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

共 24638 条记录，以下是81-90 订阅

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Adaptive Gradient Data Partition and Route Selection for distributed DNN Training 20th

Adaptive Gradient Data Partition and Route Selection for D...

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

作者： Chai, Bo Tan, Xiaobin Yuan, Shenzhi Jia, Guangge Meng, Qiushi Wang, Weifeng Zhu, Shiyin University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China H3C Technologies Co. Limited Beijing China

ISBN: (纸本)9789819628636

As the scale of distributed training for Deep Neural Network (DNN) increases, communication has become a critical performance bottleneck in data center networks. In-Network Aggregation (INA) can accelerate aggregating gradients process, the critical communication bottleneck in distributed DNN training. Due to the limited processing capacity of network switch, existing aggregation approaches have turned to a combination of INA and Parameter Server (PS) methods. However, these approaches employ a fixed partitioning of the gradient data, leading to suboptimal performance. This paper proposes GPRS, an adaptive gradient aggregation framework for distributed DNN training, which adaptively combines the line-rate processing of INA and large memory capacity of PS. We present an adaptive gradient data partition scheme, which divides the gradient data into INA and PS aggregation. We propose a heuristic algorithm that determines the aggregation route selection by assigning priority to the available network paths. To validate our approach, we build a real-world experimental testbed and implement the proposed framework. Experimental results show that it can overcome the limitations of traditional INA-only methods and reduce communication time by 15% ∼ 52% for DNN training of varying scales. © IFIP international Federation for Information Processing 2025.

关键词： Data aggregation

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments 16

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

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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： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, Thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： Apriori Algorithm Big Data Cloud-Native Architectures Data Mining distributed computing Maritime Serverless computing

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PEbfs: Implement High-Performance Breadth-First Search on PEZY-SC3s 24th

PEbfs: Implement High-Performance Breadth-First Search on P...

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

作者： Guo, Weihao Wang, Qinglin Liu, Xiaodong Peng, Muchun Yang, Shun Liang, Yaling Shi, Yongzhen Cao, Ligang 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 Engineering Research Center for National Fundamental Software National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615506

The breadth-first search (BFS) algorithm is a fundamental algorithm in graph theory, and it’s parallelization can significantly improve performance. Therefore, there have been numerous efforts to leverage the powerful parallel computing capabilities of hardware like GPGPU to implement high-performance BFS algorithms. However, the energy efficiency is relatively low due to the high power consumption of the platforms on which the algorithm is adapted to. To deal with these challenges, this paper introduces PEbfs that is a high-performance BFS algorithm based on the PEZY-SC3s efficient processor. We integrated three search algorithms, two algorithm optimization strategies, and a directional optimization scheme into PEbfs. Through multiple evaluations of the performance of PEbfs on the public SNAP dataset, the results demonstrate that the average energy efficiency ratio of PEbfs is higher than that of Enterprise and Tigr, the two most advanced implementations on Nvidia’s GPGPU: It achieves 3.08× the average energy efficiency ratio of Enterprise and 4.53× that of Tigr. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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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...

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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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Edge and Fog computing in Cyber-Physical Systems

Edge and Fog Computing in Cyber-Physical Systems

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2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Gangrade, Priyanshi Kushwah, Virendra Singh Date, Saroj S. Jagdale, Archana D. Upreti, Kamal Singh, Vinai K. School of Cse and Ai Vit Bhopal University Sehore India Csmss Chh. Shahu college of Engineering Dept. of Ai and Ds Maharashtra Chh. Sambhajinagar India Barclays Services Corp 745 Seventh Avenue New York United States Department of Computer Science Christ University Delhi Ncr Ghaziabad India Gl Bajaj Group of Institutions Department of Computer Science & Engineering Mathura India

ISBN: (纸本)9798331527495

The benefits of cyber-physical system advances include low latency and high bandwidth data processing in areas such as automotive, healthcare, and business automation. Traditional environments are often located in centralized and remote locations and cannot meet the demand. Edge computing and cloud computing have become fundamental concepts that will bring computing closer to the center of the data. Edge computing can reduce latency and bandwidth consumption by processing data on or near IoT devices. Fog computing adds another layer to this by distributing work and storage across multiple nodes, thus providing a scalable and flexible infrastructure. This article discusses the principles, benefits, and challenges of integrating edge and cloud computing into a CPS environment. It leverages the power of proximity-based edge computing and the centralized capabilities of cloud computing to provide scalable, instantaneous responses to CPS applications or time to optimize services. The demonstration shows a variety of things from smart cities to the use of IoT in healthcare in CPS. The article also covers some specific security and privacy issues and future directions in distributed computing, including the role of AI and 5G, which are supposed to offer additional resources in various applications. © 2025 IEEE.

关键词： Data reduction

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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...

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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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distributed Event Triggered Secondary Control of Islanded Microgrid System

Distributed Event Triggered Secondary Control of Islanded Mi...

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advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： B.Chiranjeev Rao Shimpy Ralhan Department of Electrical Engineering Shri Shankaracharya Technical Campus Bhilai India Department of Electrical & Electronics Engineering Shri Shankaracharya Technical Campus Bhilai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The demand for decentralized control techniques in DC microgrids has increased due to their ability to enhance scalability, reduce communication requirements, and improve overall system efficiency. This paper presents a distributed event-triggered secondary control strategy for DC microgrids, aiming to regulate voltage and manage power sharing in a cost-effective and scalable manner. By implementing event-triggered mechanisms, communication between distributed energy resources (DERs) is minimized, leading to reduced overhead while ensuring system stability. This article investigates an event-triggered mechanism that incorporates event detection and adaptive control strategies to maintain frequency, voltage, proportional current and powersharing stability, while avoiding unnecessary controller interactions.

关键词： Reactive power Event detection Scalability Microgrids Power system stability Generators Energy efficiency distributed power generation Voltage control Frequency control

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When Large-Scale Models Meet Edge computing: A Collaborative Framework for Multi-Task Applications 1

When Large-Scale Models Meet Edge Computing: A Collaborative...

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

作者： Zhang, Zhipeng Ma, Wenting Yang, Fan Yuan, Xiaohang Guo, Meng Yang, Lei Liu, Wei Zhao, Xia China Mobile Research Institute Beijing China Innovation Institute Guangzhou China

ISBN: (纸本)9781643685779

The recently proposed large-scale foundation models have made significant progress. Large-scale foundational models encounter significant challenges, particularly regarding the substantial data traffic generated from crowd sourced inputs and the intense computational workloads inherent in deep learning applications. To mitigate these resource-intensive demands, Edge computing has emerged as a promising solution, offering a means to alleviate the consumption of computational resources. However, the huge size of large-scale foundation models limits the models from wider applications on edge devices. In this paper, we investigate the challenge of an edge-cloud collaborative framework with large-scale models. Edge devices are commonly utilized by multiple services that execute a diverse array of applications. We propose an edge-cloud collaborative framework that integrates large-scale models to address multi-tasking scenarios. Our study delves into the characteristics of Transformer-based large models, observing a progression from recognizing abstract patterns in the initial layers to identifying concrete objects in the subsequent layers. Moreover, we propose an architectural design that consists of a single edge-based Universal Block (UB) and several cloud-based parallel Task-Specific Visual Prompt Tuning Blocks (VPT-TSBs), both incorporating large-scale models. In addition, we conduct an analysis to ascertain the storage and computational resources necessary for the implementation of our proposed methodology. © 2025 The Authors.

关键词： Cloud platforms

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