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

共 4337 条记录，以下是1421-1430 订阅

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CUDA Optimization Method for Activation Function in Convolution Operation

CUDA Optimization Method for Activation Function in Convolut...

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

作者： Lubin Feng Dulei Zheng Jianzhi Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Deep learning is an important field of computer research. Convolutional neural networks have received much attention in the field of image recognition because of their excellent recognition ability. The neural network contains a large number of parameters. For the training time-consuming problem of convolutional neural networks, how to effectively realize the computational acceleration in the training process is one of the hot topics in the field of deep learning research. In this paper, two parallel algorithms are designed based on single-thread block multi-thread and multi-thread block strategies. The former is a parallel algorithm between activation functions, and the latter is based on the former, and the parallelization algorithm inside the activation function. Finally, this paper designed a contrast experiment to verify the algorithm. The experimental results show that both algorithms have achieved a good acceleration effect, so that the GPU resources can be fully utilized to achieve the purpose of computational acceleration.

关键词： Instruction sets Graphics processing units Kernel Convolution Neurons Acceleration Training

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A Large-Change Sensitivity based Approach for distributed Harmonic Resonance Assessment in Multi-Area Interconnected Power Systems

A Large-Change Sensitivity Based Approach for Distributed Ha...

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IEEE Power and Energy conference at Illinois (PECI)

作者： Mazhari, Seyed Mahdi Sahami, Amirreza Kouhsari, Shahram Montaser Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK Canada UNC Charlotte Dept Elect & Comp Engn Charlotte NC USA Amirkabir Univ Technol Dept Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781538641163

This paper presents a distributed simulation based method for harmonic resonance assessment (HRA) in multi-area large-scale power systems. Further consideration is devoted to the early harmonic frequency-scan formulation to shape them into a Bordered Blocked Diagonal Form (BBDF), which is suitable for parallel processing. The proposed algorithm (BBDF) allows operator of each area of an interconnected system to independently conduct the HRA. A large-change sensitivity based approach is then handled in a secure platform to apply the effects of whole network to each single area. The introduced decentralized HRA is capable to find the exact values as those of the interconnected system through TCP/IP communication media. The developed method is successfully implemented in an existing software package and applied to IEEE 14-bus harmonic test system, followed by a discussion on results.

关键词： distributed simulation frequency-domain (FD) frequency-scan technique (FST) Harmonic Resonance Assessment (HRA) Large-Change Sensitivity (LCS)

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parallelization of Machine Learning Applied to Call Graphs of Binaries for Malware Detection 25

Parallelization of Machine Learning Applied to Call Graphs o...

引用

25th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Searles, Robert Xu, Lifan Killian, William Vanderbruggen, Tristan Forren, Teague Howe, John Pearson, Zachary Shannon, Corey Simmons, Joshua Cavazos, John Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA

ISBN: (纸本)9781509060580

Malicious applications have become increasingly numerous. This demands adaptive, learning-based techniques for constructing malware detection engines, instead of the traditional manual-based strategies. Prior work in learning-based malware detection engines primarily focuses on dynamic trace analysis and byte-level n-grams. Our approach in this paper differs in that we use compiler intermediate representations, i.e., the call-graph representation of binaries. Using graph-based program representations for learning provides structure of the program, which can be used to learn more advanced patterns. We use the Shortest Path Graph Kernel (SPGK) to identify similarities between call graphs extracted from binaries. The output similarity matrix is fed into a Support Vector Machine (SVM) algorithm to construct highly-accurate models to predict whether a binary is malicious or not. However, SPGK is computationally expensive due to the size of the input graphs. Therefore, we evaluate different parallelization methods for CPUs and GPUs to speed up this kernel, allowing us to continuously construct up-to-date models in a timely manner. Our hybrid implementation, which leverages both CPU and GPU, yields the best performance, achieving up to a 14.2x improvement over our already optimized OpenMP version. We compared our generated graph-based models to previously state-of-the-art feature vector 2-gram and 3-gram models on a dataset consisting of over 22,000 binaries. We show that our classification accuracy using graphs is over 19% higher than either n-gram model and gives a false positive rate (FPR) of less than 0.1%. We are also able to consider large call graphs and dataset sizes because of the reduced execution time of our parallelized SPGK implementation.

关键词： Malware Kernel Feature extraction Support vector machines Machine learning algorithms Engines Graphics processing units

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A General Fusion System and Maximal-Ratio Combining Fusion Rule in Unmanned Air Vehicle network 23

A General Fusion System and Maximal-Ratio Combining Fusion R...

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23rd IEEE International conference on Digital Signal processing (DSP)

作者： Xu, Shufang Xu, Dazhuan Wang, Huibin Mao, Yingchi Zhang, Xuejie Wang, Longbao Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538668122;9781538668115

The use of small and miniature unmanned air vehicles(UAVs) for remote sensing and detecting applications has become increasingly popular in recent years. The intermittent connectivity in a dynamically mobile UAV network (UAVN) makes it challenging to efficiently gather sensed target data. distributed parallel detection and centralized fusion rules in classical fusion systems are based on global message connectivity. This paper investigates the communication of sensed data from a set of mobile survey UAVs to a fusion center in large indoor or outdoor severe environment. Given the dynamic connectivity of links in UAV network, a general model of Fusion System of UAV network (FS-UAVN) is proposed to schedule the UAVs to collect detection data. based on this FS-UAVN model, a specific fusion method named Maximal-ratio Combining Fusion Rule (MRC-FR) is provided for the fusion center. MRC-FR utilizes the theory of Maximal Ratio Combiner (MRC) to discuss the fusion performance in view of link connectivity. Evaluation shows that the proposed MRC-FR can realize the centralized fusion system with simpler formulas and express the numerical relationship between outage probability, outage capacity, connectivity probability, signal-to-noise ratio of channel, and so on.

关键词： Unmanned Air Vehicle network (UAVN) connectivity information fusion fusion rule

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More Effective distributed Deep Learning Using Staleness based Parameter Updating 18th

More Effective Distributed Deep Learning Using Staleness Bas...

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18th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Ye, Yan Chen, Mengqiang Yan, Zijie Wu, Weigang Xiao, Nong Sun Yet Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou Peoples R China MoE Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9783030050542;9783030050535

Deep learning technology has been widely applied for various purposes, especially big data analysis. However, computation required for deep learning is getting more complex and larger. In order to accelerate the training of large-scale deep networks, various distributed parallel training protocols have been proposed. In this paper, we design a novel asynchronous training protocol, Weighted Asynchronous parallel (WASP), to update neural network parameters in a more effective way. The core of WASP is "gradient staleness", a parameter version number based metric to weight gradients and reduce the influence of the stale parameters. Moreover, by periodic forced synchronization of parameters, WASP combines the advantages of synchronous and asynchronous training models and can speed up training with a rapid convergence rate. We conduct experiments using two classical convolutional neural networks, LeNet-5 and ResNet-101, at the Tianhe-2 supercomputing system, and the results show that, WASP can achieve much higher acceleration than existing asynchronous parallel training protocols.

关键词： distributed deep learning parallel computing Parameter server Asynchronous parallel Supercomputing system

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Improving Short Text Classification Using Fast Semantic Expansion on Multichannel Convolutional Neural network 19

Improving Short Text Classification Using Fast Semantic Expa...

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19th IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, networking and parallel/distributed Computing (SNPD)

作者： Sotthisopha, Natthapat Vateekul, Peerapon Chulalongkom Univ Fac Engn Dept Comp Engn Big Data Analyt & IoT Ctr CUBIC Bangkok Thailand

ISBN: (纸本)9781538658895

Nowadays, text classification is recognized as one of crucial tools for business users to gain more insights from customers. However, textual data from customers such as comments are usually short. Thus, there are two main issues in short text categorization: (1) insufficient contextual information and (2) noisy data due to misspellings. Recently, there is a prior attempt to propose a deep learning approach for short text categorization by expanding semantic using word embeddings clustering via an algorithm based on density peaks searching. Since the number of words in word embeddings is usually large, the clustering algorithm does not scale with the size of data set, thus demanding unacceptable computational cost. In this paper, we aim to propose a fast short-text categorization framework. Rather than using a CNN with k- max pooling layer, we propose to use a faster version of Convolutional Neural network (CNN) called "multichannel CNN." To speed up the semantic expansion process, we propose to employ mini batch K-Means++, which is considerably faster and scales well with the size of data set. Furthermore, we also introduce an additional preprocessing step to increase vocabulary coverage rate on word embeddings. We conducted experiments on four public data sets: Google Snippets, TREC, MR, and Subj. The results showed that the proposed framework does not only improve an accuracy on all data sets, but also reduces computational costs from several days to a few hours.

关键词： Short Texts Natural Language processing Text Classification Clustering Convolutional Neural network Word Embeddings Deep Learning

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parallel and distributed Intra Query Transient Fault Tolerance Model via Parity Checking 14

Parallel and Distributed Intra Query Transient Fault Toleran...

引用

14th International conference on Electronics Computer and Computation (ICECCO)

作者： Yusuf, Salisu Ibrahim Junaidu, Sahalu B. Nile Univ Nigeria Dept Comp Sci Abuja Nigeria Ahmadu Bello Univ Dept Comp Sci Abuja Nigeria

ISBN: (纸本)9781728101323

In a distributed parallel query execution, complex queries are executed by splitting them into partially related simple subqueries executing each on a different node, communication between machines is often done by message exchange for shared-nothing architecture based grid. Integrity of messages can be lost by temporary or permanent interference along the communication network. Fault tolerance strategies are used to keep the system running in the presence of fault. This is traditionally done through query restart, replication or check pointing and other variations of these approaches to improve latency, restoration time and reduce cost of execution. These processes include: monitoring, detection and tolerance. Transient faults are caused by interference in the medium of exchange which may pass undetected but yielding an incorrect query result. Moreover, the traditional fault tolerance, there is a strong dependency between the nodes. In this research we proposed a model of a fault tolerance strategy that will allow self-detection and tolerate transient fault with less dependency between nodes. The model will be compared with the tradition strategies in terms of detection ability, inter node dependency, and cost of execution.

关键词： distributed Query processing intra-query fault tolerance transient fault shared nothing parallel query execution

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Astronomical Data Application Research based On MapReduce 3

Astronomical Data Application Research Based On MapReduce

引用

3rd IEEE Advanced Information Technology, Electronic and Automation Control conference (IAEAC)

作者： Cui, Qingfa Wu, Sheng Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China

ISBN: (纸本)9781538645093

MapReduce as an abstract distributed computing programming model could solve the issues of parallel computing, such as load balancing, network storage, data distribution, resource allocation, fault tolerance. This makes it easy for people to manipulate large scale cluster systems without considering hardware details. The paper discusses completely how to apply MapReduce. In the construction of the experimental platform, this paper successfully designs and implements the cone search service based on MapReduce, The final result proves that the astronomical data application method based on MapReduce greatly improves the processing capacity.

关键词： MapReduce Astronomical data Cone search service

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A stock forecasting method based on combination of SDAE and BP 6

A stock forecasting method based on combination of SDAE and ...

引用

International conference on Orange Technologies (ICOT)

作者： Li, Zheng Dang, Xin Tianjin Polytech Univ Sch Comp Sci & Software Engn Tianjin Peoples R China

ISBN: (纸本)9781538673195

The neural network, as a non-linear system with large-scale parallel distributed processing, has been widely used in the field of prediction. This paper proposes a stock forecasting method based on the combination of Stacked Denoising AutoEncoders (SDAE) and Back Propagation Neural network (BPNN). Using more than 4100 historical data of Sinopec Group from 2001/8/8 to 2018/8/16 for Seventeen years, then process the data and select six features of them for training and prediction. The experiment first uses SDAE for training in the pre-training stage to obtain the weights. Then the trained weights of SDAE are assigned to the Back Propagation Neural network (BPNN) for fine-tuning to optimize the entire network structure. Then optimize the network parameters for further step and chose the best optimal hyperparameter combination. Finally, the prediction is performed on the test set, the Mean Absolute Error (MAE), Error Variance (EV), and Absolute Maximum Error (AME) between the predicted value and the actual value are used as evaluation performance evaluation criteria. The results show that this neural network model can achieve high precision, and provides an effective method for predicting the stock market artificial neural network with many influencing factors and unclear mechanism.

关键词： Neural network Stock Prediction Stacked Denoising AutoEncoder (SDAE) Back Propagation Neural network (BPNN)

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Improved Fast Stereo Matching Algorithm based on Convolutional Neural networks

Improved Fast Stereo Matching Algorithm Based on Convolution...

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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： Chunfeng Xu Shiyu Lu Wen Liu Ye Bai Cheng Han School of Computer Science and Technology Changchun University of Science and Technology Changchun China College of Optical and Electronical Information Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781728152103

Various stereo matching algorithms are widely used in many fields nowadays. In order to solve low speed problem in convolutional neural network for stereo matching algorithms (MC-CNN-fst), an improved stereo matching algorithm based on convolutional neural network was proposed. By modifying pooling layers and normalization layer, the structure of the convolutional neural network has been reformed. Among them, different pooling layers are introduced to retain more image information, and the normalization layer is introduced to accelerate the speed of convergence of training, and finally the disparity map is obtained through subsequent processing steps. To evaluate the performance of the proposed algorithm and the original one under the same experimental environment. We conduct the experiment on two public datasets: KITTI and Middlebury. Experimental results demonstrate that the refinement of the network structure boosts the speed for the feature extraction procedure by 50%, and the total operation time is reduced by 12.5%.

关键词： convolutional neural nets feature extraction image matching stereo image processing Stereo image processing Feature extraction image matching Neural network operation time algorithms image information normalization network STRUCTURE

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