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An Experimental Study of Deep neural networks on HPC Clusters 5th

An Experimental Study of Deep Neural Networks on HPC Cluster...

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5th Russian Supercomputing Days conference (RuSCDays)

作者： Buryak, Dmitry Popova, Nina Voevodin, Vladimir Konkov, Yuri Ivanov, Oleg Shaykhlislamov, Denis Fateev, Ilya Lomonosov Moscow State Univ Fac Computat Math & Cybernet Moscow Russia

ISBN: (纸本)9783030365929;9783030365912

Deep neural networks (DNN) offer great opportunities for solving many problems associated with processing large-scale data. Building and using deep neural networks requires large computational resources. In this regard, the question naturally arises about the possibility of using HPC-systems for the implementation of DNN. In order to better understand the performance implications of DNN on High Performance clusters we analyze the performance of several DNN-models over 2 HPC systems: the Lomonosov-2 supercomputer (the section with processors equipped with P100 GPUs) and the Polus high-performance cluster based on IBM Power8 processors with P100 GPUs. Comparing these frameworks is interesting as they represent different types of processors (Intel for Lomonosov-2 and IBM for Polus). Apart from different processor architectures, these systems feature different internode communications, which may affect the performance of the analysed algorithms in case of parallel and distributed implementation of neural network algorithms. The studies were carried out on the basis of the PyTorch framework.

关键词： Deep neural networks HPC cluster GPU Lomonosov-2 Power8

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Deep Graph Library: Overview, Updates, and Future Developments [GrAPL 2020 Keynote Speaker]

Deep Graph Library: Overview, Updates, and Future Developmen...

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IEEE International Symposium on Parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： George Karypis (AWS Deep Learning Science University of Minnesota)

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, ecommerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph neural networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. Deep Graph Library (DGL) is an open source development framework for writing and training GNN-based models. It is designed to simplify the development of such models by using graphbased abstractions while at the same time achieving high computational efficiency and scalability by relying on optimized sparse matrix operations and existing highly optimized standard deep learning frameworks (e.g., MXNet, PyTorch, and TensorFlow). This talk provides an overview of DGL, describes some recent developments related to high-performance multi-GPU, multi-core, and distributed training, and describes our future development roadmap.

关键词： Data mining conferences Bioinformatics Libraries Machine learning Social network services

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Real-Time Vehicle Traffic Analysis using Long Short Term Memory networks in Apache Spark

Real-Time Vehicle Traffic Analysis using Long Short Term Mem...

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Emerging Trends in Information Technology and Engineering (ic-ETITE), conference on

作者： S Anveshrithaa K Lavanya School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Escalating traffic congestion in large and rapidly evolving metropolitan areas all around the world is one of the inescapable problems in our daily lives. In light of this situation, traffic monitoring and analytics is becoming the need of the hour. Real-time traffic analysis requires the processing of data streams that are generated continuously to gain quick insights. In order to processes stream data at a faster rate, we need technologies with high computing capacity. Big data frameworks such as Apache Hadoop, Spark and Kafka, with their capability of processing a massive amount of data, have made it possible to develop advanced and efficient data stream processing systems. The challenge of analyzing data streams for real-time prediction can be overcome by exploiting deep learning techniques. Taking this as a motivation, this work aims at developing a real-time data stream processing model for forecasting vehicle traffic, using Long Short-Term Memory (LSTM) networks to learn and train itself from traffic data. In the proposed analytical framework, the traffic data from an API is streamed using a distributed streaming platform called Kafka into the machine learning model in Apache Spark for analysis. The proposed model is aimed at predicting traffic flow information by integrating Spark and Kafka along with deep neural networks, that will be of great value to the citizens as well as the government by reducing the travel time, cost and energy, thus having a positive impact on the environment and the society.

关键词： Apache Kafka LSTM neural networks real-time analytics Spark streaming traffic prediction Traffic forecasting neural network ISOCHRONOUS CYCLOTRONS Traffic data Environmental impact Short-Term Memory streams Traffic Analysis traffic congestion

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16th IFIP WG 10.3 International conference on network and Parallel Computing, NPC 2019

16th IFIP WG 10.3 International Conference on Network and Pa...

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16th IFIP WG 10.3 International conference on network and Parallel Computing, NPC 2019

ISBN: (纸本)9783030307080

The proceedings contain 37 papers. The special focus in this conference is on International conference on network and Parallel Computing, NPC 2019. The topics include: High Performance DDoS Attack Detection System Based on Distribution Statistics;DDP-B: A distributed Dynamic Parallel Framework for Meta-genomics Binary Similarity;Optimal Resource Allocation Through Joint VM Selection and Placement in Private Clouds;a Parallel Multi-keyword Top-k Search Scheme over Encrypted Cloud Data;N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance;MMSR: A Multi-model Super Resolution Framework;HiPower: A High-Performance RDMA Acceleration Solution for distributed Transaction processing;LDAPRoam: A Generic Solution for Both Web-Based and Non-Web-Based Federate Access;characterizing Perception Module Performance and Robustness in Production-Scale Autonomous Driving System;Game-Based Multi-MD with QoS Computation Offloading for Mobile Edge Computing of Limited Computation Capacity;Spindle: A Write-Optimized NVM Cache for Journaling File System;two-Erasure Codes from 3-Plexes;deep Fusion: A Software Scheduling Method for Memory Access Optimization;optimizing Data Placement on Hierarchical Storage Architecture via Machine Learning;i/O Optimizations Based on Workload Characteristics for Parallel File Systems;Energy Consumption of IT System in Cloud Data Center: Architecture, Factors and Prediction;Efficient processing of Convolutional neural networks on SW26010;ADMMLIB: A Library of Communication-Efficient AD-ADMM for distributed Machine Learning;energy-Aware Resource Scheduling with Fault-Tolerance in Edge Computing;DIN: A Bio-Inspired distributed Intelligence networking;KLSAT: An Application Mapping Algorithm Based on Kernighan–Lin Partition and Simulated Annealing for a Specific WK-Recursive NoC Architecture;A DAG Refactor Based Automatic Execution Optimization Mechanism for Spark.

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Research on short text classification method based on convolution neural network 3rd

Research on short text classification method based on convol...

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3rd International conference on Intelligent, Interactive Systems and Applications, IISA2018

作者： Wang, Lei Chen, Qiaohong Sun, Qi Jia, Yubo School of Information Zhejiang Sci-Tech University Hangzhou China

ISBN: (纸本)9783030028039

Short text classification is one of the hotspots of research in Natural Language processing. a new model of text representation is proposed in this paper (N-of-DOC), and in order to solve the problem of sparse representation in Chinese, the word2vec distributed representation is used, finally, it is applied to the improved convolution neural network model (CNN) to extract the high level features from the filter layer, the classification model is obtained by connecting the softmax classifier after the pooling layer. In the experiment, the traditional text representation model and the improved text representation model are used as the input of the original data, respectively. It acts on the model of traditional machine learning (KNN, SVM, logistic regression, naive Bayes) and the improved convolution neural network model. The results show that the proposed method can not only solve the dimension disaster and sparse problem of Chinese text vectors, but also improve the classification accuracy by 10.23% compared with traditional methods. © Springer Nature Switzerland AG 2019.

关键词： neural network models

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Adaptive Coherent Sampling for network Delay Measurement

Adaptive Coherent Sampling for Network Delay Measurement

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IEEE International conference on Communications (ICC)

作者： Shuo Liu Qiaoling Wang Network Technology Laboratory 2012 Labs Huawei Technologies Co. Ltd Shenzhen P.R.China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

End-to-end network delay, as a metric to indicate the QoS (Quality-of-Service), plays an important role in distributed services. Unfortunately, it is infeasible in practice to know all node-pair delay information due to the quadratic growth of overhead by active probing. In this paper, we leverage the stateof-the-art matrix completion technology for better network delay estimation from limited measurements. Although the number of samples required for exact matrix completion is theoretically bounded, it is practically less helpful as the number cannot be specified. This motivates us to propose an adaptive coherent sampling algorithm to select the elements with larger leverage scores to maintain the characteristic of important rows or columns in the delay matrix. The number of samples is adaptively determined by a proposed stopping criterion. Simulation results based on real-world network delay datasets indicate that our proposed algorithm is capable of providing better performance (improves estimation error by 16.9% and convergence stress by 28.9%) at less cost (reduces number of samples by 3.9% and processing time by 78.6%) than traditionally used algorithms.

关键词： Delays Matrix decomposition Linear matrix inequalities Peer-to-peer computing Artificial neural networks Sparse matrices Indexes

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

Interactions within the Spatially-distributed Cortical Neura...

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International conference on Computational Intelligence and networks (CINE)

作者： Alexander E. Hramov Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Component Innopolis University

ISBN: (数字)9781728156880

ISBN: (纸本)9781728156897

Many physiological experiments demonstrate that a cognitive performance during sustained attention tasks is not maintained at a constant level. Such behavioral lowfrequency variations are connected with dynamics of attentionrelated 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.

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Parallax: Sparsity-aware Data Parallel Training of Deep neural networks 19

Parallax: Sparsity-aware Data Parallel Training of Deep Neur...

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14th EuroSys conference

作者： Kim, Soojeong Yu, Gyeong-In Park, Hojin Cho, Sungwoo Jeong, Eunji Ha, Hyeonmin Lee, Sanha Jeong, Joo Seong Chun, Byung-Gon Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450362818

The employment of high-performance servers and GPU accelerators for training deep neural network models have greatly accelerated recent advances in deep learning (DL). DL frameworks, such as TensorFlow, MXNet, and Caffe2, have emerged to assist DL researchers to train their models in a distributed manner. Although current DL frameworks scale well for image classification models, there remain opportunities for scalable distributed training on natural language processing (NLP) models. We found that current frameworks show relatively low scalability on training NLP models due to the lack of consideration to the difference in sparsity of model parameters. In this paper, we propose Parallax, a framework that optimizes data parallel training by utilizing the sparsity of model parameters. Parallax introduces a hybrid approach that combines Parameter Server and AllReduce architectures to optimize the amount of data transfer according to the sparsity. Experiments show that Parallax built atop TensorFlow achieves scalable training throughput on both dense and sparse models while requiring little effort from its users. Parallax achieves up to 2.8x, 6.02x speedup for NLP models than TensorFlow and Horovod with 48 GPUs, respectively. The training speed for the image classification models is equal to Horovod and 1.53x faster than TensorFlow.

关键词： sparsity-aware data parallel training deep learning framework graph transformation

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Artificial Intelligence Techniques for Fault Location and Detection in distributed Generation Power Systems 8

Artificial Intelligence Techniques for Fault Location and De...

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8th International conference on Modern Power Systems (MPS)

作者： Darab, Cosmin Tarnovan, Radu Turcu, Antoniu Martineac, Corina Tech Univ Cluj Napoca Dept Elect Power Syst Cluj Napoca Romania

ISBN: (纸本)9781728107509

There are multiple reasons that distributed generation systems are exposed to faults and errors. Among them we can consider the most common ones being failure of the power system components or lighting strikes. Power system failure is usually caused by components failure, human error or equipment aging. The above-mentioned events affect the power system reliability and imply expensive repairs, lost of productivity and dissatisfied customers. Due to the fact that faults are unpredictable, fast fault location and isolation is necessary to minimize the impact of fault in distribution systems. Therefore, many methods have been previously developed by researchers to locate and detect these faults in distribution power systems that incorporate distributed generation. The main available methods can be split into two categories: conventional and artificial intelligence techniques. Conventional methods are composed of travelling wave method and impedance-based method while artificial intelligence techniques include Artificial neural network, Support Vector Machine, Fuzzy Logic, Genetic Algorithm and matching approach. However, fault detection using intelligent methods are difficult to implement due to the fact that it requires training data for processing and also are time consuming. This paper presents some of the newly introduced artificial intelligence techniques for islanding detection in distributed generation power networks. Research works in islanding detection, control algorithms, advantages and disadvantages are presented, hence the opportunities in islanding detection research area in power distribution system can be explored further.

关键词： wavelet transform fault detection photovoltaic system islanding detection

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2019 International conference on Engineering and Telecommunication, EnT 2019

2019 International Conference on Engineering and Telecommuni...

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2019 International conference on Engineering and Telecommunication, EnT 2019

ISBN: (纸本)9781728135649

The proceedings contain 67 papers. The topics discussed include: virtual and physical testing of advanced driver assistance systems with soft targets;single-channel 8K ultra-high definition digital television transmission using polarization diversity;high-quality 3D medical imaging by wavelet filters with reduced coefficients bit-width;modernized method of processing low-based LFM signal burst;NLP methods for automatic candidate's CV segmentation;determination of the interfering signal field strength near a TV receiver;hypersonic vehicle trajectory classification using convolutional neural network;and printed emitters with distributed excitation.

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