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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Chaonong Kong, Weiming Liu, Min Zhang, Mingming Li, Chao Gong, Luqi Beijing Key Lab of Petroleum Data Mining Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China Bupt School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350330717

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. Data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× -3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy. © 2023 IEEE.

关键词： data parallelism heterogeneous cluster hybrid parallelism model inference pipeline parallelism

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Nearly Optimal Regret for Decentralized Online Convex Optimization 37

Nearly Optimal Regret for Decentralized Online Convex Optimi...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Wan, Yuanyu Wei, Tong Song, Mingli Zhang, Lijun The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China Institute of Blockchain and Data Security Hangzhou China School of Computer Science and Engineering Southeast University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using_only local computations and communications. Previous studies have established O(n5/4ρ-1/2 √T) and O(n3/2ρ-1 log T) regret bounds for convex and strongly convex functions respectively, where n is the number of local learners, ρ -1/4 √T) and Õ(nρ-1/2 log T). The primary technique is to design an online accelerated gossip strategy that enjoys a faster average consensus among local learners. Furthermore, by carefully exploiting the spectral properties of a specific network topology, we enhance the lower bounds for convex and strongly convex functions to Ω(nρ-14 √T) and Ω(nρ-1/2), respectively. These lower bounds suggest that our algorithms are nearly optimal in terms of T, n, and ρ. © 2024 Y. Wan, T. Wei, M. Song & L. Zhang.

关键词： Optimization algorithms

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GCSS:a global collaborative scheduling strategy for wide-area high-performance computing

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Frontiers of computer Science 2022年第5期16卷 1-15页

作者： Yao SONG Limin XIAO Liang WANG Guangjun QIN Bing WEI Baicheng YAN Chenhao ZHANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Smart City College Beijing Union UniversityBeijing 100101China

Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage ***,the geographical distribution of computing and storage resources makes efficient task distribution and data placement more *** achieve a higher system performance,this study proposes a two-level global collaborative scheduling strategy for wide-area high-performance computing *** collaborative scheduling strategy integrates lightweight solution selection,redundant data placement and task stealing mechanisms,optimizing task distribution and data placement to achieve efficient computing in wide-area *** experimental results indicate that compared with the state-of-the-art collaborative scheduling algorithm HPS+,the proposed scheduling strategy reduces the makespan by 23.24%,improves computing and storage resource utilization by 8.28%and 21.73%respectively,and achieves similar global data migration costs.

关键词： high-performance computing scheduling strategy task scheduling data placement

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Mobility-Aware Multi-Task Migration and Offloading Scheme for Internet of Vehicles

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Chinese Journal of Electronics 2023年第6期32卷 1192-1202页

作者： LI Xujie TANG Jing XU Yuan SUN Ying College of Computer and Information Hohai University State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Key Lab for Wireless Sensor Network and Communication Shanghai Institute of Microsystem School of Information Engineering Jiangsu Open University

In Internet of vehicles, vehicular edge computing(VEC) as a new paradigm can effectively accomplish various tasks. Due to limited computing resources of the roadside units(RSUs), computing ability of vehicles can be a powerful supplement to computing resources. Then the task to be processed in data center can be offloaded to the vehicles by the RSUs. Due to mobility of the vehicles, the tasks will be migrated among the RSUs. How to effectively offload multiple tasks to the vehicles for processing is a challenging problem. A mobility-aware multi-task migration and offloading scheme for Internet of vehicles is presented and analyzed. Considering the coupling between migration and offloading, the joint migration and offloading optimization problem is formulated. The problem is a NP-hard problem and it is very hard to be solved by the conventional methods. To tackle the difficult problem, the idea of alternating optimization and divide and conquer is introduced. The problem can be decoupled into two sub-problems: computing resource allocation problem and vehicle node selection problem. If the vehicle node selection is given, the problem can be solved based on Lagrange function. And if the allocation of computing resource is given, the problem turns into a 0-1 integer programming problem, and the linear relaxation of branch bound algorithm is introduced to solve it. Then the optimization value is obtained through continuous iteration. Simulation results show that the proposed algorithm can effectively improve system performance.

关键词： Costs Computational modeling System performance Simulation Programming Multitasking Resource management

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STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Zhang, Yanan Zhou, Chao Huang, Di State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection. Although Self-Training (ST) based cross-domain 3D detection methods with the assistance of pseudo-labeling techniques have achieved remarkable progress, they still face the issue of low-quality pseudo-labels when there are significant domain disparities due to the absence of a process for feature distribution alignment. While Adversarial Learning (AL) based methods can effectively align the feature distributions of the source and target domains, the inability to obtain labels in the target domain forces the adoption of asymmetric optimization losses, resulting in a challenging issue of source domain bias. To overcome these limitations, we propose a novel unsupervised domain adaptation framework for 3D object detection via collaborating ST and AL, dubbed as STAL3D, unleashing the complementary advantages of pseudo labels and feature distribution alignment. Additionally, a Background Suppression Adversarial Learning (BS-AL) module and a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain scenes, effectively alleviating the issues of the large proportion of background interference and source domain size bias. Our STAL3D achieves state-of-the-art performance on multiple cross-domain tasks and even surpasses the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$ KITTI-rain. IEEE

关键词： Feature extraction

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TH-SLP: Web Service Link Prediction Based on Topic-aware Heterogeneous Graph Neural Network

TH-SLP: Web Service Link Prediction Based on Topic-aware Het...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Peng, Qian Cao, Buqing Xie, Xiang Liu, Shanpeng Kang, Guosheng Liu, Jianxun Hunan University of Science and Technology Xiangtan School of Computer Science and Engineering Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan China

ISBN: (纸本)9798350304855

With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method. © 2023 IEEE.

关键词： Semantics

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Exercise-induced adaptation of neurons in the vertebrate locomotor system

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Journal of Sport and Health Science 2024年第2期13卷 160-171页

作者： Yue Dai Yi Cheng Renkai Ge Ke Chen Liming Yang Key Lab of Adolescent Health Assessment and Exercise Intervention of Ministry of Education College of Physical Education and Health CareEast China Normal UniversityShanghai 200241China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic EngineeringEast China Normal UniversityShanghai 200241China School of Physical Education and Health Care East China Jiaotong UniversityNanchang 330013China Key Laboratory of High Confidence Software Technologies of Ministry of Education School of Computer SciencePeking UniversityBeijing 100871China

Vertebrate neurons are highly dynamic cells that undergo several alterations in their functioning and physiologies in adaptation to various external *** particular,how these neurons respond to physical exercise has long been an area of active *** of the vertebrate locomotor system’s adaptability suggest multiple mechanisms are involved in the regulation of neuronal activity and properties during *** this brief review,we highlight recent results and insights from the field with a focus on the following mechanisms:(a)alterations in neuronal excitability during acute exercise;(b)alterations in neuronal excitability after chronic exercise;(c)exercise-induced changes in neuronal membrane properties via modulation of ion channel activity;(d)exercise-enhanced dendritic plasticity;and(e)exercise-induced alterations in neuronal gene expression and protein *** hope is to update the community with a cellular and molecular understanding of the recent mechanisms underlying the adaptability of the vertebrate locomotor system in response to both acute and chronic physical exercise.

关键词： Dendritic plasticity Excitability Exercise Ion channel modulation Neuron adaptation

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Concrete Structural Crack Damage Classification Using Nonlinear Dimension Reduction and Broad Learning System

Concrete Structural Crack Damage Classification Using Nonlin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Bingshu Lin, Jia Zhuang, Xiaodong Zhang, Guanghui Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi'an China Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781665410205

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy. © 2024 IEEE.

关键词： Deep neural networks

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ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第4期3卷 526-540页

作者： Sun, Yanan Yen, Gary G. Xue, Bing Zhang, Mengjie Lv, Jiancheng Sichuan University College of Computer Science Chengdu610065 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74078 United States Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand

The superiority of convolutional neural networks (CNNs) largely relies on their architectures that are usually manually crafted with extensive human expertise. Unfortunately, such kind of domain knowledge is not necessarily owned by every interested user. Data mining on existing CNNs can discover useful patterns and fundamental comments from their architectures, providing researchers with strong prior knowledge to design effective CNN architectures when they have no expertise in CNNs. There are various state-of-the-art data mining algorithms at hand, while there is only rare work on mining CNN architectures. One of the main reasons is the gap between CNN architectures and data mining algorithms. Specifically, the current CNN architecture descriptions cannot be exactly vectorized to feed to a data mining algorithm. In this article, we propose a unified approach, named ArcText, to describing CNN architectures based on text. Particularly, four different units and an ordering method have been elaborately designed in ArcText, to uniquely describe the same CNN architecture with sufficient information. Also, the resulted description can be exactly converted back to the corresponding CNN architecture. ArcText bridges the gap between CNN architectures and data mining researchers, and has the potential to be utilized to wider scenarios. © 2020 IEEE.

关键词： Data mining

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SP-Net: A Sparse Convolution and Point-Encoding Enhanced Network for 3D Object Detection in LiDAR Point Clouds

SP-Net: A Sparse Convolution and Point-Encoding Enhanced Net...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Liu, Meng Niu, Jianwei Liu, Yu China North Vehicle Research Institute Beijing100072 China Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing100191 China Beihang University School of Computer Science and Engineering Beijing100191 China

ISBN: (纸本)9798350327182

LiDAR 3D object detection for autonomous driving is an important issue. To address this issue, this paper provides a two-stage anchor-based solution. Firstly, voxel feature encoding and sparse convolution networks were employed for proposal generation. Secondly, we performed voxel feature aggregation and hierarchical feature learning in combination with the proposals, multi-level and multiscale voxel features, point encoding and voxel-wise ROI pooling. The aggregated object-wise voxel features were utilized to refine the proposals. Extensive experiments were conducted on three challenging datasets: KITTI, nuScenes and Waymo. Experimental results demonstrated that our SP-Net can effectively achieve multi-category 3D object detection in diverse scenarios offering satisfactory accuracy and speed (28.6 FPS). © 2023 IEEE.

关键词： Object detection

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