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检索条件"任意字段=8th International Conference on Parallel and Distributed Computing, Applications and Technologies"

共 1804 条记录，以下是21-30 订阅

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Proceedings - 16th IEEE international Symposium on parallel and distributed Processing with applications, 17th IEEE international conference on Ubiquitous computing and Communications, 8th IEEE international conference on Big Data and Cloud computing, 11th IEEE international conference on Social computing and Networking and 8th IEEE international conference on Sustainable computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018

Proceedings - 16th IEEE International Symposium on Parallel ...

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16th IEEE international Symposium on parallel and distributed Processing with applications, 17th IEEE international conference on Ubiquitous computing and Communications, 8th IEEE international conference on Big Data and Cloud computing, 11th IEEE international conference on Social computing and Networking and 8th IEEE international conference on Sustainable computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018

ISBN: (纸本)9781728111414

the proceedings contain 161 papers. the topics discussed include: deep learning for phishing detection;a partition matching method for optimal attack path analysis;an energy and robustness adjustable optimization method of file distribution services;deriving the political affinity of twitter users from their followers;on the usability of big (social) data;re-running large-scale parallel programs using two nodes;predicting hacker adoption on darkweb forums using sequential rule mining;an on-the-fly scheduling strategy for distributed stream processing platform;deadlock-free adaptive routing based on the repetitive turn model for 3D network-on-chip;and radix: enabling high-throughput georeferencing for phenotype monitoring over voluminous observational data.

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Fast Approximation for Scheduling Malleable Jobs on parallel Batch Machines with Rejection 25th

Fast Approximation for Scheduling Malleable Jobs on Parall...

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25th international conference on parallel and distributed computing, applications and technologies, PDCAT 2024

作者： Xia, Fenghe Guo, Longkun Zhang, Xiaoyan School of Mathematics and Statistics Fuzhou University Fuzhou350116 China School of Mathematical Science Institute of Mathematics and Ministry of Education Key Laboratory of NSLSCS Nanjing Normal University Nanjing210023 China

ISBN: (纸本)9789819642069

the rapid growth of cloud computing has brought new challenges in parallel Batch Machine Scheduling (PBMS), particularly when incorporating malleability and rejection constraints. this has led to the parallel Batch Machine Scheduling with Malleability and Rejection problem (PBMSMR), which involves malleable jobs whose widths can be adjusted during execution within specified limits and incorporates job rejection subject to a penalty threshold. Based on an analysis of key properties of batch scheduling with job rejection, we develop an approximation algorithm for PBMSMR by employing a greedy approach that reorders and iteratively refines job sets to minimize the objective while respecting rejection constraints. the algorithm achieves a time complexity of O(n2logn) and an approximation ratio of (4-2Km), where K and m denote the capacities and the number of the machines, respectively. For jobs with identical release times, we fine-tune the algorithm to achieve an approximation ratio of (3+2(K-1)Km). Additionally, for PBMS with two machines of non-identical capacities and fixed-width jobs, we achieve a ratio of 175 with O(nlogn), improving upon the previous state-of-the-art approximation ratio of 5 with a runtime of O(n2). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Job shop scheduling

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Enhancing Electric Vehicle Charging Systems with distributed Edge Intelligence 16

Enhancing Electric Vehicle Charging Systems with Distributed...

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16th international conference on Ambient Systems, Networks and technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Rahmani, Rahim Stojcevska, Biljana Department of Computer and Systems Sciences Stockholm University Sweden American University of Europe Skopje North Macedonia

the rapid proliferation of electric vehicles (EVs) and the corresponding expansion of charging systems, driven by global regulatory mandates, present significant challenges to the electric grid. these systems also generate massive volumes of heterogeneous, distributed data, necessitating advanced edge-cloud computing solutions. Deploying distributed Edge Intelligence (DEI) offers a transformative approach by contextualizing raw data, minimizing dependency on centralized resources, enhancing task performance through experience-based learning, predicting outcomes under uncertainties, optimizing data routing, and enabling self-organization of networked devices. Traditional centralized machine learning approaches are often unsuitable due to high communication costs, low reliability, legal constraints, and scalability limitations. In this context, Federated Learning (FL) emerges as a promising solution, enabling distributed machine learning models to be trained locally on decentralized data while preserving privacy and reducing communication overhead. this paper provides a comprehensive analysis of the key challenges associated with EV charging systems, including scalability, security, and privacy concerns. It also explores the integration of blockchain technology with FL at distributed edges to enhance system efficiency, reliability, and privacy. By addressing these challenges, the study outlines a framework for sustainable EV adoption and efficient charging infrastructure deployment in the context of evolving technological and regulatory landscapes. © 2025 the Author(s).

关键词： Blockchain Charging Systems distributed Edge Intelligence Electric Vechicle smart contract

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Proceedings of the 8th IEEE/ACM international conference on Big Data computing, applications and technologies, BDCAT 2021

Proceedings of the 8th IEEE/ACM International Conference on ...

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8th IEEE/ACM international conference on Big Data computing, applications and technologies, BDCAT 2021

ISBN: (纸本)9781450391641

the proceedings contain 15 papers. the topics discussed include: evaluating serverless architecture for big data enterprise applications;3D object recognition for virtual reality based digital twins;linking user accounts across social media platforms;crowd counting using deep learning in edge devices;multiscale clustering based diffusion representation learning method;a proactive data-parallel framework for machine learning;distributed orchestration of regression models over administrative boundaries;attribute network embedding method based on joint clustering of representation and network;and comparative analysis of pre-trained deep neural networks for vision-based security systems on a novel dataset.

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distributed k-Hop Query Powered by an Asynchronous Framework 25th

Distributed k-Hop Query Powered by an Asynchronous Framewo...

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25th international conference on Web Information Systems Engineering, WISE 2024

作者： Zheng, Jinlong Lu, Yujie Zheng, Weiguo School of Data Science Fudan University Shanghai China

ISBN: (纸本)9789819605781

the k-hop query represents a fundamental challenge in various graph applications, often supported by numerous distributed systems. the conventional approach to this query paradigm typically involves iterative layer-by-layer expansion, which inevitably increases redundant vertices as the hop count grows. Moreover, issues such as master node idle waiting and workload imbalances across computing nodes contribute to the wastage of computational resources and time. To tackle these challenges, this paper introduces an asynchronous framework for k-hop queries, namely ADFQ, designed for seamless integration into any distributed system. Diverging from the traditional vertex-centric techniques, this framework employs distributed machines to store graph blocks and process queries without waiting during deduplication between hops. By ensuring comprehensive deduplication at each hop’s conclusion and efficiently expanding frontiers, the framework significantly improves query performance. Experimental results demonstrate superior performance compared to the classic Bulk Synchronous parallel paradigm, particularly as graph imbalances intensify. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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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...

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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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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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Two-Phase distributed Algorithm for Solving the Bi-Objective Minimum Spanning Tree Problem: A Preliminary Study 15th

Two-Phase Distributed Algorithm for Solving the Bi-Objecti...

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15th international conference on parallel Processing and Applied Mathematics, PPAM 2024

作者： Amorosi, Lavinia Cairo, Mariagrazia Dell’Olmo, Paolo Di Rocco, Lorenzo Ferraro Petrillo, Umberto Department of Statistical Sciences Università di Roma "La Sapienza" P.le Aldo Moro 5 Rome00185 Italy

ISBN: (纸本)9783031856990

In this paper we present a novel distributed algorithm for solving the Bi-Objective Minimum Spanning Tree (BMST) problem using a two-phase method. the proposed approach leverages the MapReduce computing paradigm to define a distributed version of the most computational intensive part of this method, the second phase. In this phase, a recursive algorithm explores non-supported non-dominated points by evaluating spanning trees within specified regions in the objective space. the distributed nature of our approach allows parallel processing of multiple triangles in the objective space aiming at reducing the execution time and improving the scalability performance. Our preliminary experimental results, conducted using an HPC infrastructure, show that the improvements in the scalability performance when tasks show a balanced processing time are non-negligible but also highlight that a simple distribution of processes among the nodes of the distributed system is not enough to always achieve a good parallelization. Our analysis provided us with useful insights to achieve efficiency and effectiveness of this approach with respect to its sequential counterpart, pointing out its potential for practical applications in multi-objective optimization. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MapReduce

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parallel computing technologies: 8th international conference, PaCT 2005. Proceedings

Parallel Computing Technologies: 8th International Conferenc...

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8th international conference on parallel computing technologies, PaCT 2005

the proceedings contain 41 papers from the parallel computing Tecnologies: 8th international conference, PaCT 2005. the topics discussed include: on evaluating the performance of security protocols;timed equivalence for timed event structures;similarity of generalized resources in petri nets;real-time event structures and Scott domains;early-stopping k-set agreement in synchronous systems prone to any number of process crashes;allowing atomic objects to coexist with sequentially consistent objects;an approach to the implementation of the dynamical priorities method;information flow analysis for VHDL;and composing fine-grained parallel algorithms for spatial dynamics simulation.

关键词： parallel processing systems

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DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies 20th

DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Frie...

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

作者： Guo, Mingfeng Deng, Liang Dai, Zhe Li, Ruitian Lin, Gaofeng Liu, Jie Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819628292

Sparse triangular solve (SpTRSV) is a vital component in various scientific applications, and numerous GPU-based SpTRSV algorithms have been proposed. Synchronization-free SpTRSV is currently the mainstream algorithm on GPU due to its short preprocessing time and outstanding performance. However, we observed that this algorithm still has two performance bottlenecks. Firstly, the thread-level parallel mode can introduce to thread divergence issues within GPU warps during the writing phase. Secondly, the thread-level and warp-level fusion mode may struggles to fully exploit GPU resources due to suboptimal mapping relationships between rows and threads. To address these issues, this paper proposes DaCPSpTRSV, a new synchronization-free algorithm with GPU-friendly data communication and parallelism strategies. Specifically, we first develop a fast-forward thread-level approach, incorporating an efficient global memory access pattern and a light-weight dependency control mechanism, to optimize data communication and alleviate thread divergence. A fine-grained fusion strategy is then proposed to maximize GPU parallelism by adaptively selecting the suitable thread-level or warp-level modes. Moreover, the commonly-used compressed sparse row (CSR) format is employed in our DaCPSpTRSV, enhancing the versatility of our algorithm. We evaluate our approach using 245 matrices from the SuiteSparse Matrix Collection on two NVIDIA GPUs, demonstrating speedup ratios of up to 4.77×, 4.94×, 1.67×, and 1.62× compared to cuSPARSE, Sync-Free, CapelliniSpTRSV, and YuenyeungSpTRSV, respectively. the project is open-sourced at https://***/gmfff12334/DaCP. © IFIP international Federation for Information Processing 2025.

关键词： Graphics processing unit

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