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Stash: A Comprehensive Stall-Centric Characterization of Public Cloud VMs for distributed Deep Learning

Stash: A Comprehensive Stall-Centric Characterization of Pub...

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International conference on distributed Computing Systems

作者： Aakash Sharma Vivek M. Bhasi Sonali Singh Rishabh Jain Jashwant Raj Gunasekaran Subrata Mitra Mahmut Taylan Kandemir George Kesidis Chita R. Das Computer Science and Engineering Pennsylvania State University Adobe Research

Deep neural networks (DNNs) are increasingly popular owing to their ability to solve complex problems such as image recognition, autonomous driving, and natural language processing. Their growing complexity coupled with the use of larger volumes of training data (to achieve acceptable accuracy) has warranted the use of GPUs and other accelerators. Such accelerators are typically expensive, with users having to pay a high upfront cost to acquire them. For infrequent use, users can, instead, leverage the public cloud to mitigate the high acquisition cost. However, with the wide diversity of hardware instances (particularly GPU instances) available in public cloud, it becomes challenging for a user to make an appropriate choice from a cost/performance standpoint. In this work, we try to address this problem by (i) introducing a comprehensive distributed deep learning (DDL) profiler Stash, which determines the various execution stalls that DDL suffers from, and (ii) using Stash to extensively characterize various public cloud GPU instances by running popular DNN models on them. Specifically, it estimates two types of communication stalls, namely, interconnect and network stalls, that play a dominant role in DDL execution time. Stash is implemented on top of prior work, DS-analyzer, that computes only the CPU and disk stalls. Using our detailed stall characterization, we list the advantages and shortcomings of public cloud GPU instances for users to help them make an informed decision(s). Our characterization results indicate that the more expensive GPU instances may not be the most performant for all DNN models and that AWS can sometimes sub-optimally allocate hardware interconnect resources. Specifically, the intra-machine interconnect can introduce communication overheads of up to 90% of DNN training time and the network-connected instances can suffer from up to 5× slowdown compared to training on a single instance. Furthermore, (iii) we also model the impact of DNN m

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Cryptography and Adversarial Analysis for Cloud Data Security

Cryptography and Adversarial Analysis for Cloud Data Securit...

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Innovative Trends in Information Technology (ICITIIT), International conference on

作者： S. Manjula K. Valarmathi M.Swarna Sudha Dept. of Computer Science and Engineering Ramco Institute of Technology Rajapalayam Tamil Nadu India Dept of Electronics and Communication Engineering P.S.R Engineering College Sivakasi Tamil Nadu India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Cloud computing, a novel paradigm for distributed data processing, has emerged as a transformative technology enabling the storage and retrieval of data over the Internet. The prevalence of Software-as-a-Service has revolutionized work dynamics, fostering a culture of secure remote access across diverse devices. Within the realm of Cloud Storage, the imperative for effective security and privacy measures is underscored. This research proposes a robust data security framework employing the Cryptography Splitting Algorithm. User files undergo encryption, increasing security levels from 25 to 75 are securely stored with a cloud service provider. The Cloud Manager empowers users by enhancing security, managing tracking mechanisms, and safeguarding metadata, thus fortifying data confidentiality and flexibility in utilization. Leveraging the Cryptography Splitting technique ensures secure data storage, concurrently enhancing both security and privacy for user data. Furthermore, the proposed methodology undergoes rigorous scrutiny in an adversary setup, incorporating neural network analysis to evaluate the resilience of data security measures. This analysis reveals the encryption scheme protects communication by maintaining a low $A-B$ loss around 0.1 while reducing Eve’s ability to intercept messages from 0.7 to 0.2. This research contributes a comprehensive approach to securely managing data in the dynamic landscape of cloud computing.

关键词： Cloud computing Data privacy Privacy Heuristic algorithms neural networks Software as a service Encryption

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Adaptive ResNet Architecture for distributed Inference in Resource-Constrained IoT Systems

Adaptive ResNet Architecture for Distributed Inference in Re...

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International Wireless Communications and Mobile Computing conference, IWCMC

作者： Fazeela Mazhar Khan Emna Baccour Aiman Erbad Mounir Hamdi College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distribute the neural network among a cluster of nodes. However, distribution may lead to additional energy consumption and dependency among devices that suffer from unstable transmission rates. Unstable transmission rates harm real-time performance of IoT devices causing low latency, high energy usage, and potential failures. Hence, for dynamic systems, it is necessary to have a resilient DNN with an adaptive architecture that can downsize as per the available resources. This paper presents an empirical study that identifies the connections in ResNet that can be dropped without significantly impacting the model’s performance to enable distribution in case of resource shortage. Based on the results, a multi-objective optimization problem is formulated to minimize latency and maximize accuracy as per available resources. Our experiments demonstrate that an adaptive ResNet architecture can reduce shared data, energy consumption, and latency throughout the distribution while maintaining high accuracy.

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distributed and Parallel Sparse Computing for Very Large Graph neural networks

Distributed and Parallel Sparse Computing for Very Large Gra...

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IEEE International conference on Big Data

作者： Quentin R. Petit Chong Li Nahid Emad Huawei Paris Research Center & Université Paris-Saclay Boulogne-Billancourt France Distributed and Parallel Software Lab Huawei Paris Research Center Boulogne-Billancourt France Maison de la Simulation & LI-PaRAD Université Versailles Saint-Quentin Saclay France

ISBN: (纸本)9781665480468

Deep learning (DL) requires high-performance processing on big data. Graph neural networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph’s sparse data structures is one of the key *** first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore.

关键词： Deep learning distributed databases Linear algebra Big Data Parallel machines Graph neural networks Libraries

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Research on convolutional neural network for video classification

Research on convolutional neural network for video classific...

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2021 International conference on Electronic Information Engineering and Computer Technology, EIECT 2021

作者： Zhou, Yaru Department of Mathematics China University of Mining and Technology Jiangsu Xuzhou221116 China

ISBN: (数字)9781510650510

ISBN: (纸本)9781510650503

Convolutional neural network (CNN) has recently been demonstrated as an effective model in machine learning for processing a large amount of data. In addition to its applications in natural language processing (NLP), CNN plays an important role in computer vision. This machine learning model can be applied to realize face recognition, image classification, image retrieval, object detection video classification, etc. In this paper, the author constructs an CNN model to classify videos based on the data set HMDB51. HMDB51 is a large human motion database, consisting of a total of 7,000 clips distributed in 51 action classes. Different from the 2D image, video is the three-dimensional data, including time and the width and height of each frame in the video. Therefore, it is necessary to process the video into a sequence of frames of uniform length and normalize the frames firstly. This would facilitate subsequent training and help prevent premature over-fitting as well. Besides, the impact of changing parameters in this CNN model would also be tested, such as the length of image frames, the number of sample categories, the batch size to training the data and the location of dropout layers. The results show that it is difficult to significantly improve the accuracy of recognition by adjusting these parameters alone, and those various factors need to be considered comprehensively © 2021 SPIE.

关键词： Classification (of information)

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Why go full? elevating federated learning through partial network updates 24

Why go full? elevating federated learning through partial ne...

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Proceedings of the 38th International conference on neural Information processing Systems

作者： Haolin Wang Xuefeng Liu Jianwei Niu Wenkai Guo Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China and Zhongguancun Laboratory Beijing China Center for AI Business Innovation Department of Management Science and Systems University at Buffalo Buffalo New York

ISBN: (纸本)9798331314385

Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditional federated learning, the entire parameter set of local models is updated and averaged in each training round. Although this full network update method maximizes knowledge acquisition and sharing for each model layer, it prevents the layers of the global model from cooperating effectively to complete the tasks of each client, a challenge we refer to as layer mismatch. This mismatch problem recurs after every parameter averaging, consequently slowing down model convergence and degrading overall performance. To address the layer mismatch issue, we introduce the FedPart method, which restricts model updates to either a single layer or a few layers during each communication round. Furthermore, to maintain the efficiency of knowledge acquisition and sharing, we develop several strategies to select trainable layers in each round, including sequential updating and multi-round cycle training. Our theoretical analysis and experimental results show that the FedPart method significantly outperforms traditional full-network update strategies, achieving faster convergence, greater accuracy, and reduced communication and computational overhead.

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Modeling User Interest Changes with Dynamic Differential Graphs for Item Recommendation 27

Modeling User Interest Changes with Dynamic Differential Gra...

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

作者： Zhu, Chengyu Zhu, Yanmin Lu, Xuansheng Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665408783

User interests are significant components in recommendation systems. Modeling user interests based on users' historical behaviors is a challenging problem, and many recommendation models have been proposed for user interests modeling, such as long-term and short-term interests modeling. In the real world, users' interests always change over time, however, existing models rarely consider users' interest changes. The purpose of this research is to apply graph neural networks to capture users' interest changes. This research first conducts data analysis on two public datasets, and results show that there are considerable amounts of users with a trend of interest changes. Based on this analysis, we construct user-category dynamic differential graphs, and we design a novel neural network based on dynamic differential graphs to learn users' interest changes representations from dynamic differential graphs. The learned representations are integrated with long-term and short-term interest representations to get users' final representations and make recommendations by getting scores with items. Different types of experiments are conducted to evaluate the performance of our proposed model, and experiment results show that the proposed model outperforms other baseline models. © 2021 IEEE.

关键词： Graph neural networks

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A Lightweight Anchor-Free Detector Using Cross-Dimensional Interactive Feature Balance for SAR Ship Detection 23

A Lightweight Anchor-Free Detector Using Cross-Dimensional I...

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Proceedings of the 2023 5th International conference on Video, Signal and Image processing

作者： Jiaming Shen Lin Bai Haijuan Wang Bo Ji Wenming Cheng Guocai Zhang School of Electronics and Control Engineering Changan University China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd China

ISBN: (纸本)9798400709272

In recent years, the deep convolutional neural network (CNN) model has rapidly developed, and it is widely used in SAR remote sensing target detection. Many methods utilize preset anchor boxes for target classification and boundary box coordinate regression. However, these methods encounter challenges when deployed to edge devices. Firstly, in SAR images, ships are sparsely and unevenly distributed, rendering most anchor boxes redundant. Secondly, the target detection model relying on anchor boxes demands extensive computation during anchor box generation, typically running on hardware-rich environments. Nevertheless, edge devices lack the necessary hardware environment to meet the deployment requirements of the CNN model. To address these issues, this paper proposes a lightweight anchor-free detector called Cross-Dimensional Interactive Feature Balance (CDIFB-Net). We adopt a key point strategy to predict the bounding box, eliminating the reliance on anchors. CDIFB-Net employs a partial convolution (PConv) based model as a lightweight feature extraction network to tackle the model's lightweight problem. Additionally, considering the characteristics of large target scale differences and unbalanced target distribution in SAR ship images, and to reduce the precision decline problem caused by lightweight, this paper introduces a cross-dimensional interactive feature balance pyramid (CDI-FBP), which balances semantic information of different levels through feature pyramid aggregation and averaging. The cross-dimensional interaction module (CDIM) establishes the relationship between the ship target and the background. Experiments conducted on the SAR Ship dataset (SSDD) and High-resolution SAR Image dataset (HRSID), along with inference experiments in different hardware environments, validate the effectiveness of the network for SAR ship detection. The experimental results demonstrate that the proposed CDIFB-Net maintains fast reasoning, low delay, and excellent detectio

关键词： CDI-FBP Convolutional neural network lightweight ship detection

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A Low Complexity Decentralized neural Net with Centralized Equivalence using Layer-wise Learning

A Low Complexity Decentralized Neural Net with Centralized E...

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International Joint conference on neural networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Liang, Xinyue Javid, Alireza M. Skoglund, Mikael Chatterjee, Saikat KTH Royal Inst Technol Sch Elect Engn & Comp Sci Stockholm Sweden

ISBN: (纸本)9781728169262

We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers). We assume the communication network between the workers is synchronized and can be modeled as a doubly-stochastic mixing matrix without having any master node. In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. Using altemating-direction-method-of-multipliers (ADMM) along with a layer-wise convex optimization approach, we propose a decentralized learning algorithm which enjoys low computational complexity and communication cost among the workers. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place. Finally, we experimentally illustrate the time complexity and convergence behavior of the algorithm.

关键词： decentralized learning neural network ADMM communication network

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DDoS Attacks Detection Using a Deep neural network Model 14th

DDoS Attacks Detection Using a Deep Neural Network Model

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12th International Advanced Computing conference, IACC 2022

作者： Mittal, Meenakshi Kumar, Krishan Behal, Sunny Department of IT UIET Panjab University Chandigarh India Department of Computer Science and Engineering Shaheed Bhagat Singh State University Ferozepur Firozpur India

ISBN: (纸本)9783031356438

In today’s world of globalization, with the exponential increase in the use of Internet-based services, there is also an increase in network traffic and a huge risk of hacking and cyber-attacks. One of the cyber-attacks is the distributed Denial of Service (DDoS) attack which simply sends the requests to the target for the denial of service for the legitimate users. To detect the DDoS attacks from the vast volume of network traffic, there is a need of a deep learning model. Also, there is the requirement for a lightweight model for today’s high-volume traffic and time critical applications. Therefore, in this paper, a deep neural network (DNN) model has been proposed and validated over the CICDDoS 2019 and PVAMUDDoS-2020 datasets with all features and reduced (important) features. The hyperparameters of the DNN model have been tuned and the best model has an input layer, five hidden layers, and an output layer. The proposed DNN model with the same hyperparameters values performed well over the CICDDoS 2019 and PVAMUDDoS-2020 datasets with all features and reduced features. The results are comparable in both cases and the evaluation with reduced features shows less training and testing time which would be helpful in the time-critical applications and the large volume of network traffic. © 2023, Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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