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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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19th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2019

19th International Conference on Algorithms and Architecture...

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19th international conference on Algorithms andArchitectures for parallel Processing, ICA3PP 2019

ISBN: (纸本)9783030389604

The proceedings contain 102 papers. The special focus in this conference is on Algorithms and Architectures for parallel Processing. The topics include: Utility-Based Location Distribution Reverse Auction Incentive Mechanism for Mobile Crowd Sensing Network;safeguarding Against Active Routing Attack via Online Learning;reliability Aware Cost Optimization for Memory Constrained Cloud Workflows;null Model and Community Structure in Heterogeneous networks;an Asynchronous Algorithm to Reduce the Number of Data Exchanges;two-Stage Clustering Hot Event Detection Model for Micro-blog on Spark;mobility-Aware Workflow Offloading and Scheduling Strategy for Mobile Edge computing;HSPP: Load-Balanced and Low-Latency File Partition and Placement Strategy on distributed Heterogeneous Storage with Erasure Coding;adaptive Clustering for Outlier Identification in High-Dimensional Data;Improving the parallelism of CESM on GPU;penguin Search Aware Proactive Application Placement;a Data Uploading Strategy in Vehicular Ad-hoc networks Targeted on Dynamic Topology: Clustering and Cooperation;Cloud Server Load Turning Point Prediction Based on Feature Enhanced Multi-task LSTM;Neuron Fault Tolerance Capability Based Computation Reuse in DNNs;reliability Enhancement of Neural networks via Neuron-Level Vulnerability Quantization;A Fault Detection Algorithm for Cloud computing Using QPSO-Based Weighted One-Class Support Vector Machine;paraMoC: A parallel Model Checker for Pushdown Systems;FastDRC: Fast and Scalable Genome Compression Based on distributed and parallel Processing;a parallel Approach to Advantage Actor Critic in Deep Reinforcement Learning;Blockchain-PUF-Based Secure Authentication Protocol for Internet of Things;parallel Approach to Sliding Window Sums;selective Velocity distributed Indexing for Continuously Moving Objects Model;a New Bitcoin Address Association Method Using a Two-Level Learner Model.

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PREEMPT: Scalable Epidemic Interventions Using Submodular Optimization on Multi-GPU Systems

PREEMPT: Scalable Epidemic Interventions Using Submodular Op...

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international conference on High Performance computing, Networking, Storage and Analysis (SC)

作者： Minutoli, Marco Sambaturu, Prathyush Halappanavar, Mahantesh Tumeo, Antonino Kalyananaraman, Ananth Vullikanti, Anil Pacific Northwest Natl Lab Richland WA 99352 USA Univ Virginia Charlottesville VA USA Washington State Univ Pullman WA 99164 USA

ISBN: (纸本)9781728199986

Preventing and slowing the spread of epidemics is achieved through techniques such as vaccination and social distancing. Given practical limitations on the number of vaccines and cost of administration, optimization becomes a necessity. Previous approaches using mathematical programming methods have shown to be effective but are limited by computational costs. In this work, we present PREEMPT, a new approach for intervention via maximizing the influence of vaccinated nodes on the network. We prove submodular properties associated with the objective function of our method so that it aids in construction of an efficient greedy approximation strategy. Consequently, we present a new parallel algorithm based on greedy hill climbing for PREEMPT, and present an efficient parallel implementation for distributed CPU-GPU heterogeneous platforms. Our results demonstrate that PREEMPT is able to achieve a significant reduction (up to 6.75x) in the percentage of people infected and up to 98% reduction in the peak of the infection on a city scale network. We also show strong scaling results of PREEMPT on up to 128 nodes of the Summit supercomputer. Our parallel implementation is able to significantly reduce time to solution, from hours to minutes on large networks. This work represents a first-of-its -kind effort in parallelizing greedy hill climbing and applying it toward devising effective interventions for epidemics.

关键词： Epidemic networks vaccination submodular influence maximization CPU-GPU parallel algorithms.

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Optimizing one by one direct convolution on ARMv8 multi-core CPUs

Optimizing one by one direct convolution on ARMv8 multi-core...

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IEEE international conference on Joint Cloud computing (JCC)

作者： Wang, Qinglin Li, Dongsheng Mei, Songzhu Shen, Siqi Huang, Xiandong Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha 410073 Peoples R China

ISBN: (纸本)9781728169811

Convolutional layers are ubiquitous in a variety of deep neural networks. Due to the lower computation complexity and the smaller number of parameters, convolutions with small filter sizes are often used, such as one by one convolution. Nevertheless, these small convolution operations are still time-consuming. A common approach to implementing convolutions is to transform them into matrix multiplications, known as GEMM-based convolutions. The approach maybe incurs additional memory overhead, and calls matrix multiplication routines which are not optimized for matrices generated by convolutions. In this paper, we present a new parallel one by one direct convolution implementation on ARMv8 multi-core CPUs, which doesn't incur any additional memory space requirement. Our implementation is verified on two ARMv8 CPUs, Phytium FT-1500A and FT-2000plus. In terms of performance and scalability, our implementation is better than GEMM-based implementations in all the tests on Phytium FT-1500A. On Phytium FT-2000plus, our approach gives much better performance and scalability than GEMM-based approaches in most cases.

关键词： CNNs Direct convolution ARMv8 parallel computing

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LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification 21st

LightLayers: Parameter Efficient Dense and Convolutional Lay...

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21st international conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2020

作者： Jha, Debesh Yazidi, Anis Riegler, Michael A. Johansen, Dag Johansen, Håvard D. Halvorsen, Pål SimulaMet Oslo Norway UIT The Arctic University of Norway Tromsø Norway Oslo Metropolitan University Oslo Norway

ISBN: (纸本)9783030692438

Deep Neural networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud hosted infrastructures may face a long waiting time before the training completes or might not be able to train a model at all. Investigating novel ways to reduce the training time could be a potential solution to alleviate this drawback, and thus enabling more rapid development of new algorithms and models. In this paper, we propose LightLayers, a method for reducing the number of trainable parameters in DNNs. The proposed LightLayers consists of LightDense and LightConv2D layers that are as efficient as regular Conv2D and Dense layers but uses less parameters. We resort to Matrix Factorization to reduce the complexity of the DNN models resulting in lightweight DNN models that require less computational power, without much loss in the accuracy. We have tested LightLayers on MNIST, Fashion MNIST, CIFAR 10, and CIFAR 100 datasets. Promising results are obtained for MNIST, Fashion MNIST, and CIFAR-10 datasets whereas CIFAR 100 shows acceptable performance by using fewer parameters. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Efficient Wireless Static Chargers Deployment for UAV networks

Efficient Wireless Static Chargers Deployment for UAV Networ...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Mengqi Li Liang Liu Yanlin Wang Jianfei Peng Jie Xi Lisong Wang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

To prolong the lifetime of unmanned aerial vehicle (UAV) networks, this paper studies how to efficiently deploy wireless static chargers (WSCs) in UAV networks (WDU). That is, given a set of UAVs, determining the minimum number of WSCs as well as the location of each WSC, so that UAVs do not run out of energy during flight. We first formulate WDU as an optimization problem, which is proved to be NP-hard. Then to solve WDU, we propose the scheme of selecting WSCs’ locations by transforming WDU into a binary integer programming (BIP) problem (SLTB). In SLTB, we first propose a novel area discretization method to reduce the solution space of WDU, and then WDU is reformulated as a BIP problem that can be easily solved by existing methods. Finally, experiments are conducted to evaluate the performance of SLTB in terms of reducing the number of WSCs.

关键词： Wireless communication Integer programming Autonomous aerial vehicles Optimization

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Edge-Based Sampling for Complex Network with Self-Similar Structure

Edge-Based Sampling for Complex Network with Self-Similar St...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Wenting Wang Xianghua Fu Xinze Lin Shenzhen Technology University Shenzhen China

The network with self-similarity is an essential data structure widely used in social networks and bioinformatics. The network sampling as an important method for subgraph mining has attracted attention in many applications. However, traditional methods are computationally complexity and time- consuming. Due to the increase of the data scale, the algorithms’ runtime grows exponentially. A new methodology needs to handle the computational complexity challenge from large-scale graph datasets. In this paper, we develop a series of edge-based sampling algorithms: HEPS-1 and HEPS-2. We investigate the relations between edges and their edge neighbours caused by the self- similarity. Then, we determine the edges for the subgraph. In this way, we sample the networks while keeping the original statistical characters. The computational complexity is low. The effectiveness of sampling is enhanced.

关键词： Runtime Social networking (online) Complex networks Data structures Data mining Computational complexity Bioinformatics

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Plant disease detection using clustering based segmentation and neural networks 6

Plant disease detection using clustering based segmentation ...

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6th international conference on parallel, distributed and Grid computing, PDGC 2020

作者： Mohan, Aditya Srivastava, Kushagra Malhotra, Garima Khan, Nafis Uddin Jaypee University of Information Technology Solan India

ISBN: (纸本)9781728171326

Farmer suicides in India had ranged between 1.4 and 1.8 per hundred thousand people, accounting to 11.2 % of all suicides in India due to reasons like debt, low produce prices, crops failure and alcohol addiction. Among these, crop failure is attributed to various factors including unpredictable weather conditions, poor farming practices, pests and diseases along withill use of fertilizers and late disease diagnosis. Various systems have been proposed and implemented for immediate identification of the disease, using mobile devices for disease identification and consequent action, but the majority of proposed approaches involve segmentation techniques coupled with classical machine learning algorithms, which focused on the entire plant or fruit image, not primarily on the diseased part, thus embedding pixels which introduce possible bias in each data point leading to an imprecise training dataset and consequently faulty training. In this paper we propose a method of leveraging a combination of clustering based segmentation for identification of the diseased part exclusively and consequent feature extraction over it along with using neural networks over classical algorithms, thereby increasing feature complexity and thus better training, increasing training accuracy and leaving scope for further integration of huge amount of data which can added later on. © 2020 IEEE.

关键词： Diagnosis

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Detecting malignancy of ovarian tumour using convolutional neural network: A review 6

Detecting malignancy of ovarian tumour using convolutional n...

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6th international conference on parallel, distributed and Grid computing, PDGC 2020

作者： Mathur, Mansi Jindal, Vikas Wadhwa, Gitanjali Computer Science and Engineering Chandigarh University Mohali Punjab India

ISBN: (纸本)9781728171326

Ovaries are important part of female reproductive system. The importance of these tiny glands is derived from the production of female sex hormones and female gametes. The location of these ductless almond shaped small glandular organs is on just opposite sides of uterus attached with ovarian ligament. There are many factors due to which ovarian cancer can occur but it can be detected by using various techniques and among them there is one method named as convolutional neural network. This review paper tells us about how we can use Convolutional Neural Network to classify the ovarian cancer tumour and what other ways to deal with it. In this research work we have also discussed about the comparison of various machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network used in detection of ovarian cancer. After comparing the different methods for this cancer detection, it seemed Deep Learning Technique to be the best for yielding results. © 2020 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Security Algorithms used in Cloud computing

Comparative Analysis of Security Algorithms used in Cloud Co...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Mohd Tajammul Rafat Parveen Iftikhar Aslam Tayubi Jamia Millia Islamia New Delhi India King Abdulaziz University Jeddah Saudi Arabia

Cloud computing is a technique that enables the users to access applications to infrastructure through subscription methods on remote location. Cloud computing, being collective technique, possesses features of various technologies like Virtual Private Network (VPN), distributed technology, parallel computing, grid computing as well as ubiquitous computing. Cloud storage is the infrastructure offered by the cloud computing in order to facilitate users to upload and access their data on cloud space. Due to multitenancy of cloud storage, the data uploaded on it is the more prone to security breaches. To secure the data on cloud, cryptographic technique is used which works like safeguard against data leakage. This research work focuses on cryptographic algorithms from most basic to advance and also presents their comparative analysis. Moreover, the paper focuses on the nature of the algorithms in context of cloud that is homogeneous and heterogeneous nature. In the cloud computing, homogeneous nature algorithm is used only if the encryptors and decryptors both are same while heterogeneous refers to the situation in which encryptors and decryptors are different entities.

关键词： Cloud computing parallel processing Ubiquitous computing Grid computing Virtual private networks Cryptography

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