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Realization and optimization DGEMM on ARMv8 64-bit multi-core processor

Dongbei Daxue Xuebao/Journal of Northeastern University 2014年 35卷 37-43页

作者： Jiang, Hao Wang, Feng Zuo, Ke Li, Kuan Yang, Can-Qun College of Computer Science National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

The double-precision matrix-matrix multiplication (DGEMM) on ARMv8 64-bit multi-core processor architecture was realized and optimized, and the optimal model for the purpose of maximizing the compute-to-memory access ratio was built to design DGEMM kernel. The ARM 64-bit memory accessing instruction, Cache pre-fetching instruction and NEON vector FMA instruction were utilized through instruction reordering and loop unrolling to construct the kernel assembly codes. The blocking and packing algorithms and parallel methods from GotoBLAS (OpenBLAS) were chosen, and the results showed that the floating-point peak efficiency can achieve 82% with one thread and 80% with eight threads, respectively. As the fastest DGEMM implementation on ARMv8 64-bit processor, it improves the peak performance by 8.3% and 16.7% compared to ATLAS. ©, 2014, Northeastern University. All right reserved.

关键词： Digital arithmetic

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Maximizing Uniform Multicast Throughput in Multi-Channel Dense Wireless Sensor Networks

Maximizing Uniform Multicast Throughput in Multi-Channel Den...

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

作者： Xianlong Jiao Guirong Chen Xiaodong Wang Yuli Chen Li Yang College of Information System and Management National University of Defense Technology Changsha Information and Navigation College Air Force Engineering University Xi'an Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Chongqing Guanyinqiao Elementary School Chongqing Chongqing Liangjiangxinqu Renhe Experimental School Chongqing

ISBN: (纸本)9781509056972

This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with each other on multiple orthogonal channels. This kind of networks show wide application in the real world, and maximizing uniform multicast throughput for these networks is worth deep studying. Previous researches have proved MUMT problem is NP-hard. However, previous researches are either hard to implement, or use too many relay nodes to complete the multicast task, and thus incur high overhead or poor performance. To efficiently solve MUMT problem, we adopt the concept of the maximum independent set with the size constraint, and present one novel Single-Broadcast based Multicast algorithm called SBM based on the concept. We prove that SBM algorithm achieves a constant ratio to the theoretical throughput upper bound. Extensive experimental results demonstrate that, SBM performs better than existing work in terms of both the uniform multicast throughput and the total number of transmissions.

关键词： Throughput Wireless sensor networks Approximation algorithms Schedules Data communication Algorithm design and analysis Relays

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A Physics and Data-Driven Hybrid PINNs Intelligent Computing Method for Nuclear Engineering Simulation

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yufei Xie Wenlin Wang Guohua Wu Yang Yu Ping An Zibin Sun Haichuan Zhang Shengfeng Luo Yue Li School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. These challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. This paper introduces an innovative intelligent computing approach—a Physics-Informed Neural Networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. The proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model.

关键词： Solid modeling Accuracy Fluids Nuclear energy Computational modeling Pipelines Neural networks Predictive models Numerical models Accidents

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Trace Bench: An Open Data Set for Trace-Oriented Monitoring

Trace Bench: An Open Data Set for Trace-Oriented Monitoring

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Jingwen Zhou Zhenbang Chen Ji Wang Zibin Zheng Michael R. Lyu College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

User request trace-oriented monitoring is an effective method to improve the reliability of cloud systems. However, there are some difficulties in getting traces in practice, which hinder the development of trace-oriented monitoring research. In this paper, we release a fine-grained user request-centric open trace data set, called Trace Bench, collected on a real world cloud storage system deployed in a real environment. During collecting, many aspects are considered to simulate different scenarios, including cluster size, request type, workload speed, etc. Besides recording the traces when the monitored system is running normally, we also collect the traces under the situation with faults injected. With a mature injection tool, 14 faults are introduced, including function faults and performance faults. The traces in Trace Bench are clustered in different files, where each file corresponds to a certain scenario. The whole collection work lasted for more than half a year, resulting in more than 360, 000 traces in 361 files. In addition, we also employ several applications based on Trace Bench, which validate the helpfulness of Trace Bench for the field of trace-oriented monitoring.

关键词： Monitoring Servers Educational institutions Instruments Cloud computing Context Fault diagnosis

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Research on Human Pose Classification Based on Improved SVM

Research on Human Pose Classification Based on Improved SVM

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第33届中国控制与决策会议

作者： Yingda Zhang Hongyan Ma Yongxue Ye Jiaming Dou Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

With the development of intelligent buildings and aging of population,designing a high-accuracy alarm system is essential to prevent the elderly from accidentally falling *** address the problems of poor generalization and low accuracy of the SVM in the application of indoor elderly fall alarm systems,in this paper,the human keypoints detection algorithm,which increases the discrimination of the dataset,is proposed to pre-process the *** on this,the features of images are extracted from the histogram of the oriented gradient and the gray-level co-occurrence matrix,and the improved SVM algorithm is then used again for image *** simulation results show that the classification accuracy of using SVM to detect the dataset is around 91%,while the accuracy of the dataset classified as using improved SVM is around 97%.The method proposed in this paper is effective in increasing the accuracy of computer recognition of accidental falls by the elderly.

关键词： SVM Human keypoints Detection Image Classification Accidental Indoor Falls Aging of Population

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RoSAS: Deep Semi-supervised Anomaly Detection with Contamination-resilient Continuous Supervision

arXiv

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arXiv 2023年

作者： Xu, Hongzuo Wang, Yijie Pang, Guansong Jian, Songlei Liu, Ning Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing China College of Computer National University of Defense Technology Hunan Changsha410073 China School of Computing and Information Systems Singapore Management University Singapore178902 Singapore College of Information and Communications National University of Defense Technology Hubei Wuhan430010 China

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training;2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies. The source code is available at https://***/xuhongzuo/rosas/. © 2023, CC BY.

关键词： Contamination

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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Research on Energy Saving Control of Lighting Systems in Public Buildings

Research on Energy Saving Control of Lighting Systems in Pub...

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第33届中国控制与决策会议

作者： Shengyan Li Hongyan Ma Yongxue Ye Jiaming Dou Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

Most indoor users are unwilling to adjust the lighting equipment and shutters in daily life,resulting in increasing energy consumption of *** this paper,in order to reduce the energy consumption,using fuzzy control to solve this *** using fuzzy control,it could control the opening of indoor lamps,and let the shutters could determine the opening degree of the shutters according to the difference between the set illuminance values and the actual illuminance values on the working *** results show that the fuzzy control of classroom lighting system can reasonably control the running status of lamps according to the actual situation of the environment and reduce the energy consumption of the lighting *** with the single fuzzy controller,the double fuzzy controller of the shutter can make the difference between the illuminance value of indoor working surface,and the set illuminance value smaller,control the light sources in different areas of the room,and improve the uniformity of natural light in the *** method proposed in this paper can solve the energy consumption problem.

关键词： Lighting System Shutter Double Fuzzy Control System Fuzzy Control

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Symmetric Non-negative Matrix Factorization Based Link Partition Method for Overlapping Community Detection

Symmetric Non-negative Matrix Factorization Based Link Parti...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiang Zhang Naiyang Guan Wenju Zhang Xuhui Huang Shuyi Wu Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Hunan P.R. China Institute of Software College of Computer National University of Defense Technology Hunan P.R. China Department of Computer Science and Technology College of Computer National University of Defense Technology Hunan P.R. China

ISBN: (纸本)9781479986989

Partitioning links rather than nodes is effective in overlapping community detection (OCD) on complex networks. However, it consumes high CPU and memory overheads because the volume of links is huge especially when the network is rather complex. In this paper, we proposes a symmetric non-negative matrix factorization (SNMF) based link partition method called SNMF-Link to overcome this deficiency. In particular, SNMF-Link represents data in a lower-dimensional space spanned by the node-link incidence matrix. By solving a lighter SNMF problem, SNMF-Link learns the clustering indicators of each links. Since traditional multiplicative update rule (MUR) based optimization algorithm for SNMF suffers from slow convergence, we applied the augmented Lagrangian method (ALM) to efficiently optimize SNMF. Experimental results show that SNMF-Link is much more efficient than the representative clustering algorithms without reducing the OCD performance.

关键词： Symmetric matrices Convergence Optimization Partitioning algorithms Chlorine Complex networks Image edge detection

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Convolutional neural network-based age estimation using B-mode ultrasound tongue image

arXiv

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arXiv 2021年

作者： Xu, Kele Csapó, Tamás Gábor Feng, Ming National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary MTA-ELTE Lendület Lingual Articulation Research Group Budapest Hungary Tongji University Shanghai China

Ultrasound tongue imaging is widely used for speech production research, and it has attracted increasing attention as its potential applications seem to be evident in many different fields, such as the visual biofeedback tool for second language acquisition and silent speech interface. Unlike previous studies, here we explore the feasibility of age estimation using the ultrasound tongue image of the speakers. Motivated by the success of deep learning, this paper leverages deep learning on this task. We train a deep convolutional neural network model on the UltraSuite dataset. The deep model achieves mean absolute error (MAE) of 2.03 for the data from typically developing children, while MAE is 4.87 for the data from the children with speech sound disorders, which suggest that age estimation using ultrasound is more challenging for the children with speech sound disorder. The developed method can be used a tool to evaluate the performance of speech therapy sessions. It is also worthwhile to notice that, although we leverage the ultrasound tongue imaging for our study, the proposed methods may also be extended to other imaging modalities (e.g. MRI) to assist the studies on speech production. © 2021, CC BY-NC-SA.

关键词： Ultrasonics

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