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

共 34220 条记录，以下是121-130 订阅

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A Massive Open Online Course (MOOC) on High-Performance parallel computing for *** Web-learning Platform

A Massive Open Online Course (MOOC) on High-Performance Para...

引用

Euromicro conference on parallel, distributed and Network-Based Processing

作者： Giuliano Laccetti Marco Lapegna Ilaria Merciai dept. of Mathematics and Applications Univ. of Naples Federico II Naples Italy Federica Web Learning Univ. of Naples Fedrico II Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Massive Open Online Courses (MOOCs) represent an accessible and user-friendly tool for disseminating innovative and cutting-edge topics to broad segments of civil society via online learning platforms, enabling users to learn at their own pace and on their own schedule. In this contribution, we describe the design and the implementation of a Massive Open Online Course on parallel computing and High-Performance computing, developed for Federica Web Learning: the University Center for innovation, experimentation, and dissemination of multimedia teaching at the University of Naples Federico II.

关键词： Training Computer aided instruction Technological innovation Schedules Electronic learning High performance computing Multimedia computing parallel processing Character recognition Videos

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Optimizing Cloud computing Task Schedules Through Advanced Intelligent Optimization Methods

Optimizing Cloud Computing Task Schedules Through Advanced I...

引用

international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

作者： Ammari, Ahmed Chiheb Al Hmouz, Rami Khriji, Lazhar Zhou, MengChu Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9789819747832

The number of distributed Green Cloud Datacenters (DGCDs) is globally increasing. Such DGCDs deploy different types of renewable sources to generate clean energy and save money. They are located in different regions depending on the availability of renewable energy sources, costs of bandwidth and grid electricity prices. This paper focuses on applications within them, which are sensitive to delay and looks into a way to schedule different applications with respect to time constraint. The paper uses the Firefly algorithm, bat algorithm and simulated annealing-bat algorithms as optimization techniques to minimize total operational cost of such DGCDs. These algorithms have been compared through data-driven experiments carried out in this study. Of particular note is the Firefly algorithm's superior performance when compared with others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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HPC master design: experience from Pisa

HPC master design: experience from Pisa

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Marco Danelutto Dept. of Computer Science Univ. of Pisa

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware resources require application of HPC techniques when using much smaller machines. Indeed, proper parallel programming tools and applications are needed also to exploit parallel hardware resources in personal computers (laptops, desktops, servers). This paper outlines key challenges in designing master’s degree programs in HPC and shares lessons learned from various experiences in developing and implementing such programs in Italy and Europe.

关键词： Portable computers parallel programming High performance computing Europe parallel machines Hardware Telecommunications Stakeholders Servers Programming profession

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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...

引用

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. Beijing 102209 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 computing grid engineering optimization algorithm survey data

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Dynamic Bio-Information Image Recognition of Cancel Cell Death based on parallel Multiple Artificial Intelligence Modules 27

Dynamic Bio-Information Image Recognition of Cancel Cell Dea...

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27th international conference on Advanced Communications Technology, ICACT 2025

作者： Jian, Ming-Shen Ye, Yi-Ling Chung, Wen-Yu Li, Wen-Xuan Lin, Yan-Ting Huang, Bo-Yuan Sikandar, Javeria Chang, Yu-Jung CCIS Lab. Dept. of CSIE National Formosa University Yunlin632 Taiwan Dept. of Biotechnology National Formosa University Yunlin632 Taiwan

ISBN: (纸本)9791188428137

This investigation purpose a unique Dynamic Bio-Information Image Recognition System (DBIRS) built for the real-time monitoring of cancer cell apoptosis produced by available medical therapy. Utilizing advances in microscopy, AI-based image recognition, and parallel computing across distributed edge devices, the proposed system dramatically boosts the precision and efficiency of cellular investigation. By deploying numerous AI modules adapted to distinct cellular states, the system provides automatic, high-accuracy recognition of morphological changes in cancer cells, hence minimizing the need on manual procedures. This technique offers remote monitoring and fast data processing via cloud integration, permitting continuous observation and timely intervention. The system's efficiency is proven through trials on K-562 cells, where it attained a recognition accuracy of 97.41%. These outcomes underline the potential of the proposed method to refine cytotoxicity assays, delivering a strong tool for increasing the efficacy and safety of immune cell treatments in clinical and research settings. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Diseases

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A Novel Consensus Mechanism Based on Dynamic Sharding 20th

A Novel Consensus Mechanism Based on Dynamic Sharding

引用

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

作者： Zhang, Jingyu Teng, Yilong Sun, Yongtao Zhu, Shi Yang, Fangliao Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China Data Operations Center State Grid Hunan Electric Power Information and Communication Corporation Hunan Changsha China

ISBN: (纸本)9789819628292

Blockchain, as an emerging technology, has been widely studied by the researchers from academia and industry. Alliance chain, as an important form of blockchain, is often applied to smart grids, smart city and the Internet of Things to ensure the authenticity and reliability of data. Practical Byzantine Fault Tolerance (PBFT) algorithm is a key technology to ensure data consistency in the alliance chains, and it plays a critical role in improving system performance, security and scalability. However, with the increase of the number of nodes and transactions, the ability of PBFT to process transactions is challenged. In particular, PBFT’s linear transaction processing also limits its ability to process transactions in parallel. In view of the problems existing in PBFT, this paper studies the consensus mechanism optimization, and puts forward a consensus mechanism with better performance. In this paper, in order to solve the problem of weak parallel transaction processing capability of PBFT, we propose a novel Consensus Mechanism based on Dynamic Sharding (CMDS). CMDS firstly constructs a dynamic fragmentation model based on consistent hash algorithm to segment nodes, and then constructs a voting consensus mechanism based on reputation weight to achieve global state consistency. The experimental results show that CMDS can significantly improve the ability of parallel transaction processing and the performance of blockchain system. © IFIP international Federation for Information Processing 2025.

关键词： Blockchain

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Research on Big Data distributed computing Model and Algorithm Optimization for Complex Network Analysis

Research on Big Data Distributed Computing Model and Algorit...

引用

Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Aiting Sun Liaoning Vocational College of light industry Liaoning China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributed computing model in complex network analysis in detail, and focuses on the distributed computing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributed computing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.

关键词： Analytical models Accuracy Computational modeling Memory management Complex networks Big Data Data models distributed computing Detection algorithms Optimization

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LDChain: A Lightweight and Scalable Blockchain System for Dynamic IoT Scenarios 20th

LDChain: A Lightweight and Scalable Blockchain System for ...

引用

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

作者： Wang, Jianrong Cao, Yuxuan Hu, Dengcheng Li, Qi Li, Sen Li, Xuewei Liu, Xiulong College of Intelligence and Computing Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China School of Future Technology Tianjin University Tianjin China

ISBN: (纸本)9789819628292

As a distributed ledger, blockchain inherently guarantees data consistency. However, when applied to the Internet of Things (IoT), existing solutions employing blockchain systems struggle to address the scalability demands posed by the dynamic mobility of heterogeneous devices. To this end, we propose Lightweight DAG Chain (LDChain), a lightweight and scalable blockchain system tailored for IoT scenarios. LDChain connects weak nodes and strong nodes through a hierarchical DAG consensus algorithm. For weak nodes, we introduce a transaction-based DAG consensus mechanism utilizing a snapshot synchronization strategy to cater to the dynamic nature of weak nodes and the scalability demands of the system. The integrated snapshot synchronization strategy, based on the second latest block transaction selection algorithm, reduces the storage and computational overhead for resource-restricted devices. For strong nodes, we devise a block-based DAG consensus mechanism and a repackaging strategy, which resolves transaction conflicts across regions by managing conflicting transactions through a priority queue. We implement a prototype of LDChain and conduct experiments in a network environment comprising 10 cloud servers, simulating several strong nodes and weak nodes. The experimental results show that our system achieves a throughput exceeding 7000 transactions per second (TPS) and a transaction confirmation latency of 1.3 s with 100 nodes. © IFIP international Federation for Information Processing 2025.

关键词： Scalability

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Partial Order-centered Hyperbolic Representation Learning for Few-shot Relation Extraction 31

Partial Order-centered Hyperbolic Representation Learning fo...

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31st international conference on Computational Linguistics, COLING 2025

作者： Hu, Biao Huang, Zhen Hu, Minghao Yang, Pinglv Qiao, Peng Dou, Yong Wang, Zhilin National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China Center of Information Research Academy of Military Science China College of Meteorology and Oceanology National University of Defense Technology China

ISBN: (纸本)9798891761964

Prototype network-based methods have made substantial progress in few-shot relation extraction (FSRE) by enhancing relation prototypes with relation descriptions. However, the distribution of relations and instances in distinct representation spaces isolates the constraints of relations on instances, making relation prototypes biased. In this paper, we propose an end-to-end partial order-centered hyperbolic representation learning (PO-HRL) framework, which imposes the constraints of relations on instances by modeling partial order in hyperbolic space, so as to effectively learn the distribution of instance representations. Specifically, we develop the hyperbolic supervised contrastive learning based on Lorentzian cosine similarity to align representations of relations and instances, and model the partial order by constraining instances to reside within the Lorentzian entailment cone of their respective relation. Experiments on three benchmark datasets show that PO-HRL outperforms the strong baselines, especially in 1-shot settings lacking relation descriptions. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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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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