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检索条件"机构=Science and Technology on Parallel and Distribution Processing Laboratory"

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Notice of Retraction: parallel block multigrid preconditioner for 3D Navier-Stokes equations on unstructured grids

Notice of Retraction: Parallel block multigrid preconditione...

引用

International Conference on Computer Application and System Modeling (ICCASM)

作者： Zongzhe Li Lu Yao Wei Cao Yongxian Wang Zhenghua Wang School of Computer Science Parallel and Distributed Processing for National Laboratory National University of Defense Technology Changsha Hunan China

This article has been retracted by the publisher.

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A Discrete Data Dividing Approach for Erasure-Code-Based Storage Applications

A Discrete Data Dividing Approach for Erasure-Code-Based Sto...

引用

2014 IEEE 8th International Symposium on Service Oriented System Engineering

作者： Weidong Sun Yijie Wang Yongquan Fu Xiaoqiang Pei Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan P. R. China

Erasure codes are promising for improving the reliability of the storage system due to its space efficiency compared to the replication methods. Traditional erasure codes split data into equalsized data blocks and encode strips in different data blocks. This brings heavy repairing traffic when clients read parts of the data, since most strips read for repairing are not in the expected blocks. This paper proposes a novel discrete data dividing method to completely avoid this problem. The key idea is to encode strips from the same data block. We could see that for repairing failed blocks, the strips to be read are either in the same data block with corrupted strips or from the encoded strips. Therefore, no data is wasted. We design and implement this data layout into a HDFS-like storage system. Experiments over a small-scale testbed shows that the proposed discrete data divided method avoids downloading data blocks that are not needed for clients during the repairing operations.

关键词： Strips Decoding Encoding Distributed databases Writing Reliability Magnetic heads

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Research of Context Situation Awareness technology

Research of Context Situation Awareness Technology

引用

IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORCW)

作者： Dianxi Shi Zhendong Wu Bo Ding Huining Yan National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

Context situation, which means a snapshot of the status of the real world, is formed by integrating a large amount of contexts collected from various resources. How to get the context situation and use the situation to provide better services is a challenging issue. In this paper, we focused on this challenge on the basis of the mobile cloud computing architecture. An abstract model is proposed in this paper to uniformly collect the context and send them to cloud. A rule-based large-scale context aggregation algorithm is also proposed which utilizes the MapReduce computing paradigm. Finally, a large-scale context management framework based on the abstract model and the context aggregation algorithm is proposed, and a real-time traffic demo is implemented to verify the validity of the framework.

关键词： Context Mobile handsets Real time systems Sensors Servers Mobile communication Context modeling

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High-performance Audio Matching with Features Learned by Convolutional Deep Belief Network

High-performance Audio Matching with Features Learned by Con...

引用

2016 IEEE 13th International Conference on Signal processing（ICSP2016）

作者： Weijiang Feng Naiyang Guan Zhigang Luo Institute of Software College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Audio matching automatically retrieves all excerpts that have the same content as the query audio clip from given audio recordings. The extracted feature is critical for audio matching and the Chroma Energy Normalized Statistics(CENS) feature is the state-of-the-arts. However, CENS might behave unsatisfactorily on some audio because it is a handcraft feature. In this paper, we propose to utilize the features learned by Convolutional Deep Belief Network(CDBN) to enhance the performance of audio matching. Benefit from the strong generalization ability of CDBN, our method works better than CENS based methods on most audio datasets. Since the features learned by CDBN are binary-valued, we can develop a more efficient audio matching algorithm by taking the advantage of this property. Experimental results on both TIMIT dataset and a simulated music dataset confirm effectiveness of the proposed CDBN based method comparing with the traditional CENS feature based algorithm.

关键词： audio matching convolutional deep belief network content-based audio retrieval

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App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffi...

引用

IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Xin Wang Shuhui Chen Jinshu Su College of Computer Science and Technology National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

With the exponential growth of mobile traffic data, mobile traffic classification is in a great need. It is an essential step to improve the performance of network services such as QoS and security monitoring. However, the widespread use of encrypted protocols, especially the TLS protocol, has posed great challenges to traditional traffic classification techniques. As the rule-based deep packet inspection approaches are ineffective for encrypted traffic classification, various machine learning methods have been studied and used. Recently, deep learning solutions which enable automatic feature extraction are also proposed to classify encrypted traffic. In this paper we propose App-Net, an end-to-end hybrid neural network, to learn effective features from raw TLS flows for mobile app identification. App-Net is designed by combining RNN and CNN in a parallel way. So that it can learn a joint flow-app embedding to characterize both flow sequence patterns and unique app signatures. We evaluate App-Net on a real-world dataset that covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.

关键词： Cryptography Feature extraction Payloads Recurrent neural networks Machine learning Convolutional neural networks

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Flexible Virtual Channel Power-Gating for High-Throughput and Low-Power Network-on-Chip

Flexible Virtual Channel Power-Gating for High-Throughput an...

引用

Euromicro Symposium on Digital System Design

作者： Feng Wang Xiantuo Tang Qinglin Wang Zuocheng Xing Hengzhu Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan P. R. China

Power-gating is a representative circuit level technique to mitigate leakage power. While in low-power Network-on-Chip (NoC) design, the former fine-grained power-gating methods will decrease network performance due to serial wake-up latency and head-of-line blocking. Therefore, we propose a flexible Virtual Channel (VC) management scheme for fine-grained power-gating to achieve high throughput and low-power. The proposed power-gating method with the early wake-up is evaluated by using some synthetic workloads. When compared with an optimized early wake-up power-gating technique, it can improve performance effectively in medium and high network loads, and increases the network throughput by 15.7%~44.1% for different synthetic loads, while keeps network power consumption as low as the optimized method. For the PARSEC application traces of token based protocol, it can significantly decrease packet latency by 20.3% on average, however only increases less than 3.6% peak power when compared with the optimized method.

关键词： Throughput Radiation detectors Routing Process control Pipelines Power demand Ports (Computers)

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A Semantic-based Meteorology Grid Service Registry, Discovery and Composition Framework

A Semantic-based Meteorology Grid Service Registry, Discover...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Kaijun Ren Kaijun Ren Nong Xiao Nong Xiao Junqiang Song Junqiang Song Weimin Zhang Weimin Zhang Tao Chen Tao Chen National Laboratory for Parallel and Distributed Processingm College of Science National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China

Meteorology Grid Computing aims to provide scientist with seamless, reliable, secure and inexpensive access to meteorological resources. In this paper, we presented a semantic-based meteorology grid service registry, discovery and composition framework by combining grid technologies and the advantages of semantic web techniques. The main objective of the framework is to support automating the discovery, selection, and workflow composition of semantically described heterogeneous meteorological grid services, which offers the possibility of facilitating geographically distributed meteorological scientists to resolve complex scientific problems cooperately. With this framework, the key technologies such as semantic registry, semantic matchmaking, QoS ranking and composition model, will be discussed.

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Hard Contrastive Learning for Video Captioning

Hard Contrastive Learning for Video Captioning

引用

IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Lilei Wu Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China

ISBN: (纸本)9781665487900

Maximum likelihood estimation has been widely adopted along with the encoder-decoder framework for video captioning. However, it ignores the structure of sentences and restrains the diversity and distinction of generated captions. To address this issue, we propose a hard contrastive learning (HCL) method for video captioning. Specifically, built on the encoder-decoder framework, we introduce mismatched pairs to learn a reference distribution of video descriptions. The target model on the matched pairs is learned on top the reference model, which improves the distinctiveness of generated captions. In addition, we further boost the distinctiveness of the captions by developing a hard mining technique to select the hardest mismatched pairs within the contrastive learning framework. Finally, the relationships among multiple relevant captions for each video is consider to encourage the diversity of generated captions. The proposed method generates high quality captions which effectively capture the specialties in individual videos. Extensive experiments on two benchmark datasets, i.e., MSVD and MSR-VTT, show that our approach outperforms state-of-the-art methods.

关键词： Maximum likelihood estimation Visualization Video description Benchmark testing

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OpenMP-Based Monte Carlo Dose Calculation for Radiotherapy Treatment Planning on the Intel MIC Architecture

OpenMP-Based Monte Carlo Dose Calculation for Radiotherapy T...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Qinglin Wang Jie Liu Peizhen Xie Chunye Gong Yuan Li Zuocheng Xing School of Computer Science National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Monte Carlo (MC) simulation plays an important part in dose calculation for radiotherapy treatment planning. Since the accuracy of MC simulation relies on the number of simulated particles histories, it's very time-consuming. The Intel Many Integrated Core (MIC) architecture, which consists of more than 50 cores and supports many parallel programming models, provides an efficient alternative for accelerating MC dose calculation. This paper implements the OpenMP-based MC Dose Planning Method (DPM) for radiotherapy treatment problems on the Intel MIC architecture. The implementation has been verified on the target MIC coprocessor including 57 cores. The results demonstrate that the OpenMP-based DPM implementation exhibits very accurate results and achieves the maximum speedup of 10.53 times in comparison to the original DPM one on a Xeon E5-2670 CPU. Additionally, speedup and efficiency of the implementation running on the different number of cores in MIC are also reported.

关键词： Microwave integrated circuits Instruction sets Computer architecture Photonics Computational modeling History Interpolation

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Optimizing One by One Direct Convolution on ARMv8 Multi-core CPUs

Optimizing One by One Direct Convolution on ARMv8 Multi-core...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Qinglin Wang Dongsheng Li Songzhu Mei Siqi Shen Xiandong Huang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China School of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781728169811

ISBN: (纸本)9781728169828

Convolutional layers are ubiquitous in a variety of deep neural networks. Due to the lower computation complexity and the smaller number of parameters, convolutions with small filter sizes are often used, such as one by one convolution. Nevertheless, these small convolution operations are still time-consuming. A common approach to implementing convolutions is to transform them into matrix multiplications, known as GEMM-based convolutions. The approach maybe incurs additional memory overhead and calls matrix multiplication routines, which are not optimized for matrices generated by convolutions. In this paper, we present a new parallel one by one direct convolution implementation on ARMv8 multi-core CPUs, which doesn't incur any additional memory space requirement. Our implementation is verified on two ARMv8 CPUs, Phytium FT-1500A and FT-2000plus. In terms of performance and scalability, our implementation is better than GEMM-based implementations in all the tests on Phytium FT-1500A. On Phytium FT-2000plus, our approach gives much better performance and scalability than GEMM-based approaches in most cases.

关键词： Convolution Registers Layout Neural networks parallel processing Computer architecture Tensile stress

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