process. Results show that MeG2 outperforms the CPU-, GPU-, and ASIC-based genome graphs analysis solutions by 502× (30.2×), 272× (15.1×), and 5.5× (8.3×) for short (long) reads, while reducing energy consumption by 1628× (85.6×), 1443× (77.1×), and 7.8× (11.7×), respectively. We also demonstrate that MeG2 offers significant improvements over existing PIM-based genome sequence analysis accelerators.

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DD-UNet: Densely Dilated U-Net for Curvilinear Structure Segmentation in Fundus Image

DD-UNet: Densely Dilated U-Net for Curvilinear Structure Seg...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yindong Chen, Jie Wang, Li Chen, Miaohong Zhang, Guoming Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Eye Hospital China

ISBN: (纸本)9798350337488

Retinopathy of Prematurity (ROP) is a retina disorder that affects premature infants with lower weights. If the patient cannot get the treatment in time when the illness reaches the last stage, irreversible vision loss will be caused. Nevertheless, there has been relatively little consideration given to the segmentation of the ridge, the key clinical characteristic of the illness. Additionally, existing research has not adequately addressed several segmentation issues, such as fragmentary topology, class imbalance, and false positives. This paper proposes a Densely Dilated U-Net (DD-UNet) improved from U-Net to tackle these challenges. Furthermore, the post-processing techniques based on the spatial relationship between vessels and ridges, along with the relative pixel counts of ridges and false positive results is integrated to mitigate false positive results in the predicted ridge. To enhance the precision of thin vessel, a sliding window sampling method is introduced for refined training. Compared with the state-of-the-art models in medical image segmentation, DD-UNet performs well in curvilinear structure segmentation of fundus image. For instance, our DD-UNet outperforms the Attention U-Net by 6.26% in terms of sensitivity and exhibits a 1.85% higher dice score in ridge segmentation. © 2023 IEEE.

关键词： curvilinear structure segmentation fundus image Retinopathy of Prematurity (ROP) ridge segmentation vessel segmentation

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