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High Performance Interconnect Network for Tianhe System

Journal of Computer Science & Technology 2015年第2期30卷 259-272页

作者：廖湘科庞征王克非卢宇彤谢旻夏军董德尊所光 College of Computer National University of Defense Technology Changsha 410073 Science and Technology on Parallel and Distributed Processing Laboratory National Changsha 410073 China China University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China

In this paper, we present the Tianhe-2 interconnect network and message passing services. We describe the architecture of the router and network interface chips, and highlight a set of hardware and software features effectively supporting high performance communications, ranging over remote direct memory access, collective optimization, hardwareenable reliable end-to-end communication, user-level message passing services, etc. Measured hardware performance results are also presented.

关键词： Tianhe-2 supercomputer interconnect network router architecture network interface architecture user-level message passing

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Modeling complex relationship paths for knowledge graph completion

Modeling complex relationship paths for knowledge graph comp...

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作者： Zeng, Ping Tan, Qingping Meng, Xiankai Zhang, Haoyu Xu, Jianjun College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing Changsha China

Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translationmodels obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task. © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Knowledge graph

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Ladder: A Blockchain Model of Low-Overhead Storage 3rd

Ladder: A Blockchain Model of Low-Overhead Storage

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3rd International Conference on Blockchain and Trustworthy Systems, Blocksys 2021

作者： Chen, Peng Zhang, Xunhui Liu, Hui Xiang, Liangliang Shi, Peichang National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789811679926

Emerging blockchain accounting mechanism allow mutually distributed parties to transport trusted information and ensure the correctness of data. Every blockchain node stores the complete block locally. Although this measure improves security, it causes huge storage overhead. We present Ladder, a multi-level low-overhead storage model for blockchain, which can greatly reduce the storage overhead of nodes while ensuring that the blockchain cannot be tampered easily and the block information is complete. Ladder distinguishes the retrieved frequency and importance of different blocks by value-density, dividing them into Hot-Blocks, Warm-Blocks and Cold-Blocks adopting different storage strategies respectively. Through experimental verification of time and space cost of different storage strategies, Ladder can greatly reduce the local storage overhead of nodes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Research of context situation awareness technology

Research of context situation awareness technology

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作者： Shi, Dianxi Wu, Zhendong Ding, Bo Yan, Huining National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9780769546698

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. © 2012 IEEE.

关键词： MapReduce

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Resource Allocation with Dynamic Substrate Network in Data Centre Networks

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China Communications 2013年第9期10卷 130-142页

作者：李小玲王怀民丁博李小勇刘惠 National Key Laboratory for Parallel and Distributed Processing School of Computer ScienceNational University of Defense Technology

The resource allocation problem in data centre networks refers to a map of the workloads provided by the cloud users/tenants to the Substrate Network(SN)which are provided by the cloud *** studies consider the dynamic arrival and departure of the workloads,while the dynamics of the substrate are *** this paper,we first propose the resource allocation with the dynamic SN,and denote it as ***,we propose an efficient mapping algorithm for *** performance of the proposed algorithm is evaluated by performing simulation *** results show that the proposed algorithm can effectively solve the GraphMap-DS.

关键词： resource allocation data centrenetwork workload substrate algorithm

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On the feasibility of common-friend measurements for the distributed online social networks

On the feasibility of common-friend measurements for the dis...

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2012 7th International ICST Conference on Communications and Networking in China, CHINACOM 2012

作者： Fu, Yongquan Wang, Yijie National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Hunan province 410073 China

ISBN: (纸本)9781467326995

distributed social networks have emerged recently. Nevertheless, recommending friends in the distributed social networks has not been exploited fully. We propose FDist, a distributed common-friend estimation scheme that estimates the number of common-friends between any pair of users without disclosing the friends' information. FDist uses privacy-preserving common-friend measurements to collect a small number of common-friend samples, and uses low-dimensional coordinates to estimate the number of common friends to other users. Simulation results on real-world social networks confirm that FDist is both scalable and accurate. © 2012 IEEE.

关键词： Social networking (online)

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BCE: A privacy-preserving common-friend estimation method for distributed online social networks without cryptography

BCE: A privacy-preserving common-friend estimation method fo...

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2012 7th International ICST Conference on Communications and Networking in China, CHINACOM 2012

作者： Fu, Yongquan Wang, Yijie National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Hunan province 410073 China

ISBN: (纸本)9781467326995

distributed online social networks (DOSN) have emerged recently. Nevertheless, recommending friends in the distributed social networks has not been exploited fully. We propose BCE (Bloom Filter based Common-Friend Estimation), a scalable and privacy-preserving common-friend estimation scheme that estimates the set of common friends without the need of cryptography techniques. First, BCE denotes each user using the identifiers created by the Peer-to-Peer underlay that are robust against the dictionary attacks. Second, BCE uses a Bloom filter to represent a friend list for scalability. Third, BCE estimates common friends of two users using the intersection of Bloom filters computed by one of their common friends, which ensures the privacy of friend lists against unknown users. Our privacy analysis shows that BCE hides the privacy of each user with a high probability. Simulations over real-world social-network data sets confirms that BCE is both accurate and scalable. © 2012 IEEE.

关键词： Social networking (online)

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parallelization of the training for face detection with transactional memory

Parallelization of the training for face detection with tran...

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2011 International Conference on Automation and Robotics, ICAR 2011

作者： Zeng, Kun Key Laboratory and Technology for National Defence of Parallel and Distributed Processing School of Computer National Univiersity of Defense Technology ChangSha Hunan China

ISBN: (纸本)9783642256455

The development of multi-core processor makes the parallelization of traditional sequential algorithms increasingly important. Meanwhile, transactional memory serves a good parallel programming model. This paper takes the advantage of software transactional memory to parallelize the Multi-Exit Asymmetric Adaboost algorithm for face detection. The parallel version is evaluated on three different implementations of software transactional memory. The experiment results show that the transactional memory based parallelization outperforms the traditional lock based approach. A speedup of nearly seven is achieved on a eight-core machine on an eight-core system. © 2011 Springer-Verlag.

关键词： Face recognition

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AUModel: A conceptual model for adaptive software 5

AUModel: A conceptual model for adaptive software

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2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014

作者： Liu, Hui Ding, Bo Shi, Dianxi Wang, Huaimin National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781479932788

Pervasive software should be able to adapt itself to the changing environments and user requirements. Obviously, it will bring great challenges to the software engineering practice. This paper proposes AUModel, a conceptual model for adaptive software, which takes adaptability as an inherent feature and can act as the foundation of the engineering process. By introducing AUModel, the reuse of software adaptation infrastructure as well as the separation of adaptation concerns are enabled, which can facilitate both the development and maintenance of adaptive software. This paper also presents our initial attempts to realize this model, including a middleware prototype to support this model and an application to validate its effectiveness. © 2014 IEEE.

关键词： Middleware

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Research on Fine-Grained Sentiment Classification 8th

Research on Fine-Grained Sentiment Classification

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8th CCF International Conference on Natural Language processing and Chinese Computing, NLPCC 2019

作者： Wang, Zhihui Wang, Xiaodong Chang, Tao Lv, Shaohe Guo, Xiaoting National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

ISBN: (纸本)9783030322359

Aiming at the fine-grained sentiment classification that distinguishes the emotional intensity, the commonly used dataset SST-1 is analyzed in depth. Through the analysis, it is found that the dataset has serious problems such as data imbalance and small overall scale, which seriously restricts the classification effect. In order to solve the related problems, data augmentation method is adopted to realize the optimization of the dataset. The IMDB and other data which are relatively homologous to the original dataset are annotated, and the focus is to expand the categories with fewer numbers. By this way, the problem of data imbalance is effectively alleviated and the original data scale is expanded. Then, based on the Bidirectional Encoder Representations from Transformers (BERT) model, which has good overall performance on natural language processing, the benchmark classification model is built. Through multiple comparison experiments on the original dataset and the enhanced data, the influence of the deficiency of the original dataset on the classification effect is verified. And, it is fully demonstrated that the enhanced data can effectively improve the test results and solve the problem of large differences in performance between different categories well. © 2019, Springer Nature Switzerland AG.

关键词： Classification (of information)

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