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4th International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region, HPC-Asia 2000

作者： Dang, Gang Wang, Xiaoqing Zhang, Wensong Jin, Shiyao National Laboratory of Parallel and Distributed Processing National University of Defense Technology China

ISBN: (纸本)0769505902

Nowadays, many simulation environments not only can not reuse existing simulation models and tools, but also depend on operating systems and hardware platforms, and even more they lack the capability to execute over the Internet and the Web. The paper addresses these disadvantages by putting forward a prototype of a distributed simulation environment based on the Web (DSEW). It introduces both the technology of software components and the technology of integration with Java and CORBA, which enables portability, scalability, reusability, interoperability and visualization of the simulation application, and can also shorten the development period and communicate efficiently. © 2000 IEEE.

关键词： Common object request broker architecture (CORBA)

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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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Uml based statistical testing acceleration of distributed safety-critical software

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2nd International Symposium on parallel and distributed processing and Applications, ISPA 2004

作者： Yan, Jiong Wang, Ji Chen, Huo-Wang National Laboratory for Parallel and Distributed Processing Changsha China

ISBN: (纸本)9783540241287

It is necessary to assess the reliability of distributed safety-critical systems to a high degree of confidence before they are deployed in the field. However, distributed safety-critical software systems often include some rarely executed critical functions that are often inadequately tested in statistical testing based reliability estimation. This paper presents a method that can accelerate statistical testing of distributed safety-critical software. The method starts with the derivation of scenario usage diagram model (SUD) from UML diagrams annotated with usage related attributes and reliability attributes. Then the statistical testing accelerating method based on importance sampling is presented. When both the critical scenarios and the entire software are adequately tested, the method can still compute the unbiased software reliability from the test results with much less test cases. Thus, the statistical testing cost of distributed safety-critical software can be reduced effectively. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Software reliability

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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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Unsupervised feature learning for human activity recognition using smartphone sensors 2

Unsupervised feature learning for human activity recognition...

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2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014

作者： Li, Yongmou Shi, Dianxi Ding, B. Liu, Dongbo National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783319138169

Feature representation has a significant impact on human activity recognition. While the common used hand-crafted features rely heavily on the specific domain knowledge and may suffer from nonadaptability to the particular dataset. To alleviate the problems of handcrafted features, we present a feature extraction framework which exploits different unsupervised feature learning techniques to learning useful feature representation from accelerometer and gyroscope sensor data for human activity recognition. The unsupervised learning techniques we investigate include sparse auto-encoder, denoising auto-encoder and *** evaluate the performance on a public human activity recognition dataset and also compare our method with traditional features and another way of unsupervised feature learning. The results show that the learned features of our framework outperform the other two methods. The sparse auto-encoder achieves better results than other two techniques within our framework. © Springer International Publishing Switzerland 2014.

关键词： Signal encoding

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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

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7th IEEE International Conference on Smart Cloud, SmartCloud 2022

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

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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CANSE: A churn adaptive approach to network size estimation

CANSE: A churn adaptive approach to network size estimation

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16th IEEE International Conference on parallel and distributed Systems, ICPADS 2010

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

ISBN: (纸本)9780769543079

Network size is one of the fundamental information of distributed applications. The approach to estimate network size must feature both high accuracy and robustness in order to adapt to the dynamic environment in different topologies. However, existing approaches fail to guarantee accuracy and robustness simultaneously in dynamic topologies due to the randomness of nodes sampled. In this paper, we propose a churn adaptive approach to network size estimation - CANSE, which collects closest nodes in identification to each node's identification by sampling nodes periodically. Each node collects closest identifications by two schemes. One scheme is sampling random nodes from random walks along the topology. The other one is exchanging the closest identifications with other nodes. Finally, each node calculates the average spacing of the closest identifications collected to estimate network size. Compared with existing approaches, extensive experiments show that CANSE provides accurate estimation values quickly in various dynamic topologies. © 2010 IEEE.

关键词： Topology

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Recent advances on trusted computing in China

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Chinese Science Bulletin 2012年第35期57卷 4529-4532页

作者： DONG Wei CHEN LiQian School of Computer National University of Defense TechnologyChangsha 410073China National Laboratory for Parallel and Distributed Processing Changsha 410073China

This article highlights some recent research advances on trusted computing in China,focusing mainly on the methodologies and technologies related to trusted computing module,trusted computing platform,trusted network ... 详细信息

关键词：可信计算平台中国计算模块网络连接软件

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