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DOA Estimation in A distributed Optimization Framework: A Sparse Approach Based on Consensus ADMM Implementation

DOA Estimation in A Distributed Optimization Framework: A Sp...

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2023 international conference on Images, Signals, and computing, ICISC 2023

作者： Jia, Xiaoyuan Wu, Xiaohuan Zhu, Wei-Ping School of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Department of Electrical and Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9781510668171

ISBN: (纸本)9781510668164

Traditional direction-of-arrival (DOA) estimation methods use a single processor to deal with the array data. In recent years, the increasing of the scale of sensor arrays brings heavy workload for single processor. distributed optimization based on multiple local processors has become one of the current research hotspots due to the advantage of parallel computing. In this paper, we proposed a distributed DOA estimation method for massive large-scale arrays. First of all, we provide the signal model and the distributed optimization problem based on sparse representation in a distributed framework. Then, the optimization problem is solved by the alternating direction multiplier method (ADMM), where the overall structure of array is not changed. Compared with the centralized method, our distributed method can greatly reduce the computational complexity while ensuring the estimation accuracy under the large aperture array. Simulation results are provided to show the superiorities of our method. © 2023 SPIE.

关键词： Optimization

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ISAR Imaging of Targets with Complex Motions Based on the Non-uniformed Sampled Integrated Modified Chirp Rate-Quadratic Chirp Rate Distribution 26

ISAR Imaging of Targets with Complex Motions Based on the No...

引用

26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Winter 2023

作者： Sun, Zongyang Bai, Linting Xu, Kangfa Xi'an Aeronautics Computing Technique Research Institute Avic Aviation Key Laboratory of Science and Technology on Airborne and Missle-borne Computer Xi'an China

ISBN: (纸本)9798350345865

This paper proposes a new ISAR algorithm for obtaining well-focused images of targets with complex motions in low signal-to-noise ratio (SNR) environments. The algorithm models the echo signals of targets with complex motions as cubic phase signals (CPS) after motion compensations and uses the Non-uniformed Sampled Integrated Modified Chirp Rate-Quadratic Chirp Rate Distribution (NIM-CRQCRD) to estimate CPS parameters. The proposed algorithm compensates the echo signals using the parameter estimates and generates ISAR images of targets with complex motions. Simulation results show that the proposed algorithm has superior anti-noise performance. © 2023 IEEE.

关键词： Parameter estimation

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FSD-Inference: Fully Serverless distributed Inference with Scalable Cloud Communication 40

FSD-Inference: Fully Serverless Distributed Inference with S...

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40th IEEE international conference on Data Engineering, ICDE 2024

作者： Oakley, Joe Ferhatosmanoglu, Hakan University of Warwick Department of Computer Science United Kingdom

ISBN: (纸本)9798350317152

Serverless computing offers attractive scalability, elasticity and cost-effectiveness. However, constraints on memory, CPU and function runtime have hindered its adoption for dataintensive applications and machine learning (ML) workloads. Traditional 'server-ful' platforms enable distributed computation via fast networks and well-established inter-process communication (IPC) mechanisms such as MPI and shared memory. In the absence of such solutions in the serverless domain, parallel computation with significant IPC requirements is challenging. We present FSD-Inference, the first fully serverless and highly scalable system for distributed ML inference. We explore poten-tial communication channels, in conjunction with Function-as-a-Service (FaaS) compute, to design a state-of-the-art solution for distributed ML within the context of serverless data-intensive computing. We introduce novel fully serverless communication schemes for ML inference workloads, leveraging both cloud-based publish-subscribe/queueing and object storage offerings. We demonstrate how publish-subscribe/queueing services can be adapted for FaaS IPC with comparable performance to object storage, while offering significantly reduced cost at high parallelism levels. We conduct in-depth experiments on benchmark DNNs of various sizes. The results show that when compared to server-based alternatives, FSD-Inference is significantly more cost-effective and scalable, and can even achieve competitive performance against optimized HPC solutions. Experiments also confirm that our serverless solution can handle large distributed workloads and leverage high degrees of FaaS parallelism. © 2024 IEEE.

关键词： Deep neural networks

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The Impact of Importance-Aware Dataset Partitioning on Data-parallel Training of Deep Neural Networks 23rd

The Impact of Importance-Aware Dataset Partitioning on Data-...

引用

23rd IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems (DAIS) Held as Part of the 18th international Federated conference on distributed computing Techniques (DisCoTec)

作者： Sheikholeslami, Sina Payberah, Amir H. Wang, Tianze Dowling, Jim Vlassov, Vladimir KTH Royal Inst Technol Stockholm Sweden Hopsworks AB Stockholm Sweden

ISBN: (纸本)9783031352591;9783031352607

Deep neural networks used for computer vision tasks are typically trained on datasets consisting of thousands of images, called examples. Recent studies have shown that examples in a dataset are not of equal importance for model training and can be categorized based on quantifiable measures reflecting a notion of "hardness" or "importance". In this work, we conduct an empirical study of the impact of importance-aware partitioning of the dataset examples across workers on the performance of data-parallel training of deep neural networks. Our experiments with CIFAR-10 and CIFAR-100 image datasets show that data-parallel training with importance-aware partitioning can perform better than vanilla data-parallel training, which is oblivious to the importance of examples. More specifically, the proper choice of the importance measure, partitioning heuristic, and the number of intervals for dataset repartitioning can improve the best accuracy of the model trained for a fixed number of epochs. We conclude that the parameters related to importance-aware data-parallel training, including the importance measure, number of warmup training epochs, and others defined in the paper, may be considered as hyperparameters of data-parallel model training.

关键词： Data-parallel training Example importance distributed deep learning

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Graph-based Multi-view Clustering for Web services 21

Graph-based Multi-view Clustering for Web services

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21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yang, Wang Zhenzhen, Yuan Guosheng, Kang Buqing, Cao Jianxun, Liu Yong, Xiao Hnust Xiangtan China Hunan University of Science and Technology Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering China

ISBN: (纸本)9798350329223

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process. © 2023 IEEE.

关键词： Web services

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Decentralized parallel Blockchain Agricultural Product Traceability System Security Analysis 5

Decentralized Parallel Blockchain Agricultural Product Trace...

引用

5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Fenglin, Sun Guangxi Normal University Qixing District Guangxi Guilin City541000 China

ISBN: (纸本)9798350395235

This research work presents a decentralized parallel blockchain-based agricultural product traceability system, which aims to enhance information security and data model efficiency, and achieve real-time tracking of agricultural products. Through an in-depth analysis of decentralized blockchain applications, the system adopts distributed ledger processing for low-cost transactions. The Digital Promise technology ensures the credibility of the data transmission and privacy protection. The proposed decentralized parallel blockchain structure shows significant traceability results, ensuring transparent traceability of agricultural products. Experimental results demonstrate the effectiveness of the system, improving food safety and quality management. The decentralized nature reduces the risk of data tampering, thereby enhancing public trust. Experimental results show short query response times, efficient data transfers, and zero failures, highlighting the system's excellent performance in handling traceability queries. These results underscore the remarkable advantages of blockchain technology in increasing the efficiency and reliability of traceability systems. © 2024 IEEE.

关键词： Blockchain

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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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Maximum Length-Constrained Flows and Disjoint Paths: distributed, Deterministic, and Fast 2023

Maximum Length-Constrained Flows and Disjoint Paths: Distrib...

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55th Annual ACM Symposium on Theory of computing (STOC) part of the ACM Federated computing Research conference (FCRC)

作者： Haeupler, Bernhard Hershkowitz, D. Ellis Saranurak, Thatchaphol Swiss Fed Inst Technol Zurich Switzerland Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Michigan Ann Arbor MI USA

ISBN: (纸本)9781450399135

computing routing schemes that support both high throughput and low latency is one of the core challenges of network optimization. Such routes can be formalized as h-length flows which are defined as flows whose flow paths have length at most h. Many well-studied algorithmic primitives-such as maximal and maximum length-constrained disjoint paths-are special cases of h-length flows. Likewise the optimal h-length flow is a fundamental quantity in network optimization, characterizing, up to poly-log factors, how quickly a network can accomplish numerous distributed primitives. In this work, we give the first efficient algorithms for computing (1 - epsilon)-approximate h-length flows that are nearly "as integral as possible." We give deterministic algorithms that take (O) over tilde (poly(h, 1/epsilon)) parallel time and (O) over tilde (poly(h, 1/epsilon) center dot 2(O) (root log n)) distributed CONGEST time. We also give a CONGEST algorithm that succeeds with high probability and only takes (O) over tilde (poly(h, 1/epsilon)) time. Using our h-length flow algorithms, we give the first efficient deterministic CONGEST algorithms for the maximal disjoint paths problem with length constraints-settling an open question of Chang and Saranurak (FOCS 2020)-as well as essentially-optimal parallel and distributed approximation algorithms for maximum length-constrained disjoint paths. The former greatly simplifies deterministic CONGEST algorithms for computing expander decompositions. We also use our techniques to give the first efficient and deterministic (1-epsilon)-approximation algorithms for bipartite b-matching in CONGEST. Lastly, using our flow algorithms, we give the first algorithms to efficiently compute h-length cutmatches, an object at the heart of recent advances in length-constrained expander decompositions.

关键词： length-bounded flows hop-bounded flows distributed algorithms parallel algorithms flow rounding cycle covers b-matching

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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37th conference on Neural Information Processing Systems (NeurIPS)

作者： Beznosikov, Aleksandr Takac, Martin Gasnikov, Alexander Innopolis Univ Innopolis Russia Skoltech Moscow Russia MIPT Dolgoprudnyi Russia Yandex Moscow Russia MBZUAI Abu Dhabi U Arab Emirates RAS IITP Moscow Russia

ISBN: (纸本)9781713899921

Variational inequalities are a broad and flexible class of problems that includes minimization, saddle point, and fixed point problems as special cases. Therefore, variational inequalities are used in various applications ranging from equilibrium search to adversarial learning. With the increasing size of data and models, today's instances demand parallel and distributed computing for real-world machine learning problems, most of which can be represented as variational inequalities. Meanwhile, most distributed approaches have a significant bottleneck - the cost of communications. The three main techniques to reduce the total number of communication rounds and the cost of one such round are the similarity of local functions, compression of transmitted information, and local updates. In this paper, we combine all these approaches. Such a triple synergy did not exist before for variational inequalities and saddle problems, nor even for minimization problems. The methods presented in this paper have the best theoretical guarantees of communication complexity and are significantly ahead of other methods for distributed variational inequalities. The theoretical results are confirmed by adversarial learning experiments on synthetic and real datasets.

关键词： Variational techniques

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