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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1071-1080 订阅

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A High Performance Computing Method for Noise Cross-Correlation Functions of Seismic Data

A High Performance Computing Method for Noise Cross-Correlat...

引用

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

作者： Junwei Zhou Qian Wei Chao Wu Guangzhong Sun School of Earth and Space Sciences University of Science and Technology of China School of Physical Sciences University of Science and Technology of China Network and Information Center University of Science and Technology of China School of Computer Science and Technology University of Science and Technology of China

Calculation of noise cross-correlation functions (NCF) plays an important role in ambient noise imaging which is a vital seismic method to obtain Earth inner structures. To raise the resolution of the imaging results, we need more seismic data for imaging. However, as the size of seismic data increases, the serial algorithm for NCF calculation becomes much more time-consuming. Thus, how to accelerate the NCF calculation becomes a key problem in ambient noise imaging. based on the analysis of serial algorithm, we proposed a new parallel algorithm for NCF calculation using NVIDIA GPU platform. In addition, we improved reading and writing strategy to reduce I/O consumption. Experimental results on real seismic data show the effectiveness of our method. The parallel program achives about 1861 times speedup.

关键词： Earth Image resolution High performance computing Imaging Graphics processing units Writing parallel algorithms

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Enhancing Resource Management Through Prediction-based Policies 26th

Enhancing Resource Management Through Prediction-Based Polic...

引用

26th International conference on parallel and distributed Computing (Euro-Par)

作者： Navarro, Antoni Lorenzon, Arthur F. Ayguade, Eduard Beltran, Vicenc Barcelona Supercomp Ctr Barcelona Spain Fed Univ Pampa Alegrete RS Brazil

ISBN: (纸本)9783030576752;9783030576745

Task-based programming models are emerging as a promising alternative to make the most of multi-/many-core systems. These programming models rely on runtime systems, and their goal is to improve application performance by properly scheduling application tasks to cores. Additionally, these runtime systems offer policies to cope with application phases that lack in parallelism to fill all cores. However, these policies are usually static and favor either performance or energy efficiency. In this paper, we have extended a task-based runtime system with a lightweight monitoring and prediction infrastructure that dynamically predicts the optimal number of cores required for each application phase, thus improving both performance and energy efficiency. Through the execution of several benchmarks in multi-/many-core systems, we show that our prediction-based policies have competitive performance while improving energy efficiency when compared to state of the art policies.

关键词： Energy efficiency Resource management Resource sharing OmpSs-2 Predictions Monitoring Cost

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Self-Attention based Automated Vulnerability Detection with Effective Data Representation

Self-Attention based Automated Vulnerability Detection with ...

引用

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

作者： Tongshuai Wu Liwei Chen Gewangzi Du Chenguang Zhu Gang Shi Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Vulnerability detection is an important means to protect computer software systems from network attacks and ensure data security. Automatic vulnerability detection by machine learning has become a research hotspot in recent years. The emergence of deep learning technology reduces human experts’ boring and arduous work in defining vulnerability features, which obtains advanced features that human experts can not define intuitively. Among many neural networks, Recurrent Neural network(RNN) is structurally more suitable for processing sequences, which achieved excellent results in vulnerability detection. In 2017, Transformer is proposed in the field of Natural Language processing(NLP), which is based on Self-Attention mechanism, replaces traditional RNN in the way of text sequence processing, and is more effective than RNN in many natural language tasks. This paper proposes using Transformer to automatically detect vulnerabilities in Code Slices. Firstly, we extract Code Slices that are finer than the functional level, which can express the vulnerability patterns more accurately. Secondly, we propose an effective data representation method to retain more semantic information. Finally, the experiment proves that Transformer is superior to models based on RNN in terms of comprehensive performance, and the effective data representation can significantly improve the detection effect of deep neural networks.

关键词： Deep learning Codes Recurrent neural networks Data security Natural languages Semantics Transformers

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conference Organization PDP 2019

Conference Organization PDP 2019

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euromicro conference on parallel, distributed and network-based processing

conference Organization PDP 2019

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Base Station handover based on User Trajectory Prediction in 5G networks

Base Station handover Based on User Trajectory Prediction in...

引用

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

作者： Yuxiang Ma Xuefei Chen Lei Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In the 5G era, user equipment connected to 5G base stations can obtain better communication services. However, due to the limited coverage of base stations, the movement of users may cause frequent handover of base stations. With the widespread deployment of 5G base stations, how to reduce unnecessary handover times when users connect to the base station becomes particularly important. In recent years, user trajectory data has been mined and applied to many scenarios. In the 5G network, by judging the user’s movement trajectory, the number of handovers required for the user to connect to the 5G base station can be effectively reduced. In this paper, we propose a 5G base station handover method based on trajectory prediction. A CNN-LSTM neural network, which combines a Convolutional Neural network (CNN) and Long Short-Term Memory (LSTM) has been proposed to predict the user’s trajectory. The evaluation results show that our mechanism can effectively reduce the number of base station handovers and improve the efficiency of users using the network. In addition, the stability of 5G networks can be improved by reducing inefficient base station handover.

关键词： Base stations 5G mobile communication Time series analysis Handover Predictive models Prediction algorithms Feature extraction

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Performance Analysis of parallel and Scalable GPU based Convolutional Neural network

Performance Analysis of Parallel and Scalable GPU Based Conv...

引用

International conference on Computing in Engineering and Technology, ICCET 2019

作者： Chavan, Umesh Kulkarni, Dinesh Walchand College of Engineering Sangli India

ISBN: (纸本)9789813295148

Convolutional Neural networks (CNN) have succeeded great impact in various tasks of machine learning. Training CNN model is a computationally intensive task. Scalability and performance of CNN with GPU is demonstrated in this study using CUDA (Compute Unified Device Architecture) framework. We evaluated performance characteristics using our own designed CNN model. The model is configured for Facial expression Recognition classification task. The novelty of this effort is to demonstrate performance acceleration in scalable CNN. The parallel tasks using the hardware feature of general-purpose- computing on graphics processing unit (GPGPU) has been shown to be appropriate to be applied to CNN. We implemented at multi-node distributed training;which allows us to efficiently parallelize deep networks across multiple servers, in order to minimize the time to train. The experiment result shows that the proposed experiment in this study gained over 7 times speedup. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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Deep spatiooral attention model for grain storage temperature forecasting 26

Deep spatiooral attention model for grain storage temperatur...

引用

26th IEEE International conference on parallel and distributed Systems, ICPADS 2020

作者： Duan, Shanshan Yang, Weidong Wang, Xuyu Mao, Shiwen Zhang, Yuan Henan Key Laboratory of Grain Photoelectric Detection Control Key Laboratory of Grain Information Processing Control College of Information Science Engineering Henan University of Technology Zhengzhou Henan China California State University Department of Computer Science SacramentoCA95819-6021 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781728190747

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatiooral attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods. © 2020 IEEE.

关键词： Piles

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Fault Port Detection based on Hadoop

Fault Port Detection Based on Hadoop

引用

Computer Information Science and Artificial Intelligence (CISAI), International conference on

作者： Yueying Huang Xuefeng Yu Kai Lin College of Physics and Information Engineering Fuzhou University Fuzhou China China Telecom Fuzhou Branch Fuzhou China

ISBN: (纸本)9781665406932

With the continuous improvement of China’s broadband optical network capabilities, the focus of users’ attention has shifted to application perception. The problem of frequent broadband disconnection has long plagued first-line units and greatly affected users’ trust in broadband quality. Aiming at the problem of long processing time and difficult location of boardband frequent offline fault, this paper proposes a Hadoop-based fault port detection scheme. Hadoop distributed file system is used to store the user’s online and offline data, and MapReduce is used for parallel calculation. Then, the fault port is located through the correlation of time series. Finally, the data is transmitted to the database of the display system through Sqoop In the display system, the fault situation of the whole network users is analyzed. Frequent offline failures change from the original user’s application to community maintenance to the detection platform’s self discovery of fault ports, which is a successful practice of cloud network integration big data operation, and also reduces the burden of operation and maintenance personnel.

关键词： Technological innovation Correlation Time series analysis Display systems distributed databases Maintenance engineering Big Data

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TDM Edge Gateway: A Flexible Microservice-based Edge Gateway Architecture for Heterogeneous Sensors 25th

TDM Edge Gateway: A Flexible Microservice-Based Edge Gateway...

引用

25th International conference on parallel and distributed Computing (Euro-Par)

作者： Gaggero, Massimo Busonera, Giovanni Pireddu, Luca Zanetti, Gianluigi CRS4 Ctr Adv Studies Res & Dev Sardinia Edificio 1 I-09050 Pula CA Italy

ISBN: (纸本)9783030483401;9783030483395

How to effectively handle heterogeneous data sources is one of the main challenges in the design of large-scale research computing platforms to collect, analyze and integrate data from IoT sensors. The platform must seamlessly support the integration of myriads of data formats and communication protocols, many being introduced after the platform has been deployed. Edge gateways, devices deployed at the edge of the network near the sensors, communicate with measurement stations using their proper protocol, receive and translate the messages to a standardized format, forward the data to the processing platform and provide local data buffering and preprocessing. In this work we present the TDM Edge Gateway architecture, which we have developed to be used in research contexts to meet the requirements of being self-built, low-cost, and compatible with current or future connected sensors. The architecture is based on a microservice-oriented design implemented with software containerization and leverages publish/subscribe Inter Process Communication to ensure modularity and resiliency. Costs and construction simplicity are ensured by adopting the popular Raspberry Pi Single Board Computer. The resulting platform is lean, flexible and easy to expand and integrate. It does not pose constraints on programming languages to use and relies on standard protocols and data models.

关键词： Edge computing Sensor networks Embedded FIWARE

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Application of Optimized BP network in Bogie Condition Monitoring 13

Application of Optimized BP Network in Bogie Condition Monit...

引用

13th Asia Pacific Transportation Development conference - Resilience and Sustainable Transportation Systems

作者： Xu, Xi Yao, HuiMing Shanghai Univ Engn Sci Fac Urban Railway Transportat Shanghai Peoples R China

ISBN: (数字)9780784482902

ISBN: (纸本)9780784482902

The research and analysis of the technology of vehicle bogie condition monitoring combined with the basic principle of BP network is applied to the condition monitoring of vehicle bogies. The genetic algorithm is used to optimize the BP network, establish the optimal weight and threshold, and establish an ideal BP network model to complete the state monitoring. The dynamic model of the bogie is established by the knowledge of the dynamics of the rail vehicle, and the irregularity of the rail is used as an external stimulus to simulate the state of the vehicle running. Test analysis using an optimized BP network. It was found that the optimized BP network is more stable and accurate. With the continuous development of railway transportation, rail transit has occupied a very important position in the public transportation system. The safety and comfort of rail vehicles is an important factor affecting their development. The suspension system is a key component of the vehicle's travel department, and the performance of the suspension system directly affects the safety and comfort of the vehicle. The online real-time fault condition monitoring of the suspension system plays an important role in the safe and stable operation of the vehicle. Therefore, it is a hot research topic for domestic and foreign scholars to seek real-time and reliable suspension system fault diagnosis methods. At present, there are many fault diagnosis methods for rail vehicle suspension systems at home and abroad. There is a vehicle suspension system fault diagnosis based on IMM algorithm, vehicle suspension system fault diagnosis based on observation method, etc. The fault can only be alerted, and the fault cannot be further determined. Artificial neural network algorithm is a discipline developed on the basis of modern neurology. BP neural network is the most widely used. It adopts parallel distributed processing, learning and memory function, and nonlinear mapping ability, which can perform mu

关键词： Genetic algorithms

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