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检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4332 条记录，以下是931-940 订阅

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Recursive Task Generation for Scalable SDF Graph Execution on Multicore Processors

Recursive Task Generation for Scalable SDF Graph Execution o...

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euromicro conference on parallel, distributed and network-based processing

作者： Georgios Georgakarakos Johan Lilius Faculty of Science and Engineering Åbo Akademi University Turku Finland

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Dataflow modelling is a popular technique for describing parallel algorithms. Using dataflow, algorithm parallelism can be modelled and analysed efficiently at a high level of abstraction. However, challenges arise when translating dataflow semantics into executable code, mainly due to scheduling and synchronization overheads. Invoking task programming models in order to generate efficient code from dataflow representations has been proposed as a promising methodology to optimise the translation *** this paper, we propose recursive task execution as an optimisation for the dataflow-based code generation process. Our approach is based on extracting synchronous dataflow graph information in order to reduce scheduling overheads and improve load balancing when executing task-based code on multicore processors. We use PREESM dataflow-based prototyping framework to implement and test our concept. Results show that our proposed optimisation enhances code scalability therefore enabling higher application throughput.

关键词： Task analysis Multicore processing Programming Binary trees parallel processing Runtime Synchronization

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RSMCC: Enabling Ring-based Software Managed Cache-Coherent Embedded SoCs

RSMCC: Enabling Ring-based Software Managed Cache-Coherent E...

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euromicro conference on parallel, distributed and network-based processing

作者： Georgios Kornaros Electrical and Computer Engineering Department Hellenic Mediterranean University Heraklion Crete Greece

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Modern embedded heterogeneous Systems-on-Chip (SoCs) integrate differentiated memory hierarchy which require the programmer to undertake mostly simple, software-driven coherence protocols to optimize data-parallel applications. This paper presents a hybrid ring-based cache coherent multi-core architecture and a framework to develop memory hierarchy protocols customized to system constraints and workload needs. The ring protocol is developed in software over a typical mesh network-on-Chip (NoC) which is used to convey cache coherent messages. Management and system monitoring operations utilize the NoC hardware routing protocol to achieve speed and minimize software interference and overheads. The software cache coherent management protocol lays on top of a typical shared memory programming model and facilitates easy user-level control and integration with user application, while removing concerns for data allocation for scratchpad-based systems.

关键词： Protocols Coherence Software Hardware Memory management System-on-chip Multicore processing

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distributed Computation of a Robust Estimator based on Cauchy Noises 60

Distributed Computation of a Robust Estimator Based on Cauch...

引用

60th IEEE conference on Decision and Control (CDC)

作者： Snyder, Nathaniel Idan, Moshe Speyer, Jason L. Univ Calif Los Angeles Dept Mech & Aerosp Engn Los Angeles CA 90024 USA Technion Fac Aerosp Engn Haifa Israel

ISBN: (纸本)9781665436595

A real-time, recursive, multivariate estimation algorithm for time-invariant and time-varying linear systems with modelled Cauchy noises is developed. When previously compared to the Kalman Filter, the Multivariate Cauchy Estimator was shown to be robust against impulsive disturbances in the process or measurement functions, but proved computationally intractable for real-time estimation applications. Two significant insights allow for a reformulation of the Multivariate Cauchy Estimator to possess a streamlined recursive and computationally reduced characteristic function of the conditional probability density function of the system state-vector given the measurement sequence. This characteristic function is represented by a sum of terms, expanding with each measurement. First, we show that a cell-enumeration matrix can be computed for each hyperplane arrangement embedded within each term of the characteristic function of the Cauchy Estimator. We then show that functions used to formulate the terms of this characteristic function can be expressed as a vector of parameters operating on basis functions constructed from this enumeration matrix. This vector is obtained by solving an underdetermined system of equations. We demonstrate that our reformulation allows all terms with equal hyperplane arrangements to be reduced into a unique set. Secondly, we take advantage of advances in parallel processing to exploit the inherent parallelism found in the characteristic function of the Cauchy Estimator. A three state time-invariant system example is used to illustrate the performance of the Cauchy Estimator against the Kalman Filter when subjected to Gaussian and Cauchy noises. We report computational savings of over 99% when compared to the previous formulation. Furthermore, we discuss the real-time architecture of the Cauchy Estimator and report the execution speeds for a three-state system implemented on a single NVIDIA GeForce GTX 1060 graphics processing unit (GPU).

关键词： control-systems stochastic estimation bayesian estimation CUDA-C programming GPU linear programming cell enumeration Cauchy pdfs

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GK: A Graphormer Architecture based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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Analysis of Effective E-Commerce Coordination Big Data processing Strategies under Infinite Deep Neural network Topology

Analysis of Effective E-Commerce Coordination Big Data Proce...

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Communications Technology and Computer Science (ACCTCS), Asia-Pacific conference on

作者： Ailing Zhang Fuxiao Yao Shandong Vocational and Technical University of International Studies Rizhao Shandong China

ISBN: (纸本)9781665415385;9781665447034

This paper researches and analyses the effective e-commerce coordination big data processing strategy through the infinite-depth neural network topology. This paper firstly proposes a neural network training model Neural network-Storm (NN-S) based on Storm streaming distributed architecture, which decomposes the neural network training task into multiple computing units by data-parallel method, the parameters are updated synchronously after the training of a single batch of data is completed. In the Storm architecture, a Zookeeper network is used for multi-server distributed deployment. The training results show that the NN-S model can significantly improve the training speed of neural networks. At the same time, the NN-S architecture can quickly recover from node failures and network resource scheduling abnormalities with strong robustness. In this paper, we investigate the streaming-based distributed neural network training and design a Storm-based distributed neural network training model and optimized training algorithms, which are of reference significance for distributed neural network training.

关键词： Training Industries Job shop scheduling Processor scheduling Computational modeling Transportation Computer architecture

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Encoding Following Information with Graph Attention network for Opinion Prediction

Encoding Following Information with Graph Attention Network ...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jiajian Jiang Xiaoliang Chen Yuntian Zhang Zexia Huang Xianyong Li Yajun Du School of Computer and Software Engineering Xihua University Chengdu P. R. China

Opinion prediction is an important and challenging task. Previous work often only predicts opinions based on users’ content, ignoring the influence of neighbors’ opinions on users in social networks. In this paper, we utlize Graph Attention networks to capture neighbor information for opinion prediction. Firstly, we divide users in the online social media platform into general users and online influencers by the number of their fans. Then we built the implicit link between general users by their following list, and construct the implicit network for opinion prediction. Experimental results show that the proposed model is more accurate in opinion prediction compared to many competitive baselines.

关键词： Fans Social networking (online) Computational modeling Neural networks Predictive models Ubiquitous computing Encoding

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Composite Interference Signals Recognition based on YOLOv5

Composite Interference Signals Recognition Based on YOLOv5

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Shumin Tu Qin Cheng Fei Qian Pei Hui Lei Guan The 722 Research Institute Wuhan China The Integrated Service Networks Lab of Xidian University Xi’an China

Complex interference signal identification has been a paramount concern, yet there are very few effective methods. In this work, we propose a composite interference signal identification method based on target detection, which uses a detection box to detect the single interference signal in the composite interference signals (CISs) without separating CISs. This method based on YOLOv5 network can avoid the problem of difficult separation of CISs and be not affected by the multiple characteristics of the composite signal. It can not only effectively identify the composite signal, but also obtain the position information of each single signal. The experimental results validate our analysis. Moreover, it can be concluded that our proposed method is fault-tolerant and easy to migrate.

关键词： Fault diagnosis Training Fault tolerance Fault tolerant systems Interference Object detection Ubiquitous computing

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A Service Placement Algorithm based on Merkle Tree in MEC Systems Assisted by Digital Twin networks

A Service Placement Algorithm Based on Merkle Tree in MEC Sy...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Chengan Dai Kun Yang Chunjian Deng School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China

This paper considers a digital twin network (DTN) assisted mobile edge computing (MEC) system. When wireless device (WD) users request services from MECs, we assume in the system there are a cloud server, storing all of service entities, and a lightweight digital twin network, providing the digital replicas of state information of all MECs instead of service entities. We aim to maximize the number of service requests served by the MECs, or, equivalently, to minimize the load of the cloud. This is formulated as a mixed-integer non-convex optimization problem. We propose a service placement algorithm based on hash-based data structure called Merkle tree to solve the problem. The introduction of candidate mode pruning effectively reduces the time complexity of the algorithm in iterations. Simulation results show that our proposed method has a better performance compared with the other benchmarks.

关键词： Wireless communication Multi-access edge computing Simulation Heuristic algorithms Benchmark testing Ubiquitous computing Digital twins

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Knowledge Graph-based Intent network-Enhanced Web Services Recommendation

Knowledge Graph-based Intent Network-Enhanced Web Services R...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Lisheng Chen Buqing Cao Xiang Xie Yueying Qing Guosheng Kang Jianxun Liu School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

APIs recommendation for Mashup creation is becoming a hot topic in service computing. However, most existing APIs recommendation approaches fail to model the relationship between Mashup and API at a fine-grained level of intent, much less exploit relational dependencies to preserve the semantics of remote connections. Therefore, we propose an API service recommendation method based on the interactive learning intent of knowledge graphs for Mashup creation in this paper. This approach first creates a knowledge graph of APIs using natural language processing. Then, it uses the interaction learning intent model in the knowledge graph to model the intent of Mashups and APIs, obtain more independent Mashups and API intents by filtering intent independence and distance correlation, and recursively integrate them to gain intent-aware Mashup and API representations. Finally, the obtained Mashup and API representations are subjected to inner product operations to predict and generate API recommendation results. The experiments on real dataset collected from Programmable Web are performed, and the experimental results show that the recall of the proposed method have 11.35%, 15.72%, 21.33% and 9.36% improvement, the NDCG of the proposed method have 9.36%, 22.13%, 5.25% and 31.26% improvement, respectively, compared to Deep-FM, KGAT, CKE and R-GCN.

关键词： Knowledge engineering Correlation Filtering Mashups Computational modeling Semantics Service computing

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Big Data analysis of demand side management for Industrial IOT applications 3

Big Data analysis of demand side management for Industrial I...

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3rd International conference on Materials, Manufacturing and Modelling (ICMMM)

作者： Kodidala, Venkata Shiva Sai Jaswanth Akkala, Sowmya Madupoju, Sanjay Kumar Dasara, Venkata Satya Sai Teja Juvvadi, Mounish Thangadurai, N. Manipal Inst Technol Dept Comp & Commun Engn Manipal 576104 Karnataka India Manipal Inst Technol Dept Elect & Commun Engn Manipal 576104 Karnataka India JAIN Deemed Univ Dept Elect & Commun Engn Fac Engn & Technol Bangalore 562112 Karnataka India Chaitanya Bharathi Inst Technol Dept Comp Sci Engn Gandipet 500075 Telangana India

Companies today demand even more high quality services and infrastructure in the field of Industrial Internet IOT. There are several problems facing smart urbanization. Of special concern are secured energy demand side management. The IIoT makes ransomware, cyber attacks and other security threats open to industrial systems. IIoT will provide meaningful solutions to these problems through the amalgamation of big data analytics. That first author recommends a safe, reliable Demand Side Management engine for a smart cooperative client using IIoT based Big Data Analysis. A highly distributed framework to the theoretical engine for optimizing DSM across a Home Area network. A payload authentication framework based on a lightweight handshake mechanism is used to improve the protection of this engine. In order to make users to control different server native resources energy efficiently, our theoretical method uses the portable utilizes of the Constrained Application Protocol. Moreover, data streams with parallel MapReduce processing are managed using big data analysis. In addition, we use Apache Spark with Apache Hadoop to check the theoretical engine to test our input transform match on stable datafile. The test results indicate that the architecture outlined provides useful insights into the intelligent social communities of the IIoT. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International conference on Materials, Manufacturing and Modelling.

关键词： Smart communities Home Area network Demand side management Industrial Internet of Things

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