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A review on the churn character of P2P networks

Jisuanji Xuebao/Chinese Journal of Computers 2011年第9期34卷 1563-1577页

作者： Fu, Zhi-Peng Wang, Huai-Min Shi, Dian-Xi Zou, Peng School of Computer Science National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

P2P application is one of the most popular applications in the Internet, but its performance is badly influenced by the churn character-the nodes continuous arrival and departure. On the basis of introducing the origin of the churn, the definition of it and its influences to the P2P system performance, we minutely describe the research on the statistical properties, and discover some dynamic law including nodes' session time generally obey the heavy-tailed distribution. Then we detail the measurement methods. For passive monitoring, active monitoring, sample and so on, we present the advantages and disadvantages of each method, and explain the corresponding improving method to enhance the accuracy of the measurement. After that we elaborate the resilience strategies to the churn. Towards the neighbor selection, failure recovery, replica maintenance, link lifetime maintenance and so on, we present also the advantage and disadvantage of each strategy, and present our view to each of aspects. Finally, the summary and prospect of the future research are given.

关键词： Peer to peer networks

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ECDT: Exploiting Correlation Diversity for Knowledge Transfer in Partial Domain Adaptation

ECDT: Exploiting Correlation Diversity for Knowledge Transfe...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shichang He Xuan Liu Xinning Chen Ying Huang College of Computer Science and Electronic Engineering Hunan University P.R. China The Science and Technology on Paraller and Distributed Processing Laboratory Tsinghua University P.R. China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Domain adaptation aims to transfer knowledge across different domains and bridge the gap between them. While traditional knowledge transfer considers identical domain, a more realistic scenario is to transfer from a larger and more diverse source domain to a smaller target domain, which is referred to as partial domain adaptation (PDA). However, matching the whole source domain to the target domain for PDA might produce negative transfer. Samples in the shared classes should be carefully selected to mitigate negative transfer in PDA. We observe that the correlations between different target domain samples and source domain samples are diverse: classes are not equally correlated and moreover, different samples have different correlation strengthes even when they are in the same class. In this study, we propose ECDT, a novel PDA method that Exploits the Correlation Diversity for knowledge Transfer between different domains. We propose a novel method to estimate target domain label space that utilizes the label distribution and feature distribution of target samples, based on which outlier source classes can be filtered out and their negative effects on transfer can be mitigated. Moreover, ECDT combines class-level correlation and instance-level correlation to quantity sample-level transferability in domain adversarial network. Experimental results on three commonly used cross-domain object data sets show that ECDT is superior to previous partial domain adaptation methods.

关键词： Correlation Handheld computers Tagging Sensors Task analysis Knowledge transfer

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A Float-Point Arithmetic Logic Unit for High Performance RISC-V Processor

A Float-Point Arithmetic Logic Unit for High Performance RIS...

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Chunyuan Wei Chuan Yuan Yu Qi Bangjian Xu Xin Niu College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical and Information Engineering Hunan University Changsha China Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

With the rapid advancement of artificial intelligence, chips have become increasingly important. The emerging RISC-V instruction set gradually provides powerful computing support for this field. In this context, along with the computing requirements of deep learning, this paper presents the design of a high-performance floating-point arithmetic logic unit (FALU) that facilitates calculations with double-precision, single-precision, half-precision, and Bfloat16 precision data. This design is based on a single-channel algorithm with merged rounding. It improves and implements a composite adder that combines high and low bits. It also proposes a tree-like floating-point comparator based on the Kogge-Stone parallel prefix network. To ensure that the FALU components meet performance requirements, we undergo functional verification in the Vivado simulation environment. Operating at 1.47GHz under the 28nm CMOS process, the components achieve the predetermined performance indicators.

关键词： Deep learning Context Accuracy Instruction sets CMOS process Logic Artificial intelligence Adders Floating-point arithmetic

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PyMW - A Python module for desktop grid and volunteer computing

PyMW - A Python module for desktop grid and volunteer comput...

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International Symposium on parallel and distributed processing (IPDPS)

作者： Eric M. Heien Yusuke Takata Kenichi Hagihara Adam Kornafeld Graduate School of Information Science and Technology Osaka University Toyonaka Osaka Japan Laboratory of Parallel and Distributed Systems Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

We describe a general purpose master-worker parallel computation Python module called PyMW. PyMW is intended to support rapid development, testing and deployment of large scale master-worker style computations on a desktop grid or volunteer computing environment. This module targets non-expert computer users by hiding complicated task submission and result retrieval procedures behind a simple interface. PyMW also provides a unified interface to multiple computing environments with easy extension to support additional environments. In this paper, we describe the internal structure and external interface to the PyMW module and its support for the Condor computing environment and the Berkeley Open Infrastructure for Network Computing (BOINC) platform. We demonstrate the effectiveness and scalability of PyMW by performing master-worker style computations on a desktop grid using Condor and a BOINC volunteer computing project.

关键词： Grid computing Concurrent computing Computer interfaces Large-scale systems Virtual colonoscopy distributed computing Computer networks Software packages Packaging Information science

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A multi-layered emulation platform to verify the effectiveness of consistency control approaches about message ordering

A multi-layered emulation platform to verify the effectivene...

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International Conference on Communication Software and Networks, ICCSN

作者： Hangjun Zhou Sha Fu Key Laboratory of Science and Technology for National Defence of Parallel and Distributed P rocessing School of Computer Science National University of Defense Technology Changsha Hunan China Department of Information Management Hunan University of Finance and Economics Changsha Hunan China

It is an important issue to preserve the consistent delivering order of messages at each site in distributed Virtual Environments(DVEs). Currently, the violations of message delivery order are inclined to happen in the large-scale and frequently interactive DVEs running on Wide Area Network(WAN). The inconsistent anomalies are characterized as unpredictable, unrepeatable and uncontrollable, which results in the severe problem for the development of the consistency control approaches. Meanwhile, it is also highly expensive to directly verify the proposed approaches on WAN. Therefore, in this paper, we design and establish an original multi-layered emulation platform in LAN, which can emulate the network latency of WAN and repeat the scenarios of violations. On the platform, the designers of consistency approaches don't have to spend efforts to realize the fundamental functions of the verification, but could pay more attention to the development and optimization of the control algorithms, which is very beneficial for the improvement of message ordering approaches.

关键词： Switches Wide area networks

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HPDedup: A hybrid prioritized data deduplication mechanism for primary storage in the cloud

arXiv

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arXiv 2017年

作者： Wu, Huijun Wang, Chen Fu, Yinjin Sakr, Sherif Zhu, Liming Lu, Kai Data61 CSIRO University of New South Wales Australia PLA University of Science and Technology China Science and Technology on Parallel and Distributed Laboratory State Key Laboratory of High Performance Computing State Key Laboratory of High-end Server & Storage Technology College of Computer National University of Defense Technology Changsha China

—Eliminating duplicate data in primary storage of clouds increases the cost-efficiency of cloud service providers as well as reduces the cost of users for using cloud services. Most existing primary deduplication techniques either use inline caching to exploit locality in primary workloads or use post-processing deduplication running in system idle time to avoid the negative impact on I/O performance. However, neither of them works well in the cloud servers running multiple services or applications for the following two reasons: Firstly, the temporal locality of duplicate data writes may not exist in some primary storage workloads thus inline caching often fails to achieve good deduplication ratio. Secondly, the post-processing deduplication allows duplicate data to be written into disks, therefore does not provide the benefit of I/O deduplication and requires high peak storage capacity. This paper presents HPDedup, a Hybrid Prioritized data Deduplication mechanism to deal with the storage system shared by applications running in co-located virtual machines or containers by fusing an inline and a post-processing process for exact deduplication. In the inline deduplication phase, HPDedup gives a fingerprint caching mechanism that estimates the temporal locality of duplicates in data streams from different VMs or applications and prioritizes the cache allocation for these streams based on the estimation. HPDedup also allows different deduplication threshold for streams based on their spatial locality to reduce the disk fragmentation. The post-processing phase removes duplicates whose fingerprints are not able to be cached due to weak temporal locality from disks. The hybrid deduplication mechanism significantly reduces the amount of redundant data written to the storage system while maintaining inline data writing performance. Our experimental results show that HPDedup clearly outperforms the state-of-the-art primary storage deduplication techniques in terms of inline

关键词： Efficiency

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Correntropy Supervised Non-negative Matrix Factorization

Correntropy Supervised Non-negative Matrix Factorization

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International Joint Conference on Neural Networks

作者： Wenju Zhang Naiyang Guan Dacheng Tao Bin Mao Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan China 410073 Center for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia College of Science National University of Defense Technology Changsha Hunan China 410073 Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan China 410073

ISBN: (纸本)9781479919611

Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF (CSNMF) to simultaneously overcome aforementioned deficiencies. In particular, CSNMF maximizes the correntropy between the data matrix and its reconstruction in low-dimensional space to inhibit outliers during learning the subspace, and narrows the minimizes the distances between coefficients of any two samples with the same class labels to enhance the subsequent classification performance. To solve CSNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular face image datasets verify the effectiveness of CSNMF comparing with NMF, its supervised variants, and its robustified variants.

关键词： NMF subsequent classification NMF protocol matrix decomposition outliers Nonnegative LOSS FUNCTION images REDUCTION METHODS Dataset computer vision

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General audio tagging with ensembling convolutional neural networks and statistical features

arXiv

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arXiv 2018年

作者： Xu, Kele Zhu, Boqing Kong, Qiuqiang Mi, Haibo Ding, Bo Wang, Dezhi Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China University of Surrey United Kingdom College of Meteorology and Oceanography National Univ. of Defense Tech Changsha China

Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convolutional neural network architectures to solve the audio tagging task. Statistical features are applied to capture statistical patterns of audio features to improve the classification performance. Ensemble learning is applied to ensemble the outputs from the deep classifiers to utilize complementary information. a sample reweight strategy is employed for ensemble training to address the noisy label problem. Our system achieves a mean average precision (mAP@3) of 0.958, outperforming the baseline system of 0.704. Our system ranked the 1st and 4th out of 558 submissions in the public and private leaderboard of DCASE 2018 Task 2 challenge. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Collimated electron and proton BEAM from ultraintense laser interaction with a rear hole target

Collimated electron and proton BEAM from ultraintense laser ...

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1st International Particle Accelerator Conference, IPAC 2010

作者： Yang, X.H. Ma, Y.Y. Shao, F.Q. Yu, T.P. Yin, Y. Tian, C.L. Kawata, S. Yu, M.Y. Gu, Y.Q. Xu, H. Department of Physics National University of Defense Technology Changsha 410073 China Center for Optical Research and Education Graduate School of Engineering Utsunomiya University 7-1-2 Yohtoh Utsunomiya 321-8585 Japan Department of Physics Institute for Fusion Theory and Simulation Zhejiang University Hangzhou 310027 China Laser Fusion Research Center China Academy of Engineering Physics Mianyang 621000 China National Laboratory Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9789290833529

Collimated electron and proton beams from the interaction of an ultra-intense laser pulse with a rear hole target are studied by particle-in-cell (PIC) simulation. When an ultra-intense laser pulse irradiates on such a target, the hot electrons from the inner surfaces of the hole expand fast into the hole. However, the electrons at the corners expand slower and are compressed strongly. Then two intense electron jets as well as proton jets are sprayed out from the corners. The jets extend into the hole and focus along the laser propagation axis. The effects of the corner angle on the collimated proton beam are also investigated.

关键词： Hot electrons

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引用

International Conference on parallel and distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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