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15th International Conference on parallel and distributed Systems, ICPADS '09

作者： Fu, Yongquan Wang, Yijie National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology China

ISBN: (纸本)9780769539003

Predicting network latencies between Internet hosts can efficiently support large-scale Internet applications, e.g., file sharing service and the overlay construction. Several study use the Hyperbolic space to model the Internet densecore and many-tendril structure. However, existing Hyperbolic space based embedding approaches are not designed for accurate latency estimation in the distributed context. We present HyperSpring, which estimates latency by modelling a mass spring system in the Hyperbolic similar with Vivaldi. HyperSpring adopts coordinate initialization to speed up the convergence of coordinate computation, uses multiple-round symmetric updates to escape from bad local minima, and stabilizes coordinates by compensating RTT measurements to reduce the coordinate drifts. Evaluation results based on a network trace of 226 PlanetLab nodes indicate that, compared to Euclidean-space based Vivaldi, HyperSpring provides performance improvements for most nodes, and incurs slightly higher distortions for a small number of nodes. © 2009 IEEE.

关键词： Hyperbolic space Latency estimation Mass spring field

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iRank: Supporting proximity ranking for peer-to-peer applications

iRank: Supporting proximity ranking for peer-to-peer applica...

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15th International Conference on parallel and distributed Systems, ICPADS '09

作者： Fu, Yongquan Wang, Yijie National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology China

ISBN: (纸本)9780769539003

Proximity ranking according to end-to-end network distances (e.g., Round-Trip Time, RTT) can reveal detailed proximity information, which is important in network management and performance diagnosis in distributed systems. However, to the best of our knowledge, there has been no similar work on this subject in the P2P computing field. We present a distributed rating method iRank, that enables proximity rankings by providing discrete ratings in a distributed manner. It formulates the proximity ranking as a rating problem that faithfully captures the proximity based on noisy distance measurements scalably and practically. The primary challenge in inferring proximity rankings is enforcing distributed ratings with complex rating policies. Our solution is based on reconstructing ratings by decomposing a centralized rating method Maximum Margin Matrix Factorization (MMMF) into independent sub-problems, that can be efficiently solved in a decentralized manner. By relaxing the dependence on infrastructure nodes that are a single point of failure and limit scalability, iRank can gracefully handle network churns. Through real network latency data sets, we demonstrate that iRank can predict ratings with low distortion, which are smaller than 20 percentage worse than the centralized method, in the context of synthetic complex rating policies. © 2009 IEEE.

关键词： Complex networks

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Improving performance portability for GPU-specific Open CL kernels on multi-core/many-core CPUs by analysis-based transformations

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Frontiers of Information Technology & Electronic Engineering 2015年第11期16卷 899-916页

作者： Mei WEN Da-fei HUANG Chang-qing XUN Dong CHEN School of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing

OpenCL is an open heterogeneous programming framework. Although OpenCL programs are func- tionally portable, they do not provide performance portability, so code transformation often plays an irreplaceable role. When adapting GPU-specific OpenCL kernels to run on multi-core/many-core CPUs, coarsening the thread granularity is necessary and thus has been extensively used. However, locality concerns exposed in GPU-specific OpenCL code are usually inherited without analysis, which may give side-effects on the CPU performance. Typi- cally, the use of OpenCL＇s local memory on multi-core/many-core CPUs may lead to an opposite performance effect, because local-memory arrays no longer match well with the hardware and the associated synchronizations are costly. To solve this dilemma, we actively analyze the memory access patterns using array-access descriptors derived from GPU-specific kernels, which can thus be adapted for CPUs by （1） removing all the unwanted local-memory arrays together with the obsolete barrier statements and （2） optimizing the coalesced kernel code with vectorization and locality re-exploitation. Moreover, we have developed an automated tool chain that makes this transformation of GPU-specific OpenCL kernels into a CPU-friendly form, which is accompanied with a scheduler that forms a new OpenCL runtime. Experiments show that the automated transformation can improve OpenCL kernel performance on a multi-core CPU by an average factor of 3.24. Satisfactory performance improvements axe also achieved on Intel＇s many-integrated-core coprocessor. The resultant performance on both architectures is better than or comparable with the corresponding OpenMP performance.

关键词： OpenCL Performance portability Multi-core/many-core CPU Analysis-based transformation

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Property-oriented testing of real-time systems

Property-oriented testing of real-time systems

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Proceedings - 11th Asia-Pacific Software Engineering Conference, APSEC 2004

作者： Li, Shuhao Wang, Ji Dong, Wei Qi, Zhi-Chang National Laboratory for Parallel and Distributed Processing Changsha China State Key Laboratory for Software Engineering Wuhan University China

ISBN: (纸本)0769522459

Although Statecharts has gained widespread use as a formalism for modeling reactive real-time systems, testing these systems still confronts some difficulties, of which a major one is the existence of numerous and complex system behaviors. It is extremely difficult to conduct comprehensive and in-depth testing of such real-time systems. This paper presents an approach to property-oriented real-time testing. Necessary real-time extensions are proposed such that the time-enriched Statecharts can describe non-trivial timing constraints. The properties to be tested are characterized by a restricted real-time logic. Then the targeted test sequences are derived from the real-time models according to the user-specified properties. Using this approach, testing efforts can be focused on particular properties of the real-time systems and usually only a small portion of the total behaviors needs to be tested. © 2004 IEEE.

关键词： Real time systems

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Reduced complexity tree-structured conditional random field for all-words word sense disambiguation

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Journal of Computational Information Systems 2012年第3期8卷 1177-1186页

作者： Zhou, Yun Wang, Ting Wang, Zhiyuan Xie, Songxian Computer School National University of Defense Technology Changsha 410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

In this paper, we apply tree-structured conditional random field (TCRF) to all-words word sense disambiguation (WSD), where the graphical structure of TCRF is the dependency syntax tree produced by Minipar. The extremely large label space and high time complexity of TCRF hinders its application in semantic parsing, especially in all-words WSD, and we focus on reducing the time complexity of TCRF in this paper. The running time of TCRF is reduced significantly by edge feature selection and parallelization in training phase and beam search strategy in decoding phase. We evaluate the TCRF method on the English all-words task of Senseval-3, and the F1 value of TCRF method is 0.668, which outperforms the best result 0.652 in Senseval-3. For convenience of comparison, our training set only includes a part of Semcor, which reserves the space for improvement. Furthermore, the practical problem in the fields of information retrieval and natural language processing, often has a large label space and tree-structured information, which can also be solved by the TCRF method proposed in this paper. 1553-9105/Copyright © 2012 Binary Information Press.

关键词： Semantics

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Anadem: A hybrid overlay network for content-based data distribution

Anadem: A hybrid overlay network for content-based data dist...

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15th International Conference on parallel and distributed Systems, ICPADS '09

作者： Zheng, Zhong Wang, Yi-Jie National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9780769539003

As an infrastructure for data distribution, overlay networks have to feature efficient routing and adequate robustness to achieve fast and accurate data distribution in the environment with node churn. Considering that the existing overlay networks mostly focus on single optimization objective and fail to ensure routing efficiency and robustness simultaneously, a hybrid overlay network for content-based data distribution - Anadem is proposed in this paper. Anadem achieves a better compromise between routing efficiency and robustness by combining the intercluster multiple structured topologies with the intra-cluster unstructured topologies. Anadem also provides mechanisms for dynamic concurrent cluster creation, cluster departure and load balance to make data distribution more adaptive to the dynamic network environment. Experimental results reveal that compared with existing overlay networks, Anadem can support fast and accurate content-based data distribution even when large amount of nodes fail in the system. © 2009 IEEE.

关键词： Overlay networks

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Learning from internet: Handling uncertainty in robotic environment modeling 17

Learning from internet: Handling uncertainty in robotic envi...

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Li, Yiying Wang, Huaimin Ding, Bo Che, Huimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

ISBN: (纸本)1595930361

Uncertainty is a great challenge for environment perception of autonomous robots. For instance, while building semantic maps (i.e., maps with semantic labels such as object names), the robot may encounter unexpected objects of which it has no knowledge. It will lead to inevitable failures in traditional environment modeling software. The abundant knowledge being accumulated on the Internet has the potential to assist robots to handle such kind of uncertainty. However, existing researches have not touched this issue yet. This paper proposes a cloud-based semantic mapping engine named SemaCloud, which can not only augment robot's environment modeling capability by the rich cloud resources but also cope with uncertainty by utilizing the Internet knowledge on necessary. It adopts a state-of-art Deep Neural Network (DNN) for real-time and accurate recognition of pre-trained objects. If an object is beyond the knowledge of this DNN, a special mechanism named QoS-aware cloud phase transition is triggered to seek help from existing recognition services on the Internet. By a set of carefullydesigned algorithms, it can maximize benefits and minimize the negative impacts on the Quality of Service (QoS) properties of robotic applications, which is essential to many robot scenarios. The experiments on both open datasets and real robots show that our work can handle uncertainty successfully in robotic semantic mapping without sacrificing critical real-time constraints. © 2017 Association for Computing Machinery.

关键词： Deep neural networks

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CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms

CD-Sched: An Automated Scheduling Framework for Accelerating...

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2023 International Conference on Power, Communication, Computing and Networking Technologies, PCCNT 2023

作者： Xiao, Yuanyuan Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology Changsha China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%. © 2023 ACM.

关键词： Deep neural networks

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Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Geng, Mingyang Li, Yiying Ding, Bo Wang, And Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

ISBN: (纸本)9781509060146

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learningbased approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot visionbased trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the singlerobot system. © 2018 IEEE.

关键词： Multipurpose robots

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SemanticCast: Content-based data distribution over self-organizing semantic overlay networks

SemanticCast: Content-based data distribution over self-orga...

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11th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2010

作者： Zheng, Zhong Wang, Yijie National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9780769542874

Many applications demand distributing data with different contents efficiently in the network environment with unreliable links and a high node churn. Existing approaches mostly focus on optimizing either efficiency or robustness of data distribution, and fail to ensure both of them simultaneously. In this paper, we propose SemanticCast - a content-based data distribution approach over self-organizing semantic overlay networks. SemanticCast maintains a self-organizing semantic overlay based on view exchange (called Crowd). In Crowd, each node seeks neighbors with more similar interests by periodically exchanging its neighbor list (called view) with a chosen neighbor. Through these nodes' self-organizing behavior, various interest communities emerge in the overlay. For data distribution over Crowd, SemanticCast adopts random walk to route data between interest communities, and adopts flooding to disseminate data inside the interested communities. The experimental results show that compared to existing approaches, SemanticCast can support efficient content-based data distribution in the unreliable and highly dynamic network environment. © 2010 IEEE.

关键词： Semantics

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