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检索条件"任意字段=14th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2014"

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algorithms and architectures for parallel processing - 14th international conference, ica3pp 2014, Proceedings

Algorithms and Architectures for Parallel Processing - 14th ...

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14th international conference on algorithms and architectures for parallel processing, ica3pp 2014

ISBN: (纸本)9783319111964

the proceedings contain 125 papers. the topics discussed include: efficient detection of cloned attacks for large-scale RFID systems;probability based algorithms for guaranteeing the stability of rechargeable wireless sensor networks;PTAs for minimum k-path connected vertex cover in growth-bounded graphs;a simple and effective long duration contact-based utility metric for mobile opportunistic networking;virtual network mapping algorithm in wireless data center networks;a weighted centroid based tracking system in wireless sensor networks;utilizing multiple Xeon phi coprocessors on one compute node;service scheduling algorithm in vehicle embedded middleware;similar samples cleaning in speculative multithreading;equi-join for multiple datasets based on time cost evaluation model;and identifying file similarity in large data sets by modulo file length.

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algorithms and architectures for parallel processing - 14th international conference, ica3pp 2014, Proceedings

Algorithms and Architectures for Parallel Processing - 14th ...

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14th international conference on algorithms and architectures for parallel processing, ica3pp 2014

ISBN: (纸本)9783319111933

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Dynamic Privacy Protection with Large Language Model in Social Networks 24th

Dynamic Privacy Protection with Large Language Model in So...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Xie, Yizhe Zhu, Congcong Zhang, Xinyue Hu, Xiangyu Liu, Xuan School of Data Science City University of Macau Macau China Hainan International College Minzu University of China Hainan China School of Information Engineering Minzu University of China Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan China

ISBN: (纸本)9789819615445

In contemporary social networks, dynamic privacy protection remains a pivotal yet challenging endeavor due to the intricate and evolving nature of information exchange. Traditional privacy models, predominantly static, falter in effectively safeguarding privacy amidst the complex interplay of continuously changing network interactions and structures. Addressing these deficiencies, this study introduces a novel dynamic privacy protection system anchored by large language model (LLM). Leveraging the natural language processing prowess of LLM, this system excels in real-time, context-sensitive analysis and protection of textual data within vast and variable social networks. By integrating closed-loop control theory, the system adeptly balances robust privacy safeguards with the requisite fluidity of information exchange. Experimental validations on large network datasets illustrate the system’s adeptness in balancing privacy leaks and information distortion through intelligent adaptations to privacy thresholds and strategic noise injection. the outcomes highlight the system’s utility in enhancing data security and operational efficiency, promising significant implications for future applications in broader domains such as mobile computing and IoT. this study not only propels forward the capabilities of dynamic privacy protection mechanisms but also sets a foundational architecture for subsequent innovations in privacy-sensitive, data-intensive environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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A Dual-Defense Self-balancing Framework Against Bilateral Model Attacks in Federated Learning 24th

A Dual-Defense Self-balancing Framework Against Bilateral Mo...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Wu, Xiang Yao, Aiting Pal, Shantanu Jiang, Frank Li, Xuejun Xu, Jia Dong, Chengzu Chen, Xuefei Zhang, Xiuyi Liu, Xiao Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui China Deakin University Melbourne Australia School of Data Science Lingnan University Hong Kong

ISBN: (纸本)9789819615247

With the rapid expansion of Artificial Intelligence (AI) services, smart devices generate a large amount of user data at the edge network, which urgently needs to be protected while effectively extracting information. Federated learning (FL) is an important technology for handling dispersed data and strict privacy requirements in this context. However, the security threats caused by model inversion attacks and poisoning attacks can affect the mutual trust between the client and server. Yet, for these two types of attacks, the existing defense mechanisms are contradictory in terms of whether the model parameters are publicly disclosed. In addition, the data distribution of the clients is imbalanced which will increase the bias of model, reducing its practicality. To address this issue, this study proposes a dual defense self-balanced federated learning (DDSFL) framework, aiming to introduce a novel lightweight defense mechanism during the model parameter aggregation stage, combating these two types of attacks simultaneously by applying differential privacy and adjusting learning rates. In addition, this method also integrates a middleware-based reordering algorithm to enhance the robustness of the framework. Experimental results show that DDSFL effectively improves the ability to resist imbalanced data, forged data, and malicious behavior, significantly enhancing the generalization performance and security of the FL system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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ORL-EPM: A Profit-Aware and Load-Driven Heterogeneous Resource Management Scheme with Collaborative Edge Computing 24th

ORL-EPM: A Profit-Aware and Load-Driven Heterogeneous Resou...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Huang, Fengyi Zhang, Yutao Wang, Wenhua Guo, Jianxiong Xu, Yang Fan, Wentao Wang, Tian Hong Kong Baptist University Hong Kong China Hong Kong Baptist University United International College Beijing Normal University Zhuhai China Beijing Normal University Zhuhai China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9789819615414

To address the limitations of energy-efficient but computationally limited ARM64-based AI edge devices and general-purpose edge servers, this paper proposes a Decentralized Collaborative Heterogeneous Edge Computing (DCEC) architecture. this architecture combines edge servers with embedded AI devices to achieve low latency and enhanced computational capabilities. Commercially, the DCEC framework is divided into edge private clouds and edge public clouds, aiming to maximize the profits of Edge Service Providers (ESPs) through dynamic resource management. Considering task dynamism, complexity, and resource heterogeneity, this complex problem is formulated as a multi-stage mixed-integer nonlinear programming (MINLP) problem. We developed the ORL-EPM resource management framework—a three-layer optimization system that adjusts task scheduling based on varying latency sensitivity weights and heterogeneous resource demands. Additionally, we introduced a resource collaboration system based on resource leasing to manage resource overloads and accommodate diverse task complexities. this system includes three Economic Payment Models (EPMs) designed to achieve efficient and profitable resource utilization. Extensive simulation results indicate that this method ensures convergence and closely approximates optimal solutions across various scenarios, significantly outperforming existing methods. Testbed experiments demonstrate that the DCEC architecture can reduce latency by up to 21.83% in real-world applications, notably exceeding previous approaches. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy efficiency

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A parallel Algorithm of Kirchhoff Pre-stack Depth Migration Based on GPU 1

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14th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Wang, Yida Li, Chao Tian, Yang Yan, Haihua Zhao, Changhai Zhang, Jianlei Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (数字)9783319111940

ISBN: (纸本)9783319111940;9783319111933

Kirchhoff pre-stack depth migration (KPSDM) algorithm, as one of the most widely used migration algorithms, plays an important part in getting the real image of the earth. However, this program takes considerable time due to its high computational cost;hence the working efficiency of the oil industry is affected. the general purpose Graphic processing Unit (GPU) and the Compute Unified Device Architecture (CUDA) developed by NVIDIA have provided a new solution to this problem. In this study, we have proposed a parallel algorithm of the Kirchhoff pre-stack depth migration and an optimization strategy based on the CUDA technology. Our experiments indicate that for large data computations, the accelerated algorithm achieves a speedup of 8 similar to 15 times compared with NVIDIA GPU.

关键词： Kirchhoff pre-stack depth migration GPU CUDA parallel algorithm optimization

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GPU-Accelerated Verification of the Collatz Conjecture

GPU-Accelerated Verification of the Collatz Conjecture

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14th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Honda, Takumi Ito, Yasuaki Nakano, Koji Hiroshima Univ Dept Informat Engn Higashihiroshima 7398527 Japan

ISBN: (纸本)9783319111971;9783319111964

the main contribution of this paper is to present an implementation that performs the exhaustive search to verify the Collatz conjecture using a GPU. Consider the following operation on an arbitrary positive number: if the number is even, divide it by two, and if the number is odd, triple it and add one. the Collatz conjecture asserts that, starting from any positive number m, repeated iteration of the operations eventually produces the value 1. We have implemented it on NVIDIA GeForce GTX TITAN and evaluated the performance. the experimental results show that, our GPU implementation can verify 5.01x10(11) 64-bit numbers per second, while the CPU implementation on Intel Xeon X7460 can verify 1.80 x 10(9) 64-bit numbers per second. thus, our implementation on the GPU attains a speed-up factor of 278 over the single CPU implementation.

关键词： Collatz conjecture GPGPU parallel processing Exhaustive verification

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Performance-Aware Data Placement in Hybrid parallel File Systems

Performance-Aware Data Placement in Hybrid Parallel File Sys...

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14th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： He, Shuibing Sun, Xian-He Feng, Bo Feng, Kun Wuhan Univ Sch Comp Wuhan 430072 Hubei Peoples R China Illinois Inst Technol Dept Comp Sci Chicago IL USA

ISBN: (纸本)9783319111971;9783319111964

Hybrid parallel file systems (PFS), which consist of both HDD and SSD servers, provide a promising solution for data-intensive applications. In this study, we propose a performance-aware data placement (PADP) strategy to enable efficient data layout in hybrid PFSs. the basic idea of PADP is to dispatch data on different file servers with adaptive varied-size file stripes based on the server storage performance. By using an effective data access cost model and a linear programming optimization method, the appropriate stripe sizes for each file server are determined effectively. We have implemented PADP within OrangeFS, a widely used parallel file system in HPC domain. Experimental results of representative benchmark show that PADP can significantly improve the I/O performance of hybrid PFSs.

关键词： parallel I/O System parallel File system Solid State Drive

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An Algorithm to Embed a Family of Node-Disjoint 3D Meshes into Locally Twisted Cubes 1

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14th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： You, Lantao Han, Yuejuan Soochow Univ Suzhou Ind Pk Inst Serv Outsourcing Suzhou 215000 Peoples R China Soochow Univ Ctr Informat Dev & Management Suzhou 215000 Peoples R China

ISBN: (数字)9783319111940

ISBN: (纸本)9783319111940;9783319111933

In this paper, embeddings of a family of 3D meshes in locally twisted cubes are studied. Let LTQ(n)(V, E) denotes the n-dimensional locally twisted cube. We find two major results in this paper:(1) For any integer n >= 4, two node-disjoint 3D meshes of size 2 x 2 x 2(n-3) can be embedded into LTQ(n) with dilation 1 and expansion 2. (2) For any integer n = 6, four node-disjoint 4x2x2(n-5) meshes can be embedded into LTQ(n) with dilation 1 and expansion 4. Further, an embedding algorithm can be constructed based on our embedding method. the obtained results are optimal in the sense that the dilations of the embeddings are 1.

关键词： Interconnection networks locally twisted cube 3D mesh embedding parallel computing

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Improving Speculation Accuracy with Inter-thread Fetching Value Prediction 1

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14th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Xu, Fan Shen, Li Wang, Zhiying Guo, Hui Su, Bo Chen, Wei Natl Univ Def Technol Coll Comp State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China

ISBN: (数字)9783319111940

ISBN: (纸本)9783319111940;9783319111933

Conventional software speculative parallel models are facing challenges due to the increasing number of the processor core and the diversification of the application. the speculation accuracy is one of the key factors to the performance of software speculative parallel model. In this paper, we proposed a novel value prediction mechanism named Inter-thread Fetching Value Prediction(IFVP). It supports a speculative thread to read the values of conflict variables speculatively from another speculative thread. this method can remarkably reduce the miss speculation rate in a loop to be parallelized with cross-iter dependencies. We have proved that the IFVP can improve the speculation accuracy by about 19.1% on the average, and can improve the performance by about 37.1% on the average, compared with the conventional models without value prediction.

关键词： computer architecture thread level speculation parallel computing

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