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检索条件"任意字段=5th International Conference on Algorithms and Architectures for Parallel Processing"

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algorithms and architectures for parallel processing: 13th international conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings, Part II

Algorithms and Architectures for Parallel Processing: 13th I...

引用

2013年

作者： Rocco Aversa Joanna KołOdziej Jun Zhang

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Federated Learning and parallel Prompt Scheduling Strategies for Large Language Models 24th

Federated Learning and Parallel Prompt Scheduling Strategie...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Lv, Guangtong Gu, Bruce Jia, Xiaocong Gao, Longxiang Qu, Youyang Cui, Lei Jinan China Shandong Provincial Key Laboratory of Computer Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615278

Large language models have garnered significant attention and are widely utilized across different fields due to their impressive performance. However, centralized training of these models can pose privacy risks like data leakage, hindering their application and advancement. As a decentralized learning paradigm, Federated Learning(FL) can effectively address data security in large language models and has specific application scenarios. In addition, the fine-tuning process of large language models also incurs significant communication expenses and leads to the loss of computational resources. the utilization of prompt tuning in federated learning is anticipated to decrease training costs, accelerate the training process, and enhance model performance. In this paper, we propose ‘FedLLM-PPS’ to explore a novel approach for large language models within federated learning. this method address data security concerns associated with large language models while enhancing performance and efficiency. Experiments based on several datasets for various Natural Language processing(NLP) tasks demonstrate superior performance and efficiency compared to baseline methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770777

the proceedings contain 102 papers. the special focus in this conference is on Cognitive Computing and Cyber Physical Systems. the topics include: Comparative Analysis Between Fuzzy theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and Machine Learning Models;defense Strategy Security Mechanism for Sensor Networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine Learning and Image processing;Leveraging the Power of MRMR in Machine Learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward Neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT Images;investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep Learning;Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images;non-invasive Technique for Detecting Glycosuria through Image processing and Deep Learning Approaches;deep Learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN architectures;thz Wideband Metamaterial Absorber for Different Applications.

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770746

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Hybridization of One- and Two-Point Bandits Convex Optimization in Non-stationary Environments 24th

Hybridization of One- and Two-Point Bandits Convex Optimiz...

引用

24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zeng, Gailun Guo, Jianxiong Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Hong Kong Baptist University Hong Kong Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9789819615278

Bandit Convex Optimization (BCO) is an imperative analysis framework when dealing with sequential decision-making problems. Considering to balance the computational cost and bounds of regrets, in this paper, we propose a hybridized algorithm of one- and two-point bandit convex models in non-stationary environments and use a more general performance measure dynamic regret, which records the cumulative difference between function loss and a feasible comparator sequence during the time horizon T. the path length of a comparator sequence PT reveals the non-stationarity of environments. Our proposed algorithm builds an upper bound of dynamic regret O((1+PT)1/2[β(λT)1/2+((1-λ)T)3/4]), where the parameter λ can dynamically adjust the bound guarantee to balance the computational cost in real applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Comparators (optical)

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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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MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework 24th

MARO: Enabling Full MPI Automatic Refactoring in DSL-Based ...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Lei, Tong Chen, Zongjing Che, Yonggang Xu, Chuanfu Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615445

Currently, the landscape of computer hardware architecture presents the characteristics of heterogeneity and diversity, prompting widespread attention to cross-platform portable parallel programming techniques. Most existing portable programming approaches adopt the "MPI+X" strategy, focusing mainly on node-level "X" parallelism, while inter-node parallelization still relies on manual MPI programming. OP2 is a domain-specific language-based portable parallel programming framework for unstructured mesh applications that supports the automatic generation of MPI parallel code. However, programmers must manually write pre-processing code during code refactoring, such as handling the mapping relationship between global and process-local variables and the initial distribution of process-local data. In this paper, we propose MARO (MPI Automatic Refactoring for OP2), an automatic refactoring method for generating pre-processing code targeting MPI code generation within the OP2 programming framework for unstructured mesh applications. this method automatically generates the pre-processing code required for MPI parallelism through the source-to-source translator, enabling OP2 to generate MPI parallel code without requiring users to write MPI-related code manually. We compare the automatically refactored OP2 applications using the proposed method with those manually refactored to validate its effectiveness. the proposed method enhances OP2’s automatic parallel code generation capability, enabling it to achieve fully automatic MPI parallelization. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic programming

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Optimized FPGA architectures for High-Speed NTT Computation 5

Optimized FPGA Architectures for High-Speed NTT Computation

引用

5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Lekkala, Chetan Harsha Bharatula, Sitadevi Reddy, B. Naresh Kumar Sarangam, K. Amrita School of Engineering Department of Ece Chennai India

ISBN: (纸本)9798350356236

this research paper aims to enhance the performance of Number theoretic Transform (NTT) operations in cryptographic applications by leveraging the flexibility and parallel processing capabilities of Field Programmable Gate Arrays (FPGAs). NTT, integral to many cryptographic algorithms, often faces real-time performance challenges due to computational demands. By implementing NTT on FPGAs, this approach introduces features such as parallelism, pipelining, and dynamic memory management, significantly improving computational efficiency. the proposed architecture, utilizing 8T SRAM on the AMD Kintex 7 FPGA KC705 Kit, optimizes memory bandwidth and polynomial multiplication, resulting in a robust, high-speed implementation suitable for real-time cryptographic tasks. Simu-lation results demonstrate substantial performance gains, offering a valuable framework for future cryptographic hardware designs. © 2025 IEEE.

关键词： Probabilistic logics

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Dynamic Offloading Control for Waste Sorting Based on Deep Q-Network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. the method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication Network

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