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GCSS:a global collab.rative scheduling strategy for wide-area high-performance computing

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 collab.rative scheduling strategy for wide-area high-performance computing *** collab.rative 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 collab.rative 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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Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

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Journal of computer science & Technology 2022年第5期37卷 1026-1048页

作者： She Sun Shuai Ma Jing-He Song Wen-Hai Yue Xue-Lian Lin Tiejun Ma State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang UniversityBeijing 100191China Department of Decision Analytics and Risk Southampton Business SchoolUniversity of Southampton Southampton SO171BJU.K

With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices,trajectory data is continuously *** large-scale trajectories provide opportunities for various applications,the locations in trajectories pose a threat to individual ***,there has been an interesting debate on the reidentifiability of individuals in the science *** main finding of Sánchez et *** exactly opposite to that of De Montjoye et al.,which raises the first question:"what is the true situation of the privacy preservation for trajectories in terms of reidentification?''Furthermore,it is known that anonymization typically causes a decline of data utility,and anonymization mechanisms need to consider the trade-off between privacy and *** raises the second question:"what is the true situation of the utility of anonymized trajectories?''To answer these two questions,we conduct a systematic experimental study,using three real-life trajectory datasets,five existing anonymization mechanisms(i.e.,identifier anonymization,grid-based anonymization,dummy trajectories,k-anonymity andε-differential privacy),and two practical applications(i.e.,travel time estimation and window range queries).Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories,and essentially close the debate between De Montjoye et *** Sánchez et *** the best of our knowledge,this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.

关键词： anonymization privacy reidentification trajectory utility

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Energy-Efficient Task Offloading in UAV-Enabled MEC via Multi-agent Reinforcement Learning 18th

Energy-Efficient Task Offloading in UAV-Enabled MEC via Mu...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Gao, Jiakun Zhang, Jie Xu, Xiaolong Qi, Lianyong Yuan, Yuan Li, Zheng Dou, Wanchun School of Software Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China QingDao266580 China School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Zhongguancun Laboratory Beihang University Beijing100191 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819998951

Nowadays, artificial intelligence-based tasks are imposing increasing demands on computation resources and energy consumption. Unmanned aerial vehicles (UAVs) are widely utilized in mobile edge computing (MEC) due to maneuverability and integration of MEC servers, providing computation assistance to ground terminals (GTs). The task offloading process from GTs to UAVs in UAV-enabled MEC faces challenges such as workload imbalance among UAVs due to uneven GT distribution and conflicts arising from the increasing number of GTs and limited communication resources. Additionally, the dynamic nature of communication networks and workload needs to be considered. To address these challenges, this paper proposes a Multi-Agent Deep Deterministic Policy Gradient based distributed offloading method, named DMARL, treating each GT as an independent decision-maker responsible for determining task offloading strategies and transmission power. Furthermore, a UAV-enabled MEC with Non-Orthogonal Multiple Access architecture is introduced, incorporating task computation and transmission queue models. In addition, a differential reward function that considers both system-level rewards and individual rewards for each GT is designed. Simulation experiments conducted in three different scenarios demonstrate that the proposed method exhibits superior performance in balancing latency and energy consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Reinforcement learning

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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Fine-grained management of I/O optimizations based on workload characteristics

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Frontiers of computer science 2021年第3期15卷 33-46页

作者： Bing WEI Limin XIAO Bingyu ZHOU Guangjun QIN Baicheng YAN Zhisheng HUO State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

With the advent of new computing paradigms,parallel file systems serve not only traditional scientific computing applications but also non-scientific computing applications,such as financial computing,business,and public *** file systems provide storage services for multiple *** a result,various requirements need to be ***,parallel file systems usually provide a unified storage solution,which cannot meet specific application *** this paper,an extended tile handle scheme is proposed to deal with this *** original file handle is extended to record I/O optimization information,which allows file systems to specify optimizations for a file or directory based on workload ***,fine-grained management of I/O optimizations can be *** the basis of the extended file handle scheme,data prefetching and small file optimization mechanisms are proposed for parallel file *** experimental results show that the proposed approach improves the aggregate throughput of the overall system by up to 189.75%.

关键词： parallel file systems workload characteristics extended file handle data prefetching small files

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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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Spatial-Frequency Cross-Domain Mutual Guidance for Visible-Infrared Image Fusion

Spatial-Frequency Cross-Domain Mutual Guidance for Visible-I...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhili Lin Biao Leng School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The primary objective of Visible-Infrared Image Fusion (VIF) is to combine the rich texture and color information from visible light images with the comprehensive thermal radiation data provided by infrared images. However, most current fusion algorithms focus solely on spatial domain feature transformations, which results in fused images lacking sufficient detail and failing to effectively preserve crucial details from the source images. In this paper, we propose Spatial-Frequency Mutual Guidance for VIF. The framework comprises two branches. Each branch reconstructs input features in the frequency and spatial domains. We introduce a novel cross-domain mutual guidance mechanism. It fully integrates information between the frequency and spatial domains to enhance the detail quality of fused images. Furthermore, a weight allocation network is utilized to adaptively assign importance to visible and infrared images based on scene characteristics. Experiments on three VIF datasets show that our method outperforms recent advanced algorithms.

关键词： Image color analysis Frequency-domain analysis Heuristic algorithms Signal processing algorithms Signal processing Resource management Speech processing Image fusion Image reconstruction Visual perception

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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dgQuEST: Accelerating Large Scale Quantum Circuit Simulation through Hybrid CPU-GPU Memory Hierarchies 18th

dgQuEST: Accelerating Large Scale Quantum Circuit Simulation...

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18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

作者： Feng, Tianyu Chen, Siyan You, Xin Zhong, Shuzhang Yang, Hailong Luan, Zhongzhi Qian, Depei State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9783030935702

With the advancement of quantum computing, verifying the correctness of the quantum circuits becomes critical while developing new quantum algorithms. Constrained by the obstacles of building practical quantum computers, quantum circuit simulation has become a feasible approach to develop and verify quantum algorithms. Although there are many quantum simulators availab.e, they either achieve low performance on CPUs, or limited simulation scale (e.g., number of qubits) on GPUs due to limited memory capacity. Therefore, we propose dgQuEST, a novel acceleration method that utilizes hybrid CPU-GPU memory hierarchies for large-scale quantum circuit simulation across multiple nodes. dgQuEST adopts efficient memory management and communication schemes to leverage the distributed CPU and GPU memories for accelerating large-scale quantum simulation. Our evaluation demonstrates that dgQuEST achieves an average speedup of 403 × compared to QuEST on quantum circuit simulation with 32 qubits, and scales to quantum circuit simulation with 35 qubits on two GPU nodes, far beyond the state-of-the-art implementation HyQuas can support. © 2022, IFIP International Federation for Information Processing.

关键词： Circuit simulation

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DRStencil: Exploiting Data Reuse within Low-order Stencil on GPU 23

DRStencil: Exploiting Data Reuse within Low-order Stencil on...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： You, Xin Yang, Hailong Jiang, Zhonghui Luan, Zhongzhi Qian, Depei State Key Laboratory of Software Development Environment Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665494571

Stencil computation is widely adopted in scientific applications as one of the most significant computation patterns. Although there are various optimizations proposed to accelerate the stencil computation, the low-order stencil still suffers from limited performance on GPU due to its low computation inten-sity. In this paper, we propose the fusion-partition optimization techniques to accelerate the low-order stencil computation and implement an effective code generation framework DRStencil to automatically generate optimized stencil codes with fusion-partition applied. Specifically, we adopt a four-stage optimization workflow such as time-fusion, partition, forward and backward computation. We also propose an auto-tuning method to deter-mine the optimal parameter settings of the generated stencil codes. We evaluate DRStencil with representative low-order stencils on Nvidia P100, V100, and A100 GPUs. Our evaluation results achieve 1.46 x, 1.59 x, and 1.10 x speedup on average for widely used low-order stencils compared to the state-of-the-art implementations on P100, V100, and A100 GPUs, respectively. © 2021 IEEE.

关键词： Codes Smart cities Graphics processing units Manuals Parallel processing Computational efficiency Optimization

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