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Freeing Hybrid distributed AI Training Configuration 2021

Freeing Hybrid Distributed AI Training Configuration

引用

29th ACM Joint Meeting on European Software Engineering conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Wang, Haoran Univ Orleans LIFO Orleans France Huawei Paris Res Ctr DPSL Paris France

ISBN: (纸本)9781450385626

Deep neural network (DNN) has become the leading technology to realize Artificial Intelligence (AI). As DNN models become larger and more complex, so do datasets. Being able to efficiently train DNNs in parallel has become a crucial need. Data Parallelism (DP) is the widest-used solution today to accelerate DNN training but could be inefficient when processing DNNs with large-size parameters. Hybrid Parallelism (HP), which applies different parallel strategies on different parts of DNNs, is more efficient but requires advanced configurations. Not all AI researchers are experts in parallel computing, thus automating the configuration of HP strategies is very desirable for all AI frameworks. We propose a parallel semantics analysis method, which can analyze the trade-offs among different kinds of parallelisms and systematically choose the HP strategies with good training time performance. We demonstrated experimentally 260% speedup when applying our method compared to using a conventional DP approach. With our proposal, AI researchers would be able to focus more on AI algorithm research without being disturbed by parallel analysis and engineering concerns.

关键词： distributed Deep Learning Systematic Partitioning Performance Analysis AI Framework

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Hybrid Model of Artificial neural network-Cuckoo Search for Irradiance and Load Forecasting

Hybrid Model of Artificial Neural Network-Cuckoo Search for ...

引用

IEEE URUCON conference (IEEE URUCON)

作者： Ferraz, Rafael S. F. Ferraz, Renato S. E. Rueda-Medina, Augusto C. Fardin, Jussara F. Univ Fed Espirito Santo UFES Postgrad Program Elect Engn PPGEE Vitoria ES Brazil

ISBN: (纸本)9781665424431

It is worth noting the importance of irradiance and load time series forecasting, in order to contribute to the planning and operation of the smart-grids, due to the increasing distributed Energy Resource (DER) integration in the electricity network. Thus, Artificial neural network (ANN) has been widely adopted, in recent studies, to solve this forecasting problem. However, there is not a consolidated methodology which defines the better combination of some ANN parameters, such as the number of historical data in the input and neurons in the hidden layer. Therefore, in this paper, the authors proposed an optimization methodology which minimizes the Mean Absolute percentage error (MAPE), considering the lowest amount of historical data in the input and neurons in the hidden layer. Thus, this study compared a modified Cuckoo Search (CS) and Particle Swarm Optimization (PSO) performance with the exhaustive search, in order to validate the optimization methodology results. It was possible to notice, from the results, that the CS presented lower MAPE than the PSO, and both presented a considerably lower processing time than trial-and-error.

关键词： Artificial neural network Cuckoo search Forecasting Irradiance Load

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STOCHASTIC GRAPH neural networkS

STOCHASTIC GRAPH NEURAL NETWORKS

引用

IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Gao, Zhan Isufi, Elvin Ribeiro, Alejandro Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Delft Univ Technol Dept Intelligent Syst Delft Netherlands

ISBN: (纸本)9781509066315

Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. However, current GNN implementations assume ideal distributed scenarios and ignore link fluctuations that occur due to environment or human factors. In these situations, the GNN fails to address its distributed task if the topological randomness is not considered accordingly. To overcome this issue, we put forth the stochastic graph neural network (SGNN) model: a GNN where the distributed graph convolutional operator is modified to account for the network changes. Since stochasticity brings in a new paradigm, we develop a novel learning process for the SGNN and introduce the stochastic gradient descent (SGD) algorithm to estimate the parameters. We prove through the SGD that the SGNN learning process converges to a stationary point under mild Lipschitz assumptions. Numerical simulations corroborate the proposed theory and show an improved performance of the SGNN compared with the conventional GNN when operating over random time varying graphs.

关键词： Graph neural networks graph filters random link losses convergence analysis distributed learning

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Trace-based Intelligent Fault Diagnosis for Microservices with Deep Learning 45

Trace-based Intelligent Fault Diagnosis for Microservices wi...

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45th Annual International IEEE-Computer-Society Computers, Software, and Applications conference (COMPSAC)

作者： Chen, Hao Wei, Kegang Li, An Wang, Tao Zhang, Wenbo Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China

ISBN: (纸本)9781665424639

Due to the scalability, fault tolerance, and high availability, distributed microservice-based applications gradually replace traditional monolithic applications as one of the main forms of Internet applications. However, current fault diagnosis methods for distributed applications have drawbacks in coarse-grained fault location and inaccurate root-cause analysis. To address the above issues, this paper proposes a trace-based intelligent fault diagnosis approach for microservices with deep learning. First, we build a request weighted directed graph and a request string to characterize the behaviors of microservices with collected historical traces. Then, we build a normal trace dataset in normal status and a faulty dataset by injecting faults, and then calculate the expected intervals of microservices' response time and the call sequences. After that, we train the fault diagnosis model based on the deep neural network with the trace datasets to diagnose faulty microservices. Finally, we have deployed a typical open-source microservice-based application TrainTicket to validate our approach by injecting various typical faults. The results show that our approach can effectively characterize the behavior of microservices when processing requests and effectively detect faults. For fault detection, our approach achieves 91.5% accuracy in detecting faults, and has the accuracy of 85.2% in locating root causes.

关键词： microservice fault diagnosis deep learning distributed tracing

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Straggler Mitigation through Unequal Error Protection for distributed Matrix Multiplication

Straggler Mitigation through Unequal Error Protection for Di...

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IEEE International conference on Communications (ICC)

作者： Tegin, Busra Hernandez, Eduin E. Rini, Stefano Duman, Tolga M. Bilkent Univ Dept Elect & Elect Engn Ankara Turkey Natl Yang Ming Chiao Tung Univ Dept Elect & Comp Engn Hsinchu Taiwan

ISBN: (纸本)9781728171227

Large-scale machine learning and data mining methods routinely distribute computations across multiple agents to parallelize processing. The time required for computation at the agents is affected by the availability of local resources giving rise to the "straggler problem" in which the computation results are held back by unresponsive agents. For this problem, linear coding of the matrix sub-blocks can be used to introduce resilience toward straggling. The Parameter Server (PS) utilizes a channel code and distributes the matrices to the workers for multiplication. It then produces an approximation to the desired matrix multiplication using the results of the computations received at a given deadline. In this paper, we propose to employ Unequal Error Protection (UEP) codes to alleviate the straggler problem. The resiliency level of each sub-block is chosen according to its norm as blocks with larger norms have higher effects on the result of the matrix multiplication. We validate the effectiveness of our scheme both theoretically and through numerical evaluations. We derive a theoretical characterization of the performance of UEP using random linear codes, and compare it the case of equal error protection. We also apply the proposed coding strategy to the computation of the back-propagation step in the training of a Deep neural network (DNN), for which we investigate the fundamental trade-off between precision and the time required for the computations.

关键词： distributed computation Approximate matrix multiplication Straggling servers Unequal error protection

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ADSwitch: Adaptive CPU Scheduling for distributed Systems

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yidong Zhong Yang Ao Dingding Li Jianguo Li Deze Zeng School of Computer Science South China Normal University Guangzhou Guangdong China School of Computer Science China University of Geosciences Wuhan Hubei China

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. Therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. The experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness.

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Sub-Resolution mmWave FMCW Radar-based Touch Localization using Deep Learning

Sub-Resolution mmWave FMCW Radar-based Touch Localization us...

引用

IEEE conference on Vehicular Technology (VTC)

作者： Raghunandan M. Rao Amit Kachroo Koushik A. Manjunatha Morris Hsu Rohit Kumar Amazon Lab126 Sunnyvale CA USA Amazon Web Services (AWS) Santa Clara CA

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Touchscreen-based interaction on display devices are ubiquitous nowadays. However, capacitive touch screens, the core technology that enables its widespread use, are prohibitively expensive to be used in large displays because the cost increases proportionally with the screen area. In this paper, we propose a millimeter wave (mmWave) radar-based solution to achieve sub-resolution error performance using a network of four mmWave radar sensors. Unfortunately, achieving this is non-trivial due to inherent range resolution limitations of mmWave radars, since the target (human hand, finger etc.) is ‘distributed’ in space. We overcome this using a deep learning-based approach, wherein we train a deep convolutional neural network (CNN) on range-FFT (range vs power profile)-based features against ground truth (GT) positions obtained using a capacitive touch screen. To emulate the clutter characteristics encountered in radar-based positioning of human fingers, we use a metallic finger mounted on a metallic robot arm as the target. Using this setup, we demonstrate sub-resolution position error performance. Compared to conventional signal processing (CSP)-based approaches, we achieve a 2−3× reduction in positioning error using the CNN. Furthermore, we observe that the inference time performance and CNN model size support real-time integration of our approach on general purpose processor-based computing platforms.

关键词： Location awareness Vehicular and wireless technologies Accuracy Spaceborne radar Computational modeling Touch sensitive screens Millimeter wave radar Real-time systems Convolutional neural networks Millimeter wave communication

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The Capability of Wavelet Convolutional neural network for Detecting Cyber Attack of distributed Denial of Service in Smart Grid 18

The Capability of Wavelet Convolutional Neural Network for D...

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18th International Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2021

作者： Monday, Happy Nkanta Li, Jian Ping Nneji, Grace Ugochi Yutra, Abel Zenebe Lemessa, Bona Debela Nahar, Saifun James, Edidiong Christopher Haq, Amin Ul University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu Sichuan 611731 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan 611731 China University of Missouri School of Computer and Information Systems St LouisMO United States

ISBN: (纸本)9781665413640

The electrical system's dependability, security, and efficiency are all improved through smart grid technologies. Its dependence on digital communication technology, on the other hand, introduces new risks and vulnerabilities that should be examined for the purpose to providing effective and trustworthy service delivery. This study presents a method for the detection of distributed denial of service (DDoS) attacks on smart grid infrastructure. Continuous wavelet transform (CWT) is used in the suggested approach to convert one-dimensional traffic data to two-dimensional time-frequency domain scalogram as the input to the wavelet convolutional neural network (WavCovNet) to detect anomalous behavior in the data by distinguishing attack features from normal patterns. Our results demonstrate that the proposed approach detects DDoS attacks with a high rate of detection and with a very low rate of false alarm. © 2021 IEEE.

关键词： Smart power grids

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7th China High-resolution Earth Observation conference, CHREOC 2020

7th China High-resolution Earth Observation Conference, CHRE...

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7th China High-resolution Earth Observation conference, CHREOC 2020

ISBN: (纸本)9789811657344

The proceedings contain 41 papers. The special focus in this conference is on China High-resolution Earth Observation. The topics include: A Fast and Robust UAV Images Mosaic Method;a Differential Flat Approach for Attitude Manoeuver Control of Agile Satellite;robust Detection and Tracking Algorithm for Pedestrian Targets with Posture Change;weight Factor Determination of Reverse Distance Weighting Method in Computation of Geomagnetic Diurnal Variation Data;research of Industrial Heat Sources Detection Method Based on Thermal Infrared Satellite Data;comparative Analysis of Chinese High-Resolution Satellite Data for Sugarcane Classification Based on U-Net Model;a Voxel-Based Fusing Method for Aerial Laser Scanning and Oblique Image Point Cloud Via Noise-and-Occupancy-Aware;FD-LinkNet: A Encoder-decoder Structure network for High Resolution Satellite Imagery Rural Road Extraction;preliminary Evaluation of the Stereo Mapping Accuracy of the Gaofen-7;Capability Requirement Analysis of Space-Based Ocean Reconnaissance and Surveillance System Based on Improved QFD Method;top-of-Atmosphere Radiance Image Simulation from the Visible to Thermal Infrared;distributed HRWS MIMO-SAR Linear Vertical Baseline Estimation and Compensation;Ship Azimuth Velocity Estimation in Spaceborne SAR Based on Minimum-Entropy Criterion and Newton’s Method;mapping 30-m Resolution Land Cover of China Based on Full Convolutional neural network;processing and Application of AMS-3000 Large Field of View Triple Line Array Camera in Photogrammetry;Research on Brightness Compensation Algorithm for GF-3 SAR Images;Evaluation and Analysis of Geometric Accuracy of the ZY-1 02D VNIC Image;Analysis and Optimization of Geometric Accuracy of ZY-1 02C HRC Image;research on Space-Ground Integrated Control Technology of Earth Observation Spacecraft.

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STNN: A Spatial-Temporal Graph neural network for Traffic Prediction 27

STNN: A Spatial-Temporal Graph Neural Network for Traffic Pr...

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27th IEEE International conference on Parallel and distributed Systems, ICPADS 2021

作者： Yin, Xueyan Li, Feifan Wu, Genze Wang, Pengfei Shen, Yanming Qi, Heng Yin, Baocai School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781665408783

Accurate traffic prediction is of great importance in Intelligent Transportation System. This problem is very challenging due to the complex spatial and long-range temporal dependencies. Existing models generally suffer two limitations: (1) GCN-based methods usually use a fixed Laplacian matrix to model spatial dependencies, without considering their dynamics;(2) RNN and its variants are only capable of modeling a limited-range temporal dependencies, resulting in significant information loss. In this paper, we propose a novel spatial-temporal graph neural network (STNN), an end-to-end solution for traffic prediction that simultaneously captures dynamic spatial and long-range temporal dependencies. Specifically, STNN first uses a spatial attention network to model complex and dynamic spatial correlations, without any expensive matrix operations or relying on predefined road network topologies. Second, a temporal transformer network is utilized to model long-range temporal dependencies across multiple time steps, which considers not only the recent segment, but also the periodic dependencies of historical data. Making full use of historical data can alleviate the difficulty of obtaining real-time data and improve the prediction accuracy. Experiments are conducted on two real-world traffic datasets, and the results verify the effectiveness of the proposed model, especially in long-term traffic prediction. © 2021 IEEE.

关键词： Topology

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