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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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Detecting duplicate pull-requests in GitHub 17

Detecting duplicate pull-requests in GitHub

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Li, Zhixing Yin, Gang Yu, Yue Wang, Tao Wang, Huaimin National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)1595930361

The widespread use of pull-requests boosts the development and evolution for many open source software projects. However, due to the parallel and uncoordinated nature of development process in GitHub, duplicate pull-requests may be submitted by different contributors to solve the same problem. Duplicate pull-requests increase the maintenance cost of GitHub, result in the waste of time spent on the redundant effort of code review, and even frustrate developers' willing to offer continuous contribution. In this paper, we investigate using text information to automatically detect duplicate pull-requests in GitHub. For a new-arriving pull-request, we compare the textual similarity between it and other existing pull-requests, and then return a candidate list of the most similar *** evaluate our approach on three popular projects hosted in GitHub, namely Rails, Elasticsearch and ***. The evaluation shows that about 55.3% - 71.0% of the duplicates can be found when we use the combination of title similarity and description similarity. © 2017 Association for Computing Machinery.

关键词： Open source software

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ROGC: Role-Oriented Graph Convolution Based Multi-Agent Reinforcement Learning

ROGC: Role-Oriented Graph Convolution Based Multi-Agent Rein...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Liu, Yuntao Li, Yuan Xu, Xinhai Liu, Donghong Dou, Yong National University of Defense Technology National Laboratory for Parallel and Distributed Processing China Academy of Military Sciences China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The role-oriented learning approach could improve the performance of multi-agent reinforcement learning by decomposing complex multi-agent tasks into different roles. However, due to the dynamic environment and interactions among agents, the role undertaken by an agent changes rapidly with time going on. Therefore, the roles of agents should be adapted to the varying situation during the learning process. In this paper, we propose a role-oriented graph convolution based multi-agent reinforcement learning framework (ROGC). Firstly, we design a role assigner based on samples generated from the environment to learn roles for classifying agents into different groups. To further enhance cooperation among agents in the same group for higher performance, we design a graph convolutional module to achieve intra-role communications based on discovered roles. With roles and extracted role features, we design a role-oriented policy learning module that embeds the role information into the algorithm and generates effective policies for individuals. Further, we introduce an auto-encoder to learn the intra-role cooperation knowledge in the graph convolutional module, which ensures our framework executes in a decentralized way. Extensive experiments show that our framework can learn dynamic roles and make full use of learned roles, which makes it outperform popular MARL methods. © 2022 IEEE.

关键词： Reinforcement learning

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A programming language approach to internet-based virtual environment computing

A programming language approach to internet-based virtual en...

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作者： Ji, Wang Shen, Rui Wang, Huai-Min National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China

There is an increasing need to build scalable distributed systems over the Internet infrastructure. However, the development of distributed scalable applications suffers from lack of a wide accepted virtual computing environment. Users have to take great efforts on the management and sharing of the involved resources over Internet, whose characteristics are intrinsic growth, autonomy and diversity. To deal with this challenge, Internet-based Virtual Computing Environment (iVCE) is proposed and developed to serve as a platform for distributed scalable applications over the open infrastructure, whose kernel mechanisms are on-demand aggregation and autonomic collaboration of resources. In this paper, we present a programming language for iVCE named Owlet. Owlet conforms with the conceptual model of iVCE, and exposes the iVCE to application developers. As an interaction language based on peer-to-peer content-based publish/subscribe scheme, Owlet abstracts the Internet as an environment for the roles to interact, and uses roles to build a relatively stable view of resources for the on-demand resource aggregation. It provides language constructs to use 1) distributed event driven rules to describe interaction protocols among different roles, 2) conversations to correlate events and rules into a common context, and 3) resource pooling to do fault tolerance and load balancing among networked nodes. We have implemented an Owlet compiler and its runtime environment according to the architecture of iVCE, and built several Owlet applications, including a peer-to-peer file sharing application. Experimental results show that, with iVCE, the separation of resource aggregation logic and business logic significantly eases the process of building scalable distributed applications. © 2011 Springer science+Business Media, LLC & science Press, China.

关键词： Fault tolerance

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Optimization and parallelization Single Particle Cryo-EM Software RELION with GPU

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2018年第2期55卷 409-417页

作者： Su, Huayou Wen, Wen Li, Dongsheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

Single particle cryo-electron microscopy (cryo-EM) is one of the most important methods of macromolecular structure. RELION (regularized likelihood optimization) is an open-source computer program for the refinement of macromolecular structures by single-particle analysis of cryo-EM data. Due to its easy usage and high quality results, RELION has attracted a lot of attentions from researchers. However, the computation requirement of this program is too huge to solve some large molecular structures with CPU, which harpers the popularization of RELION. In this paper, we characterize the algorithm of RELION and parallelize it with GPU. Firstly, the mathematical theory, computer patterns and performance bottlenecks of RELION are analyzed comprehensively. Then, we optimize the program targeting at fine-grained many-core architecture processor, such as GPU. We propose an efficient multi-level parallel model to utilize the powerful computation capacity of many-core processor. In order to achieve high performance, we reconstruct the data structure for GPU continues memory access. To avoid the limitation of GPU memory size, we implement an adaptive framework. The experimental results show that the proposed GPU based algorithm can achieve good performance. When compared with the CPU implementation, the speedup ratio of the application is more than 36 times, while the speedup ratio of compute-intensive algorithm is about 75X. Moreover, the testing results on multi GPUs show that the GPU based implementation has good scalability. © 2018, science Press. All right reserved.

关键词： Graphics processing unit

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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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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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A task-mapping algorithm for performance simulation based on simulated annealing

A task-mapping algorithm for performance simulation based on...

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2011 International Conference on Computational and Information sciences, ICCIS 2011

作者： Guo, Xiaowei Lin, Yufei Xu, Xinhai Zhang, Xin National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9780769545011

Nowadays, the scale of parallel computer systems is increasing, and simulation technology has become an important tool for performance prediction in the system development process. Task mapping approach is an important aspect affecting the performance of simulation. In this paper, in order to solve the task mapping problem in performance simulation, a task mapping algorithm based on simulated annealing is proposed, and we verified the correctness and effectiveness of the algorithm by experiments. Experimental results show that the algorithm has high efficiency, and can solve the large-scale problem with lower time cost. © 2011 IEEE.

关键词： Simulated annealing

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SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Distribution Structure 25

SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Di...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xu, Jinwei Jiang, Jingfei Xu, Shiyao Gao, Lei National University of Defense Technology Science and Technology on Parallel and Distributed Laboratory Hunan Changsha China

ISBN: (纸本)9798350330014

Recurrent neural networks (RNNs) have become common models in the field of artificial intelligence to process temporal sequence task, such as speech recognition, text analysis, natural language processing, etc. To speedup RNNs inference, previous research proposed model sparse pruning techniques. However, the pruning rate of existing sparse pruning algorithms will be affected by pruning granularity and hardware friendliness. In order to approximate nonstructured pruning algorithm, this paper proposes Large Region Balanced Sparse (LRBS) pruning method, which does not limit sub-matrix shape and effectively improves pruning rate. Furthermore, we propose Sparse Matrix Vector Multiplication Accelerator for RNNs (SMVAR), which adopt non-blocking data distribution structure to solve the problem of efficient execution of large region irreg-ular matrix multiplication. To further improve the accelerator performance, SMVAR fine-grained adjusts the pipeline between macro-operations to reduce the idle of compute components. In addition, according to the coarse-grained block characteristics of LRBS algorithm, we develop the coarse-grained parallelism of accelerator with multiply compute units(CUs) structure. Experiments show that the pruning rate of our proposed LRBS is 1.25x-2.5x higher than that of the existing pruning algorithms. Compared with the existing work, the execution efficiency is improved by more than 2.02x-35.9x in the same application scenario. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Communication delay analysis based on network calculus

Communication delay analysis based on network calculus

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2010 2nd International Conference on Software technology and Engineering, ICSTE 2010

作者： Lin, Yufei Xu, Xinhai Lin, Yisong National Laboratory for Parallel and Distributed Processing Computer School National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781424486656

Network calculus is a promising theory for analyzing and modeling networks based on min-plus algebra. Using network calculus theory, we propose formulas of arrival curve and service curve for end-to-end communication, build the corresponding time model, and derive the communication delay formulas for two scenarios of the model respectively. Then we take fat tree topology, which is widely used in Infiniband interconnection, as an example to analyze the delay of one-to-all broadcast. This paper, as a groundwork, provides a new approach for the network researchers to delve communication delay in future researches. © 2010 IEEE.

关键词： Calculations

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