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Performance Analysis of distributed Deep Learning Frameworks in a Multi-GPU Environment

Performance Analysis of Distributed Deep Learning Frameworks...

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International conference on Ubiquitous Computing and Communications and International Symposium on Cyberspace and Security (IUCC-CSS)

作者： Tulasi Kavarakuntla Liangxiu Han Huw Lloyd Annabel Latham Samson B. Akintoye Manchester Metropolitan University Manchester UK

ISBN: (纸本)9781665466684

Deep Learning frameworks, such as TensorFlow, MXNet, Chainer, provide many basic building blocks for designing effective neural network models for various applications (e.g. computer vision, speech recognition, natural language processing). However, run-time performance of these deep learning frameworks varies significantly even when training identical deep network models on the same GPUs. This study presents an experimental analysis and performance model for assessing deep learning models (Convolutional neural networks (CNNs), Multilayer Perceptrons (MLP), Autoencoder) on three frameworks: TensorFlow, MXNet, and Chainer, in a multi-GPU environment. We analyse factors that influence these frameworks' performance by computing the running time of each framework in our proposed model, taking load imbalance factor into account. The evaluation results highlight significiant differences in the scalability of the frameworks, and the importance of load balance in parallel distributed deep learning.

关键词： Deep learning Training Analytical models Computational modeling Scalability neural networks Speech recognition

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A Predictive Real-Time Short-Term Battery Scheduling Method for Peak Load Shaving in Residential distributed Energy Storage Systems

A Predictive Real-Time Short-Term Battery Scheduling Method ...

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Smart Grid conference (SGC)

作者： Arash Barzkar Ashkan Barzkar Seyed Mohammad Hassan Hosseini South Tehran Branch Islamic Azad University Tehran Iran Virginia Tech Blacksburg USA

ISBN: (纸本)9781665401661

Providing a smooth power demand in many applications is necessary so as to maintain the Grid stability. Peak load shaving is a method in demand side management (DSM) requiring information on the future load profile data and forecasting methods. In this paper, an artificial neural network short-term load forecasting (ANNSTLF) method is adapted in order to apply forecasted load demand data to a previously discussed non-predictive real-time battery scheduling peak load shaving system. The introduced algorithm aims to provide peak load shaving and load smoothing in residential areas and can be considered for small processing systems. The ANNSTLF is implemented in a non-predictive peak load shaving algorithm, where uses profile data of a residual for a year to train the artificial neural network (ANN) to forecast the day ahead load demand. In order to assess the performance of the proposed method, simulations in MATLAB are done and predictive and the non-predictive approaches results are compared to each other.

关键词： Smoothing methods Load forecasting distributed databases Artificial neural networks Reservoirs Real-time systems Stability analysis

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Detection of DDoS based on Gray Level Co-occurrence Matrix theory and deep learning 5

Detection of DDoS based on Gray Level Co-occurrence Matrix t...

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5th International conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Shi, Jiayu Wu, Bin Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Beijing Univ Posts & Telecommun Natl Disaster Recovery Technol Engn Lab Beijing Peoples R China

ISBN: (纸本)9781665423144

There have been researches on distributed Denial of Service (DDoS) attack detection based on deep learning, but most of them use the feature data processed by data mining for feature learning and classification. Based on the original data flow, this paper combines the method of Gray Level Co-occurrence Matrix (GLCM), which not only retains the original data but also can further extract the potential relationship between the original data. The original data matrix and the reconstructed matrix were taken as the input of the model, and the Convolutional neural network(CNN) was used for feature learning. Finally, the classifier model was trained for detection. The experimental part is divided into two parts: comparing the detection effect of different data processing methods and different deep learning algorithms;the effectiveness and objectivity of the proposed method are verified by comparing the detection effect of the deep learning algorithm with that of the statistical analysis feature algorithm.

关键词： raw data flow Gray Level Co-occurrence Matrix Convolutional neural network DDoS attack detection

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Load Balancing Applying BPN in a Dual-Core Processor in IP Router Running with QUEST Scheduler 2nd

Load Balancing Applying BPN in a Dual-Core Processor in IP R...

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2nd International conference on Communication, Devices and Computing (ICCDC)

作者： Paul, Suman Pandit, Malay Kumar Haldia Inst Technol Dept ECE Haldia W Bengal India West Bengal Univ Technol Sch Engn & Technol Maulana Abul Kalam Azad Univ Technol West Bengal Kolkata India

ISBN: (纸本)9789811508295;9789811508288

In this paper, we investigate on a load balancing scheme applying neural network-backpropagation network (BPN) algorithm in a dual-core processor in IP router for processing of multimedia IP traffic. Load balancing is an important challenging issue among the cores of a processor in network routers. A balanced equally distributed and a steady-state load of the cores enhances the system quality of service (QoS) and performance. In this system, each core runs with a QoS-enhanced stochastic smart packet scheduler-QUEST. For analysis, we take both the cases of moderately and heavily unbalanced initial load of the cores of a dual-core processor in router. Each core is provisioned with the QUEST scheduler and runs to attain a ratio of process utilization which is of steady-state featured for scheduling and processing of multimedia IP traffic, namely, deadline-sensitive Voice over Internet Protocol, Internet Protocol Television, and best-effort Hypertext Transfer Protocol. We observe the computed balanced load applying the BPN for different conditions. Applying BPN, the load imbalance among the cores is minimized and the results show that load of each core has attained a target steady-state balanced load.

关键词： Backpropagation network (BPN) Dual-core processor in IP router Load balancing network QoS Scheduler

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

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

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

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

ISBN: (纸本)9781665413657

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.

关键词： Time-frequency analysis Continuous wavelet transforms Wavelet domain Information processing Media Denial-of-service attack Feature extraction

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Performance Evaluation of distributed Training in Tensorflow 2 14

Performance Evaluation of Distributed Training in Tensorflow...

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14th International conference on Advanced Computing and Applications, ACOMP 2020

作者： Quang-Hung, Nguyen Doan, Hieu Thoai, Nam Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam Hewlett Packard Enterprise Vietnam Ho Chi Minh City Viet Nam

ISBN: (纸本)9781728181677

Deep learning (DL) is most interested in Artificial Intelligence (AI) because it brings many interesting results in object identification, natural language processing, speech processing, application algorithms in autonomous vehicles, etc. DL also use neutron network. A useful neural architecture for an application may have tens or hundreds of millions of numbers that need to be selected appropriately during the training process. The most popular technique for training this architecture is a gradient-based iteration technique. To complete the training process, it needs to iterate over large amounts of data, which is why the training process is so slow. Without high performance computing (HPC) technology, it will take weeks or months to complete the training of network architecture. Training DL by distributed training is one of the solutions to improve the learning process faster and reduce computing time. In this paper, we study performance evaluation for distributed training strategy of the Tensorflow 2.2 on some GPUs. © 2020 IEEE.

关键词： network architecture

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Interactions within the Spatially-distributed Cortical neural network during Visual Attentional Tasks 4

Interactions within the Spatially-distributed Cortical Neura...

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4th International conference on Computational Intelligence and networks (CINE)

作者： Hramov, Alexander E. Innopolis Univ Ctr Technol Robot & Mechatron Component Neurosci & Cognit Technol Lab Innopolis Indonesia

ISBN: (纸本)9781728156880

Many physiological experiments demonstrate that a cognitive performance during sustained attention tasks is not maintained at a constant level. Such behavioral low-frequency variations are connected with dynamics of attention-related cortical neuronal networks. At the same time neurophysiological reason of a such behavior remains unclear. In this talk we analyze the neuronal interactions in alpha and beta bands by means of 31-channels EEG processing. As a visual attention task we use a binary classification of a large number of presented images - ambiguous Necker cubes. Analyzing of response time of subjects indicates that processing of similar tasks takes both long and short reaction time. Provided analysis based on complex network theory reveals strong correlation between response time and duration of formation of a functional frontoparietal functional network associated with perceptual decision-making process. Having considered a functional topology during a prestimulus period, we find that lower reaction times are subserved by a strong prestimulus functional connectivity over a somatosensory and sensorimotor areas of cortex. Finally, we show that high reaction times are dominant during the beginning of experiment and suppose that this time is required for a prestimulus template formation. Since similar visual tasks are presented several times during the experiment, brain uses their templates to perform them faster and effectively.

关键词： functional brain network direction of functional links recurrence-based measure of dependence visual stimulus processing wavelet analysis

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High Accuracy Matrix Computations on neural Engines: A Study of QR Factorization and its Applications 20

High Accuracy Matrix Computations on Neural Engines: A Study...

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29th International conference on High-Performance Parallel and distributed Computing (HPDC)

作者： Zhang, Shaoshuai Baharlouei, Elaheh Wu, Panruo Univ Houston Dept Comp Sci Houston TX 77204 USA

ISBN: (纸本)9781450370523

Fueled by the surge of ever expanding successful applications of deep neural networks and the great computational power demanded, modern computer processors and accelerators are beginning to offer half precision floating point arithmetic support, and special units (neural engines) such as NVIDIA TensorCore on GPU and Google Tensor processing Unit (TPU) to accelerate the training and prediction of deep neural networks. It remains unclear how neural engines can be profitably used in applications other than neural networks. In this paper we present an endeavor of accelerating and stabilizing a fundamental matrix factorization on neural enginesthe QR factorization-which may open doors to much wider relevance to scientific, engineering, and data science. We show that traditional Householder QR algorithms and implementations do not have the necessary data locality, parallelism, accuracy, and robustness on neural engines which are characterized by extreme speed and low precision/range. We demonstrate that neural engines can be effectively used to accelerate matrix computations (QR 3.0x-14.6x speedup compared to cuSOLVER, reaching up to 36.6TFLOPS);however different algorithms (recursive Gram-Schmidt) are needed to expose more locality and parallelism, even at the cost of increased computations. Moreover, scaling, iterative refinement, and other safeguarding procedures are also needed to regain accuracy and avoid overflowing. Our experience seems to suggest that presently with neural engines the matrix factorizations (QR, LU, Cholesky) are best to be co-designed with its applications (linear solver, least square, orthogonalization, SVD, etc) to achieve high performance and adequate accuracy and reliability.

关键词： tensorcore QR factorization v100 half precision mixed precision matrix computations GPU numerical linear algebra recursive QR Gram-Schmidt householder QR least square problem neural engine

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Using hybrid neural networks to detect DDOS attacks 3

Using hybrid neural networks to detect DDOS attacks

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3rd IEEE International conference on Data Stream Mining and processing, DSMP 2020

作者： Belej, Olexander Halkiv, Liubov Lviv Polytechnic National University Department of Computer-Aided Design Lviv Ukraine Lviv Polytechnic National University Department of Management of Organizations Lviv Ukraine

ISBN: (纸本)9781728132143

The results of the development of a denial of service network attack detection method for various services of storing, processing, and transmitting data on the Internet are presented. The focus is on the detection of low-level denial of service attacks. The opinion is refuted that special means for detecting denial of service attacks are not required since the fact of a detecting denial of service attacks cannot be overlooked. It is shown that for effective counteraction it is necessary to know the type, nature, and other indicators of a denial of service attack, and distributed attack detection systems allow you to quickly obtain this information. Also, the use of this type of attack detection system can significantly reduce the time it takes to determine the fact of an attack from 2-3 days to several tens of minutes, which reduces the cost of traffic and the downtime of the attacked resource. A hybrid neural network based on the Kohonen network and a multilayer perceptron is used as a detection module. The work of the created prototype of the attack detection system, the methodology for the formation of the training sample, the course of experiments, and the topology of the experimental stand are described. The results of an experimental study of the prototype are presented, during which errors of the first and second kind, respectively. © 2020 IEEE.

关键词： Denial-of-service attack

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Learning Heterogeneous DAG Tasks Scheduling Policies With Efficient neural network Evolution

Learning Heterogeneous DAG Tasks Scheduling Policies With Ef...

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

作者： Weiyu Tan Tian Ling Zhenyu Zeng Ying Tao Yuxia Cheng School of Computer Science Hangzhou Dianzi University Hangzhou China

neural networks based on NeuroEvolution of Augmenting Topology (NEAT) attracted much attention owing to multiple novel features. It is expected to develop optimal net structure adaptively through evolution and applied to solve specific problems. However, the difficulty of indicating problem status and determining evolution direction along with long execution time makes NEAT hard to show its superiority in many applications. This paper proposed a new DAG scheduling algorithm NeuroSched based on NEAT and parallel Reinforcement Learning. In Markov Decision Processes we use evolved network as a bridge between State of DAG and Action of strategy, and guide network’s evolution direction through Fitness which is calculated by Reward, both of which are under parallel RL environment. Experimental results show that our algorithm is superior to contrasted algorithms in multiple indicators like Makespan and SLR on different scales of DAG, and the parallel process greatly reduces execution time compared with previous evolutionary algorithms.

关键词： Training Scheduling algorithms network topology neural networks Reinforcement learning Evolutionary computation Markov processes

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