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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991-1000 订阅

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XKBlas: a High Performance Implementation of BLAS-3 Kernels on Multi-GPU Server

XKBlas: a High Performance Implementation of BLAS-3 Kernels ...

引用

euromicro conference on parallel, distributed and network-based processing

作者： Thierry Gautier João V. F. Lima LIP Laboratory INRIA CNRS UCL-Lyon France Graduate Program in Computer Science Universidade Federal de Santa Maria – Santa Maria Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

In the last ten years, GPUs have dominated the market considering the computing/power metric and numerous research works have provided Basic Linear Algebra Subprograms implementations accelerated on GPUs. Several software libraries have been developed for exploiting performance of systems with accelerators, but the real performance may be far from the platform peak performance. This paper presents XKBlas that aims to improve performance of BLAS-3 kernels on multi-GPU systems. At low level, we model computation as a set of tasks accessing data on different resources. At high level, the API design favors non-blocking calls as uniform concept to overlap latency, even by fine grain computation. Unit benchmark of BLAS-3 kernels showed that XKBlas outperformed most implementations including the overhead of dynamic task's creation and scheduling. XKBlas outperformed BLAS implementations such as cuBLAS-XT, PaRSEC, BLASX and Chameleon/StarPU.

关键词： Task analysis Graphics processing units Libraries Kernel Layout Runtime Matrix decomposition

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Transmission Line Decoupling based parallel Simulation Method of an Active Distribution network

Transmission Line Decoupling Based Parallel Simulation Metho...

引用

Sustainable Power and Energy conference (iSPEC)

作者： Songtao Wei Zhichun Yang Guannan Lou Ke Li Fan Yang Yu Shen School of Electrical Engineering Southeast University Nanjing China Electric Power Research Institute State Grid Hubei Electric Power Co. Ltd Wuhan China

ISBN: (纸本)9781665414401

Now that a large number of distributed generations (DGs) and nonlinear loads are included in the distribution network, the current active distribution network gradually presents the characteristics of multiple time scales and wide-ranging frequency domain, which undoubtedly increases the complexity of electromagnetic transient (EMT) simulation. Because of this, to improve the efficiency of electromagnetic transient simulation in the condition, this study proposes a network decoupling method that can be suitable for parallel simulation of distribution networks. According to the characteristics of distributed parameter transmission lines, the study introduces a linear interpolation algorithm into the traditional Bergeron model to match the requirements of short-distance line simulation in distribution networks. In addition, applying the improved model to the decoupling of distribution networks, a novel parallel simulation interface of distribution networks is designed and established, and a multi-area decoupling simulation of the distribution network was realized. Finally, the proposed decoupling method is turn out to be more accurate and efficient than the traditional method and more suitable for parallel computing of distribution networks by a simulation example of IEEE13 Node Test Feeders.

关键词： Adaptation models Interpolation Power transmission lines Computational modeling Simulation Distribution networks parallel processing

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An A3C Deep Reinforcement Learning FPGA Accelerator based on Heterogeneous Compute Units

An A3C Deep Reinforcement Learning FPGA Accelerator based on...

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International conference on Communication Technology (ICCT)

作者： Ziyu Li Fen Ge Fang Zhou Ning Wu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

Deep reinforcement leaning (DRL) has a great performance in intelligent decision-making question. As one of well-performance DRL algorithms, Asynchronous Advantage Actor-Critic (A3C) can make distributed agents work simultaneously to infer and train without experience pool. However, the multiple agents also bring complex deep neural network (DNN) computations in each agent which costs the almost time in A3C algorithm. In this paper, we propose a DRL FPGA accelerator based on heterogeneous compute units for A3C algorithm to reduce the DNN computing periods. According to A3C algorithm computing flow, we implement both inference and training steps on accelerator and pipeline their computations. In DNN forward propagation (FP) and backward propagation (BP) computing, the Multiply-ADDer-tree (MADD) structure with high input parallelism and Multiply-Accumulation (MAC) structure with high output parallelism are proposed to be employed, respectively. Heterogeneous computing method makes the weight parameters layout of inference and training consistent, and improves the access efficiency of on-chip memory. Experimental results show that our design achieves 40-times speedup in inference-training speedup compared to CPU, and 54-times speedup compared to GPU. Moreover, our heterogeneous computing method lead to better efficiency in memory accessing compared to exiting approaches.

关键词： Training Deep learning Accelerated aging Memory management Graphics processing units parallel processing Inference algorithms

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A Mining Frequent Itemsets Algorithm in Stream Data based on Sliding Time Decay Window 20

A Mining Frequent Itemsets Algorithm in Stream Data Based on...

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3rd International conference on Artificial Intelligence and Pattern Recognition (AIPR)

作者： Lu, Xin Jin, Shaonan Wang, Xun Yuan, Jiao Fu, Kun Yang, Ke Univ Elect Sci & Technol China Sch Software Chengdu Peoples R China China Railway Eryuan Engn Grp Co Ltd Chengdu Peoples R China

ISBN: (纸本)9781450375511

In order to reduce the time and memory consumption of frequent itemsets mining in stream data, and weaken the impact of historical transactions on data patterns, this paper proposes a frequent itemsets mining algorithm SWFIUT-stream based on sliding decay time window. In this algorithm, the time attenuation factor is introduced to assign different weights to each window unit to weaken their influence on data mode. In order to realize the fast stream data mining processing, when mining the frequent itemsets, the two-dimensional table is used to scan and decompose the itemsets synchronously to mine all the frequent itemsets in the window, and the distributed parallel computing processing is carried out based on storm framework. Experimental data show that the algorithm consumes less time and consumes less memory space than conventional algorithms when mining frequent itemsets in stream data.

关键词： Sliding window stream data frequent itemset storm framework

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A Graph Theory-based Clustering Method for Power System networks

A Graph Theory-Based Clustering Method for Power System Netw...

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Clemson-University Power Systems conference (PSC)

作者： Jayawardene, Iroshani Herath, Pramod Venayagamoorthy, Ganesh Kumar Clemson Univ Holcombe Dept Elect & Comp Engn Real Time Power & Intelligent Syst Lab Clemson SC 29634 USA Univ KwaZulu Natal Sch Engn Durban South Africa

ISBN: (纸本)9781728193847

With the increasing human population and requirements, the current electric power grid is becoming complex and expanding rapidly. Increasing integration of renewable energy sources and other advanced metering technologies introduces a complex, expanded and distributed power system infrastructure. Real-time operation and control of large-scale power system networks have become a challenging task due to these reasons. Power system clustering is a feasible solution to overcome these challenges. Clustering utilizes parallel processing capabilities of control methods and provides adequate methods for real-time power system control. In this paper, a graph theory-based clustering approach is presented for large interconnected power system networks. The algorithm is tested on multiple power system networks from small to large scale and the performance is analyzed. Typical results indicate that the method can provide clusters with uniform sizes within a short time limit for any type of power system network. The clusters observed are applied in solving optimal power flow (OPF) problem.

关键词： clustering graph theory parallel processing power networks real-time operation

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A Cloud-hosted MapReduce Architecture for Syntactic Parsing 45

A Cloud-hosted MapReduce Architecture for Syntactic Parsing

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45th euromicro conference on Software Engineering and Advanced Applications (SEAA) / 22nd euromicro conference on Digital System Design (DSD)

作者： Woldemariam, Yonas D. Pletschacher, Stefan Clausner, Christian Bass, Julian M. Umea Univ Comp Sci Umea Sweden Univ Salford Comp Sci & Software Engn Manchester Lancs England

ISBN: (纸本)9781728134215

Syntactic parsing is a time-consuming task in natural language processing particularly where a large number of text files are being processed. Parsing algorithms are conventionally designed to operate on a single machine in a sequential fashion and, as a consequence, fail to benefit from high performance and parallel computing resources available on the cloud. We designed and implemented a scalable cloud-based architecture supporting parallel and distributed syntactic parsing for large datasets. The main architecture consists of a syntactic parser (constituency and dependency parsing) and a MapReduce framework running on clusters of machines. The resulting cloud-based MapReduce parsing is able to build a map where syntactic trees of the same input file have the same key and collect into a single file containing sentences along with their corresponding trees. Our experimental evaluation shows that the architecture scales well with regard to number or processing nodes and number of cores per node. In the fastest tested cloud-based setup, the proposed design performs 7 times faster when compared to a local setup. In summary, this study takes an important step toward providing and evaluating a cloud-hosted solution for efficient syntactic parsing of natural language data sets consisting of a large number of files.

关键词： cloud deployment natural language processing (NLP) syntactic parsing

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Handwritten Chinese Character Recognition based on Convolutional Neural networks and TrueType Font Template Matching

Handwritten Chinese Character Recognition Based on Convoluti...

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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： Yue Chen Guangyao Pang Xiaoying Zhu Baoxing Pu Jihong Huang Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

Convolutional neural networks(CNN) have made significant advances in offline handwritten Chinese character recognition, CNN recognize characters based on local feature but not making use of the overall topology of the character to improve recognition accuracy. In this paper, we propose a method for handwritten Chinese character recognition by using CNN combined with TrueType font template matching. Firstly, a trained CNN is used to recognize the handwritten Chinese character, the output Top-N are selected as the candidate characters, and then the handwritten Chinese character is linearly transformed and matched with the TrueType font templates of the candidate characters respectively, the best matching candidate character is selected as the result. The experiments show that the method can combine the speed of CNN and the accuracy of template matching effectively, has higher accuracy than conventional CNN, especially for specific types of handwritten Chinese characters recognition.

关键词： Handwriting recognition network topology Neural networks Stroke (medical condition) Ubiquitous computing Topology Convolutional neural networks

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Chiron: Optimizing Fault Tolerance in QoS-aware distributed Stream processing Jobs 8

Chiron: Optimizing Fault Tolerance in QoS-aware Distributed ...

引用

8th IEEE International conference on Big Data (Big Data)

作者： Geldenhuys, Morgan K. Thamsen, Lauritz Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781728162515

Fault tolerance is a property which needs deeper consideration when dealing with streaming jobs requiring high levels of availability and low-latency processing even in case of failures where Quality-of-Service constraints must he adhered to. Typically, systems achieve fault tolerance and the ability to recover automatically from partial failures by implementing Checkpoint and Rollback Recovery. However, this is an expensive operation which impacts negatively on the overall performance of the system and manually optimizing fault tolerance for specific jobs is a difficult and time consuming task. In this paper we introduce Chiron, an approach for automatically optimizing the frequency with which checkpoints are performed in streaming jobs. For any chosen job, parallel profiling runs a re performed, each containing a variant of the configurations, with the remitting metrics 11 sed to model the impact of checkpoint-based fault tolerance on performance and availability. Understanding these relationships is key to minimizing performance objectives and meeting strict Quality-of-Service constraints. We implemented Chiron prototypically together with Apache Flink and demonstrate its usefulness experimentally.

关键词： distributed Stream processing Fault Tolerance Profiling Performance Modeling Quality of Service

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Location-based Service Recommendation for Cold-Start in Mobile Edge Computing 17th

Location-Based Service Recommendation for Cold-Start in Mobi...

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17th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2020

作者： Yu, Mengshan Fan, Guisheng Yu, Huiqun Chen, Liang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030794774

With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Service recommendation in such a distributed environment is a hot issue. However, the cold-start problem for service recommendation in mobile edge computing is still urgent to be solved. In this paper, we propose a service recommendation method based on collaborative filtering (CF) and user location, by comprehensively considering the characteristic of services at the edge and mobility of users. In detail, we first synthesize the service characteristics of each dimension through multidimensional weighting method. We further introduce the idea of Inverse_CF_Rec to the traditional CF and then predict the lost QoS value to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS value and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on collaborative filtering (MDICF) outperforms Inverse_CF_Rec in terms of the accuracy of recommendation. © 2021, IFIP International Federation for Information processing.

关键词： Location based services

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Machine learning based network intrusion detection for data streaming IoT applications

Machine learning based network intrusion detection for data ...

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Software Engineering, Artificial Intelligence, networking and parallel/distributed Computing (SNPD-Winter), ACIS International Winter conference on

作者： Aymen Yahyaoui Haithem Lakhdhar Takoua Abdellatif Rabah Attia SERCOM Lab University of Carthage Military Academy of Fondouk Jedid Tunisia

In recent years, Internet of Things (IoT) technologies have been widely used in many fields such as surveillance, health-care, smart metering and environment monitoring. This extensive usage leads to massive data management and a complexity in data analysis. A huge number of IoT sensors are deployed for monitoring task and send continuously their collected data to gateways. IoT applications are analyzing these data flows and making real time decisions about specific monitored events (fire, flood, terrorist attacks, etc.). Anomalies that may be related to sensor failures or network intrusions are affecting such decisions. Therefore, they should be detected and eliminated as soon as they arrive. This task requires real time data processing detectors for making accurate and fast predictions. In this paper, we design an architecture for a real time network intrusion detection system for IoT streaming data. The system was developed, deployed and tested with the two leading stream processing frameworks (Apache Flink and Apache Spark Streaming). We used two different public datasets and different machine learning algorithms. Results show considerable throughputs and high detection accuracy especially for Apache Flink.

关键词： Terrorism Surveillance network intrusion detection Throughput Real-time systems Internet of Things Task analysis

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