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The International Conference on Automation and Robotics(ICAR 2011)

作者： Kun Zeng Key Laboratory and technology for National Defence of Parallel and Distributed Processing School of Computer National Univiersity of Defense Technology

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.

关键词： Transactional Memory Face Detection Machine Learning Optimistic Algorithm Pessimistic Algorithm

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Optimization techniques of on-chip memory system based on UltraSPARC architecture

Optimization techniques of on-chip memory system based on Ul...

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Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics (PrimeAsia)

作者： Anwen Huang Jun Gao Chaochao Feng Minxuan Zhang Key Laboratory of Science and Technology of National Defense of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

It is of great significance to optimize the design of on-chip memory system for improving the performance of multi-core processor. This paper describes the organization of on-chip memory hierarchy in UltraSPARC T2, analyzes the procedure of dealing with the memory access request from multi-core processor in detail. To get the basic parameters of timing characteristic and area consumption, the primary element of on-chip memory system is simulated by ModelSim and synthesized by Synopsys Design Complier with 90 nm standard cell library. Aimed at the potential limiting factor of improving the performance of memory hierarchy, corresponding optimization techniques of critical elements in memory system are explored finally.

关键词： System-on-a-chip Elliptic curve cryptography Hardware Costs Algorithm design and analysis Random access memory Clocks Registers Elliptic curves Protection

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Fault Recovery Based on parallel Recomputing in Transactional Memory System

Fault Recovery Based on Parallel Recomputing in Transactiona...

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The 2011 International Conference on Electric and Electronics(EEIC 2011)

作者： Wei Song Jia Jia National Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology

This paper addresses the issue of fault recovery in transactional memory,and proposes a method of fault recovery based on parallel recomputing in transactional memory *** method utilizes the dataversioning mechanism of transactional memory system to avoid the extra cost of state saving,rolls back a single transaction to avoid wasting the computing time of the fault-free transactions,and adopts the parallel recomputing method to reduce the cost of fault *** paper applies this method to Open TM programs,and proposes the implementation method of parallel recomputing in Open *** last,this paper tests the performance of this method through a test *** experimental results show that,compared with the fault recovery method of rolling back a single transaction,the parallel recomputing method in transactional memory system can execute the fault recovery quickly and accurately and the method has a well scalability.

关键词： Fault tolerance parallel recomputing Fault Recovery Transactional memory

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Cooperative monitoring BGP among autonomous systems

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Security and Communication Networks 2015年第10期8卷 1943-1957页

作者： Hu, Ning Wang, BaoSheng Liu, Xin National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha Hunan China

As the de facto Internet inter-domain routing protocol, BGP protocol has a number of vulnerabilities and weakness. Monitoring BGP is an effective way to improve the security of inter-domain routing. This paper presents a cooperative BGP monitoring method, which is called cooperative information sharing model (CoISM). CoISM is based on self-organization and can be used to solve some problems during BGP monitoring, such as route validation and bogus route notification delivery. CoISM provides autonomous systems with a more comprehensive information view by introducing information diffuse reflection based on initiative inquiry and making use of the relativity of monitoring information. CoISM optimizes the information transmission by leveraging the data locality caused by BGP policy and implements ISP coordination with low communication and deployment cost. More specifically, CoISM provides a self-organizing and incentive mechanism, which drives autonomous systems to coordinate independently and shares information on-demand. CoISM supports incremental deployment and can also be applied to a wide range of inter-domain cooperative management applications such as inter-domain routing failure analysis and intrusion detection. © 2014 John Wiley & Sons, Ltd.

关键词： Monitoring

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Accelerate Graph Neural Network Training by Reusing Batch Data on GPUs

Accelerate Graph Neural Network Training by Reusing Batch Da...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Zhejiang Ran Zhiquan Lai Lizhi Zhang Dongsheng Li National Key Laboratory of Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665443326

With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes’ data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79× over the state-ofthe-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.

关键词： Training Productivity Deep learning Design methodology Conferences Graphics processing units Programming

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A study of strategies to restrain the C&C activities of structured P2P botnets

A study of strategies to restrain the C&C activities of stru...

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International Conference on computer sciences and Convergence Information Technology (ICCIT)

作者： Tianzuo Wang Bo Liu Huaimin Wang Hao Ren Xiaolong Ma School of Computer Science National Laboratory for Parallel and Distributed Processing National University of Defense and Technology Changsha China

Botnets are threatening the Internet heavily, and more and more botnets are utilizing the P2P technology to build their C&C (Command and Control) mechanisms. Some research have been made to compare the resilience of structured P2P botnets and unstructured ones, against elimination of nodes, but the problem that which eliminating strategy is the best is rarely studied. In this paper, we proposed a new metric called the half point, to measure the effectiveness of different strategies. We also selected seven different eliminating strategies and compared them. Through extensive simulations, we find that RBC is the best eliminating strategy. Further analysis shows that for the strategy RBC, the average degree of nodes in the botnet have the most significant influence. The bigger the average degree is, the bigger the half point of RBC is, which implies that node eliminating may not be a reasonable choice for mitigating botnets with big average degree. Results of this paper can provide guidance for restraining structured P2P botnets.

关键词： C&C mechanism betweenness centrality coreness eliminating strategy half point restrain structured P2P botnets

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TeeSwap: Private Data Exchange using Smart Contract and Trusted Execution Environment 23

TeeSwap: Private Data Exchange using Smart Contract and Trus...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Peng Shi, Peichang Xu, Jie Fu, Xiang Li, Linhui Zhong, Tao Xiang, Liangliang Kong, Jinzhu College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China Zhejiang Lab Hangzhou311100 China Kylin Software Co. Ltd Changsha410073 China

ISBN: (纸本)9781665494571

With importance of data value is approved, data-sharing will create more and greater value has become consensus. However, data exchange has to use a trusted third party(TTP) as an intermediary in an untrusted network environment, which may lead to leakage of data privacy. In this paper, we design a system named TeeSwap, which uses smart contract to complete data exchange, and at the same time uses cryptographic algorithms and trusted execution environment (TEE) to ensure privacy in process of data transmission, verification and contract execution. Meanwhile, TeeSwap can ensure the credibility, fairness, integrity and privacy while data are shared in our system. In the end, we implement contracts about data market and data exchange. When confirming the data transaction, data market contract can automatically generate and deploy exchange contract, which can complete the data exchange authoritatively. © 2021 IEEE.

关键词： Data privacy Smart contracts Data communication Cryptography

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Providing Responsiveness Requirement Based Consistency in DVE

Providing Responsiveness Requirement Based Consistency in DV...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Wei Zhang Hangjun Zhou Yuxing Peng Sikun Li National Laboratory of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

Consistency and responsiveness are two important factors in providing the sense of reality in distributed Virtual Environment (DVE). However, it is not easy to optimize both aspects because of the trade-off between these two factors. As a result, most existing consistency maintenance methods ignored the responsiveness requirements, or just assumed a simple responsiveness requirement model which cannot meet the real need of DVE systems. In this paper, we first present a new responsiveness requirement model. The model can describe requirement satisfaction situation of each node. Base on this model, we propose a responsiveness requirement based consistency method. The method can adjust the utilization of time resource according to the requirements of different nodes and improve the overall responsiveness performance by at least 20%. Therefore, it provides a good support to increase the applicability of DVE systems.

关键词： Delay effects Virtual environment distributed processing computer science Games Distance learning Electronic commerce Safety Research and development Real time systems

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parallel computing for large-scale author name disambiguation in medline 21

Parallel computing for large-scale author name disambiguatio...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Tang, Anyao Wu, Chengkun Liu, Jie Wang, Wei Yang, Xi Xing, Yuting Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781728120584

Author name disambiguation (AND) is an important task in the field of scientific data mining. It has become a great challenge with the rapid growth of academic digital libraries. The task of AND for a large number of authors is computationally intensive. In particular, an author's name in MEDLINE is represented by full last name and initials, like 'Zhang S', which leads to a lot of identical strings that actually represent different names. In this paper, we proposed an efficient algorithm for parallel AND computation. The proposed algorithm mainly addresses the load balancing issue across many computing nodes. It involves the following strategies:(1) Author-based load balancing, which splits the computation load for each core by author name labels. (2) Matrix-based strategy, which calculates the pairwise similarity between publications and saves them in a matrix globally shared by all processes. Then group them by width-first search. We combine the above two strategies, the second of which is used to calculate authors with a large number of documents, and the other authors apply the first. We constructed a publications database written by Chinese authors from MEDLINE, the biggest public database for biomedical literature (abstracts). For benchmark testing, we experimented our algorithm with a dataset of 1 million publications on the Tianhe-2A supercomputer. Firstly, we trained an AND classifier that can achieve 98.1% of F1. The serial computation time is estimated to be approximately 246 hours, while the parallel execution time is approximately 66 hours in the case of four cores on a single node (with a speedup of 3.7x). Finally, we reduced the total parallel computing time of 1 million documents to about 2 hours and achieved 65.8% of parallelism efficiency using 200 cores on 90 nodes. © 2019 IEEE.

关键词： Matrix algebra

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Challenge-aware U-net for breast lesion segmentation in ultrasound images

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Pattern Recognition 2025年 168卷

作者： Dengdi Sun Changxu Dong Yuchen Yan Bo Jiang Yayang Duan Zhengzheng Tu Chaoxue Zhang Key Laboratory of Intelligent Computing & Signal Processing (ICSP) Ministry of Education School of Artificial Intelligence Anhui University China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui Provincial Key Laboratory of Multimodal Cognitive Computing School of Computer Science and Technology Anhui University China Department of Ultrasound the First Affiliated Hospital of Anhui Medical University China

Deep learning methods can enhance the efficiency of tumor segmentation in breast ultrasound (BUS) images. However, noise interference, small tumors, and blurred boundaries can reduce segmentation accuracy. We design a three-branch challenge-aware U-net (CAU-net) to address these main challenges in BUS images. Our CAU-net extracts the features from three challenge-aware encoders in parallel first. Secondly, we propose an adaptive aggregation layer (AAL) to merge the multi-scale features of three challenging branches, enabling the network to adaptively handle different breast lesion samples with these main challenges. To further enhance the accuracy of segmentation, we introduce the graph reasoning module (GRM) to the network to model the correlation between the channels of the features and acquire the global information in the features. The result of our experiment on two datasets demonstrates the superiority of CAU-net over the advanced medical image segmentation methods. Our code can be downloaded from https://***/tzz-ahu .

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