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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34218 条记录，以下是131-140 订阅

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Research on optimization algorithm in grid engineering survey data acquisition and processing based on cloud computing 5

Research on optimization algorithm in grid engineering surve...

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5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Zhang, Jiyong Sun, Xiaohu Huang, Bing Liu, Donghui Ren, Peixiang State Grid Economics and Technology Research Institute Co. Ltd. Beijing102209 China

ISBN: (纸本)9781510691667

With the increasing scale of power grids and the growing demand for intelligence, efficient and accurate collection and processing of grid engineering survey data has become critical. This study aims to explore the optimization application of cloud computing technology in grid engineering data collection and processing. We design and implement an optimization algorithm based on cloud computing, which integrates several technologies such as distributed computing, big data storage and management, cluster monitoring and security management. Through comparative experiments, our algorithm significantly outperforms traditional methods in terms of data processing speed, achieving at least a four-fold improvement;in terms of data accuracy, the error rate is controlled at a lower level of 0.2%-0.3%. In addition, our method demonstrates good scalability and cost-effectiveness. This study not only provides an efficient data processing solution for power grid engineering, but also lays a solid foundation for the intelligent and digital transformation of future power grids. © 2025 SPIE.

关键词： Cloud storage

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parallel Acceleration of Transportation Problem Solving Using SpMV in the Industrial Chain Context 25th

Parallel Acceleration of Transportation Problem Solving Usi...

引用

25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Dong, De Lin, Guanyu Xu, Yicheng Ma, Ruikang Xia, Chenliang Fan, Jianping Li, Fuchong Southern University of Science and Technology Guangdong Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China Jinan Industrial Development Park Technology Development Co. Ltd. Shandong Jinan China

ISBN: (纸本)9789819642069

As modern industrial chains grow increasingly complex and time-sensitive, traditional transportation planning methods face efficiency bottlenecks. To address this, we propose a parallelization method based on Sparse Matrix-Vector Multiplication (SpMV) to accelerate the Transportation Simplex Algorithm (TSA) for large-scale transportation problems. Existing approaches primarily leverage data parallelism but underutilize GPU computational potential. This study transforms sequential algorithms into matrix-based representations, combining SpMV and parallel graph algorithms to enhance TSA performance. Key contributions include mapping performance bottlenecks—potential vector calculation and closed-loop search—onto SpMV operations, and converting branch logic into matrix operations to reduce divergence. Experiments on a 10000×10000 dataset demonstrate a 20.8x speedup for the parallelized MODI algorithm and a 28x overall speedup for solving the Transportation Problem, significantly improving efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

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Vickrey Auction Offloading for Edge-Assisted Video Analytics with Dynamic Gain Prediction 20th

Vickrey Auction Offloading for Edge-Assisted Video Analytic...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Du, Mei Tan, Xiaobin Pan, Yaying Wang, Mingyang Wang, Shunyi Zheng, Quan University of Science and Technology of China Hefei China Institute of Artificial Intelligence HCNSC Hefei China

ISBN: (纸本)9789819628636

Mobile Edge computing (MEC) with offloading from local light models to edge heavy models offers a promising approach to meeting the high processing quality and low latency demands of video analytics while reducing overall system resource consumption. However, significant challenges remain in predicting offloading gains and ensuring effective multi-device collaboration under dynamic video content and network conditions, as existing methods struggle with accuracy prediction, timeliness, and resource allocation flexibility. In this paper, we present a Tile Confidence Gains-inspired Vickrey Auction-based Offloading System (TCGVA), designed to optimize latency-decayed offloading accuracy gains under network and computation constraints. To dynamically and efficiently predict offloading improvements, the system leverages historical gains thresholds feedback from edge batch processing, incorporating recent offloading intervals. A distributed Vickrey auction-based strategy is proposed to optimize resource allocation among multiple devices, enabling quick adaptation to content and network fluctuations. Our implemented system, tested on the VisDrone dataset with HSDPA network traces, achieves 93.28% of the ideal accuracy gain while maintaining prediction speeds close to the greedy method, with 62.36% less bandwidth occupancy and 16.49% fewer latency violations under fluctuating conditions. © IFIP international Federation for Information Processing 2025.

关键词： Mobile edge computing

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Advances on P2P, parallel, grid, Cloud and Internet computing: Proceedings of the 11th international conference 1

引用

丛书名： Lecture Notes on Data Engineering and Communication Technologies 1

2016年

作者： Fatos Xhafa, Leonard Barolli, Flora Amato (eds.)

ISBN: (纸本)3319491083;9783319491080

P2P, grid, Cloud and Internet computing technologies have been very fast established as breakthrough paradigms for solving complex problems by enabling aggregation and sharing of an increasing variety of distributed computational resources at large scale. The aim of this volume is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to P2P, grid, Cloud and Internet computing as well as to reveal synergies among such large scale computing paradigms. This proceedings volume presents the results of the 11th international conference on P2P, parallel, grid, Cloud And Internet computing (3PGCIC-2016), held November 5-7, 2016, at Soonchunhyang University, Asan, Korea

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LSSM-SpMM: A Long-Row Splitting and Short-Row Merging Approach for parallel SpMM on PEZY-SC3s 24th

LSSM-SpMM: A Long-Row Splitting and Short-Row Merging Appro...

引用

24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Cao, Ligang Wang, Qinglin Yang, Shun Xia, Rui Guo, Weihao Liu, Jie Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615506

Sparse Matrix-Dense Matrix Multiplication (SpMM) is a crucial kernel used in a wide range of fields including machine learning and linear algebra solvers. Thus, enhancing the performance of SpMM is essential. The uneven distribution of non-zeros in sparse matrices and the tight data dependency between sparse and dense matrices make efficiently running SpMM on various hardware platforms challenging. To address these issues, optimisations are tailored according to the characteristics of the different hardware platforms. In this study, we propose a Long Row Split and Short Row Merge (LSSM) approach on the new MIMD computing platform PEZY-SC3s, utilising the standard Compressed Sparse Row (CSR) format to optimise SpMM. Specifically, LSSM divides the rows of the sparse matrix into short rows (rows with a number of non-zeros less than blockSize) and long rows (rows with a number of non-zeros greater than blockSize), applying splitting to the long rows and merging to the short rows to optimize their computations separately. Additionally, based on the hardware features of PEZY-SC3s, we employed the atomic cache for workload scheduling, SIMD instructions to accelerate computation, and Local Memory to reduce data read-write operations, addressing issues of poor data locality, workload imbalance, and vectorisation during SpMM execution. As the first study of SpMM on PEZY-SC3s, compared with BS-SpMM and RoDe-SpMM implemented on PEZY-SC3s, LSSM-SpMM offers up to 26.17× and 13.59× acceleration on the SuiteSparse and deep learning datasets, respectively, with geometric mean speedups of 1.56× and 1.71×. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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A Cluster-Based Platoon Formation Scheme for Realistic Automated Vehicle Platooning 20th

A Cluster-Based Platoon Formation Scheme for Realistic Auto...

引用

20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Liu, Ziye Chen, Chen Zhang, Qizhong Wang, Zhiyi Choon Chang, Yoong Liu, Lei Pei, Qingqi Wan, Shaohua School of Telecommunications Engineering Xidian University Xi’an China State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun China Lee Kong Chian Faculty of Engineering & Science Universiti Tunku Abdul Rahman Kajang Bandar Sungai Long Malaysia Xidian Guangzhou Institute of Technology Xidian University Guangzhou China Xidian Hangzhou Institute of Technology Xidian University Hangzhou China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)9789819628292

In response to the burgeoning interest in Vehicle-to-Everything (V2X) communications and its pivotal role in enhancing road transportation efficiency and safety, this paper presents a novel scheme for the formation of vehicle platoons, addressing the critical need for realistic implementation strategies. Recognizing the limitations of existing research that often relies on idealized assumptions, this work proposes a platoon formation method utilizing the real-world dataset. The method combines the robustness of Random Forest algorithms for feature selection with the adaptability of Fuzzy K-Means (FKM) clustering to effectively group vehicles with similar characteristics into platoons. This approach ensures the consistency of speed and acceleration within platoons, a crucial aspect for maintaining platoon integrity and stability. Validated through the Kaggle dataset, the scheme not only showcases the feasibility of forming efficient vehicle platoons in realistic scenarios but also introduces a velocity and acceleration-based Cooperative Adaptive Cruise Control (CACC) strategy to prove the formatted platoon stability. © IFIP international Federation for Information Processing 2025.

关键词： K-means clustering

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Job Scheduler-Driven Power Gateway for High Performance computing

Job Scheduler-Driven Power Gateway for High Performance Comp...

引用

ISC High Performance 2025 Research Paper Proceedings (40th international conference)

作者： Matthew Sgambati Matthew Anderson Idaho National Laboratory Idaho USA

ISBN: (数字)9783982633619

Power gateways in the form of a microgrid can incorporate multiple distributed energy resources (DER) in either grid forming or grid following mode and support high performance computing (HPC) power profiles including the large load-follow requirements observed in multi-user HPC systems. The microgrid's flexibility to operate in either grid forming or grid following mode and to actively switch between these modes enables baseline power from multiple non-baseline DER while maintaining high power quality metrics for the HPC system. But this enormous flexibility in demand response and time of use shifting is generally programmed independently of any integration with an HPC job scheduler, which can better inform the load shaping by the microgrid. While there are many existing approaches where the HPC job scheduler takes in information from the grid to make queue scheduling decisions, this work takes the opposite view and explores three strategies where the forecast of jobs in the queue can change the settings of the grid. Three strategies are tested where the forecast of jobs in the queue adjusts the settings of a microgrid designed for a datacenter, in this case with three classes of HPC architectures. The strategies are demonstrated using a microgrid with 64 kW of solar capacity and 320 kWh of battery over a period of 21 days operating with significant load-follow swings, a throttled grid, cloudy conditions, switching between grid following and grid forming modes, and a wide range of battery states-of-charge, all while maintaining high quality power metrics. The strategies presented provide a mechanism for the forecast of jobs in the queue to influence and adjust the settings of a microgrid and to improve HPC power outcomes such as maximizing renewable energy usage.

关键词： Measurement Renewable energy sources High performance computing Microgrids Switches Logic gates distributed power generation Batteries Time factors grid forming

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Research on Cable Vibration Measurement System Based on Michelson Interference Principle 1

Research on Cable Vibration Measurement System Based on Mich...

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1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Kou, Hanpeng Bu, Tianlong Zhang, Dapei Nie, Dayu Feng, Zhenghua Huang, Kaiyang Mengdong Power Grid Company Hohhot120022 China Changchun University of Science and Technology Changchun130022 China

ISBN: (纸本)9781643685779

Extra-high voltage transmission lines and cables are subject to irregular vibration due to icing or unusual weather conditions, which is potentially hazardous to the safety of the towers and the structural strength of the wires. Most of the current monitoring methods use electronic acceleration sensors, which have low sensitivity, poor signal-to-noise ratio, troublesome installation process, need for additional power supply and other issues. distributed fibre optic vibration demodulation method is currently more advanced sensing technology, can achieve long distance, large scale, multi-dimensional online monitoring and structural safety warning. The laser fibre sensing distributed Michaelson coherent technology is used to develop a cable vibrate sensor, which can achieve the vibrate monitoring of cables and towers. The sensor consists of an elastic thin-walled cylinder, a mass ring, a fixed ring, and a sensing fibre, which uses a highly sensitive fine optical fibre and a coupler to build a laser-fibre Michelson coherent optical path, which is very sensitive to vibration signals, and encapsulates the interferometer into a specific metal casing to achieve the encapsulation and integration of the vibrate monitoring sensor. The optical system of this laser fibre sensor has the advantages of high sensitivity and high signal-to-noise ratio. Using the differential cross-multiplication demodulation algorithm for many experiments and results analysis, the demodulation output of the linear correlation coefficient of up to 0.95 in the frequency of 10Hz ~ 200Hz under the conditions of the vibrate, suitable for high-voltage towers and cables for online monitoring of the vibrate. © 2025 The Authors.

关键词： Optical fibers

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Securing Digital Integrity: Proposed Comprehensive Framework for Deepfake Detection and Blockchain Validation 5th

Securing Digital Integrity: Proposed Comprehensive Framework...

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Jain, Anant Gaur, Adamya Gupta, Gauranshi Mishra, Shubhangi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819773701

This paper proposes a comprehensive solution to combat the growing threat of Deepfake technology, employing Convolutional Neural Networks (CNNs) and Blockchain. CNNs analyze video frames for anomalies indicative of Deepfake manipulation, while Blockchain ensures content integrity through timestamping and IPFS hashes. Leveraging transfer learning with EfficientNet-B1 architecture and dropout layers to prevent overfitting, the CNN model attains a 98.53% accuracy on testing data. Smart contracts are utilized to store social media content and AI verification results. The methodology integrates private storage on social media platforms, AI-based CNN verification, Blockchain timestamping, smart contract verification, and continuous improvement. By synergizing AI and Blockchain technologies, our approach aims to bolster defenses against deceptive content proliferation, enhancing the reliability of online media ecosystems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles 18th

Recommendation-Aware Collaborative Edge Caching Strategy in...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ou, Pingjie Chen, Ningjiang Huang, Zizhan School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9783031714696

Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. This model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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