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arXiv 2019年

作者： Wang, Deguang Shen, Junzhong Wen, Mei Zhang, Chunyuan College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory for Parallel Distributed ProcessingNational University of Defense Technology Changsha410073 China

Three-dimensional deconvolution is widely used in many computer vision applications. However, most previous works have only focused on accelerating 2D deconvolutional neural networks (DCNNs) on FPGAs, while the acceleration of 3D DCNNs has not been studied in depth as they have higher computational complexity and sparsity than 2D DCNNs. In this paper, we focus on the acceleration of both 2D and 3D DCNNs on FPGAs by proposing efficient schemes for mapping 2D and 3D DCNNs on a uniform architecture. By implementing our design on the Xilinx VC709 platform for four real-life 2D and 3D DCNNs, we can achieve up to 3.0 TOPS with high hardware efficiency. Comparisons with CPU and GPU solutions demonstrate that we can achieve an improvement of up to 63.3× in throughput relative to a CPU solution and an improvement of up to 8.3× in energy efficiency compared to a GPU solution. Copyright © 2019, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Social media in Git Hub: the role of @-mention in assisting software development

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Science China(Information Sciences) 2017年第3期60卷 50-67页

作者： Yang ZHANG Huaimin WANG Gang YIN Tao WANG Yue YU Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology

Recently, many researches propose that social media tools can promote the collaboration among developers, which are beneficial to the software development. Nevertheless, there is little empirical evidence to confirm that using @-mention has indeed a beneficial impact on the issues in Git Hub. In order to begin investigating such claim, we examine data from two large and successful projects hosted on Git Hub, the Ruby on Rails and the Angular JS. By using qualitative and quantitative analysis, we give an in-depth understanding on how @-mention is used in the issues and the role of @-mention in assisting software development. Our statistical results indicate that, @-mention attracts more participants and tends to be used in the difficult issues. @-mention favors the solving process of issues by enlarging the visibility of issues and facilitating the developers' collaboration. Our study also build an @-network based on the @-mention database we *** the @-network, we investigate its evolution over time and prove that we certainly have the potential to mine the relationships and characteristics of developers by exploiting the knowledge from the @-network.

关键词： issues social media @-mention Git Hub software development

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Density-Attentive Head Detector for Crowd Counting

Density-Attentive Head Detector for Crowd Counting

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International Conference on Data Science and Business Analytics (ICDSBA)

作者： Ziyu Zhao Xingyu Shen Long Lan Xiaoyao Yin Xiang Zhang Yonggang Che Zhigang Luo College of Computer National University of Defense Technology Changsha China Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728146447

ISBN: (纸本)9781728146454

In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which significantly restricts its applications. Detection-based method counts each head with their accurate locations but works only in middle or low-level density scenes. In this paper, a joint learning method, named Density-attentive Head Detector (DAHD) is developed to overcome their respective shortcomings via a multi-task training procedure. Specifically, to guarantee the sensitivity of the detector to small or partially occluded heads, we carefully equip the detector with a density map which learned from regression module. Moreover, a novel Dilated Feature Pyramid Network (DFPN) is introduced to our method to enlarge the receptive field of convolutional kernel, bringing confirmative additional benefits to identify small heads. Experiments on the popular ShanghaiTech and Mall datasets confirm the improved performance of DAHD compared with the current detection-based approaches, and a comparable performance to regression-based approaches in term of counting.

关键词： Detectors Feature extraction Object detection Faces Kernel Training Task analysis

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Surveying concurrency bug detectors based on types of detected bugs

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Science China(Information Sciences) 2017年第3期60卷 5-31页

作者： Zhendong WU Kai LU Xiaoping WANG Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology College of Computer National University of Defense Technology

Concurrency bugs widely exist in concurrent programs and have caused severe failures in the real world. Researchers have made significant progress in detecting concurrency bugs, which improves software reliability. In this paper, we survey the most up-to-date and well-known concurrency bug detectors. We categorize the existing detectors based on the types of concurrency bugs. Consequently, we analyze data race detectors, atomicity violation detectors, order violation detectors, and deadlock detectors, respectively. We also discuss some other techniques which are mostly related to concurrency bug detection, including schedule bounding techniques, interleaving optimizing techniques, path expanding techniques, and deterministic replay techniques. Additionally, we statistically analyze the reviewed detectors and get some interesting findings, for instance, nearly 86% of previous detectors focus on data races and atomicity violations, and dynamic approaches are popular(74%). We also discuss the limitations of previous detectors, finding that 91% of previous detectors suffer from false negatives and 64% of previous detectors suffer from runtime overhead. Based on the reviewed detectors and statistical analysis, we conclude some future research directions, including accuracy, performance,applicability, and integrality.

关键词： concurrency bug detection data race atomicity violation order violation deadlock

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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

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International Conference on Computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

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Building a Cloud-Ready Program:A highly scalable Implementation based on Kubernetes

Building a Cloud-Ready Program:A highly scalable Implementat...

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作者： Qiankun Li Gang Yin Tao Wang Yue Yu National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Build system,which can convert source codes into applications,is essential for the development of *** general build systems that relying on single physical or cloud host to run bring problems such as system security,resource shortage,overload,and low availability in the face of massive build *** modularizing and streamlining the steps during a build process,this paper proposes a system that introduces container technology and then builds a large-scale,real-time,and huge-concurrency supported build system based on Kubernetes[1].The system provides a highly scalable and feature-stable cloud architecture that supports huge concurrency with lower resource ***,the system controls programs' behaviors very well to avoid potential security and resource issues and shows excellent performance in concurrency,scalability,security,and load balance even when handling a large number of build tasks.

关键词： Build system Container Kubernetes Concurrent build

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Providing Virtual Cloud for Special Purposes on Demand in JointCloud Computing Environment

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Journal of Computer Science & Technology 2017年第2期32卷 211-218页

作者： Dong-Gang Cao Member, CCF, IEEE, Bo An Pei-Chang Shi Huai-Min Wang Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing 100871 China National Key Laboratory for Parallel and Distributed Processing National University of Defense and Technology Changsha 410073 China

Cloud computing has been widely adopted by enterprises because of its on-demand and elastic resource usage paradigm. Currently most cloud applications are running on one single cloud. However, more and more applications demand to run across several clouds to satisfy the requirements like best cost efficiency, avoidance of vender lock-in, and geolocation sensitive service. JointCloud computing is a new research initiated by Chinese institutes to address the computing issues concerned with multiple clouds. In JointCloud, users＇ diverse and dynamic requirements on cloud resources axe satisfied by providing users virtual cloud （VC） for special purposes. A virtual cloud for special purposes is in essence a user＇s specific cloud working environment having the customized software stacks, configurations and computing resources readily available. This paper first introduces what is JointCloud computing and then describes the design rationales, motivation examples, mechanisms and enabling technologies of VC in JointCloud.

关键词： cloud computing JointCloud virtual cloud （VC） cloud working environment

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An efficient SIMD compression format for sparse matrix-vector multiplication

An efficient SIMD compression format for sparse matrix-vecto...

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作者： Chen, Xinhai Xie, Peizhen Chi, Lihua Liu, Jie Gong, Chunye Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Institute of Advanced Science and Technology Hunan Institute of Traffic Engineering Hengyang China

Sparse matrix-vector multiplication (SpMV) is an essential kernel in sparse linear algebra and has been studied extensively on all modern processor and accelerator architectures. Compressed Sparse Row (CSR) is a frequently used format for sparse matrices storage. However, CSR-based SpMV has poor performance on processors with vector units. In order to take full advantage of SIMD acceleration technology in SpMV, we proposed a new matrix storage format called CSR-SIMD. The new storage format compresses the non-zero elements into many variable-length data fragments with consecutive memory access addresses. Thus, the data locality of sparse matrix A and dense vector x expands and the floating-point operations for each fragment can be completely calculated by vectorized implementation on wide SIMD units. Our experimental results indicate that CSR-SIMD has better storage efficiency and low-overhead for format conversion. Besides, the new format achieves high scalability on wide SIMD units. In comparison with the CSR-based and BCSR-based SpMV, CSR-SIMD obtains better performance on FT1500A, Intel Xeon, and Intel Xeon Phi. © 2018 John Wiley & Sons, Ltd.

关键词： Storage efficiency

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Design of an Indoor Exploration and Multi-Objective Navigation System 37

Design of an Indoor Exploration and Multi-Objective Navigati...

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37th Chinese Control Conference, CCC 2018

作者： Zheng, Guoxian Zhang, Lei Zhang, Huaxi Yulin Ding, Bo State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China MIS INSSET Universite de Picardie Jules Verne Amiens80039 France National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410008 China

ISBN: (纸本)9789881563941

In this paper, we propose an indoor robot autonomous navigation system. The robot firstly explores in an unknown environment, and then navigates autonomously by using the explored map. The robot is equipped a 2D laser scanner as the main sensor. The laser scanner is used for path planning and frontier-based exploration. A 2D global occupancy map is built for path planning, frontier-based exploration and multi-objective autonomous navigation. Laser scans are transmitted into Simultaneous Localization and Mapping (SLAM) process in the exploration phase. In indoor environment, the exploration efficiency is improved by merging a heuristic algorithm. By using multi-threading technology and a 3D perception approach proposed in this paper, the robot equipped with a low-cost RGBD sensor can detect all kinds of obstacles to achieve highly reliable navigation in complicated 3D environment. Meanwhile, we develop a multi-objective navigation application to make human-robot interaction more convenient and satisfy multi-task deployment. Our approaches are demonstrated by experimental results. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Motion planning

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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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International Joint Conference on Neural Networks

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

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learning-based 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 vision-based 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 single-robot system.

关键词： Task analysis Robot kinematics Machine learning Multi-robot systems Quality of service Cloud computing

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