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TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI Accelerators 29th

TrainBF: High-Performance DNN Training Engine Using BFloat16...

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29th international conference on parallel and distributed computing (Euro-Par)

作者： Xie, Zhen Raskar, Siddhisanket Emani, Murali Vishwanath, Venkatram Argonne Natl Lab Lemont IL 60439 USA

ISBN: (纸本)9783031396977;9783031396984

Training deep neural networks (DNNs) with half-precision floating-point formats is widely supported on recent hardware and frameworks. However, current training approaches using half-precision formats neither obtain the optimal throughput due to the involvement of single-precision format nor achieve state-of-the-art model accuracy due to lower numerical digits. In this work, we present a new DNN training engine, named TrainBF, which leverages a typical half-precision format BFloat16 to maximize training throughput while ensuring sufficient model accuracy. TrainBF deploys BFloat16 across the entire training process for best throughput and improves model accuracy by introducing three proposed normalization techniques. TrainBF is also lightweight by only applying these normalization techniques to the layers that are most critical to model accuracy. Furthermore, TrainBF implements a parallel strategy that parallelizes the execution of operators in DNN training to make use of the spare memory space saved by half-precision for better throughput. Evaluating with six common DNN models and compared with the state-of-the-art mixed-precision approach, TrainBF achieves competitive model accuracy with an average throughput speedup of 1.21x, 1.74x, and 1.16x on NVIDIA A100 GPU, AMD MI100 GPU, and an emerging AI accelerator SambaNova, respectively.

关键词： Deep neural networks

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2023 IEEE 3rd international conference on Computer Communication and Artificial Intelligence, CCAI 2023

2023 IEEE 3rd International Conference on Computer Communica...

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3rd IEEE international conference on Computer Communication and Artificial Intelligence, CCAI 2023

ISBN: (纸本)9798350335262

The proceedings contain 83 papers. The topics discussed include: self-supervised representation learning for time series via temporal contrasting and transformation;early warning and screening of elderly cognitive impairment based on machine learning algorithm;chewing detection using brightness changes in video based on deep learning;multi-agent reinforcement learning investigation based on football games;predicting the trajectory of ai utilizing the Markov model of machine learning;machine learning approach to sentiment recognition from periodic reports;deep learning models for cancer classification from microarray gene expression profiles;neural abstractive summarization: a brief survey;towards accurate crowd counting via smoothed dilated convolutions and transformer;hyperbolic graph convolutional networks for aspect-based sentiment analysis;survey of neuromorphic computing: a data science perspective;poultry disease identification based on light weight deep neural networks;fairness and effectiveness in federated learning on non-independent and identically distributed data;and corpus database management design for Chinese-Portuguese bidirectional parallel corpora.

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A Comparative Review on State-of-the-art Clustering techniques for Vehicular Ad-Hoc networks 7

A Comparative Review on State-of-the-art Clustering techniqu...

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7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Goyal, Geetanjali Goel, Shubham Punjabi University Department of Computer Science Patiala India Jaypee University of Information Technology Department of CSE/IT Waknaghat Solan India

ISBN: (纸本)9781665454018

Advancements in wireless technologies have given start to the exposure of vehicular ad-hoc networks (VANETs). It is a special kind of Mobile ad-hoc network that has a capability to provide communication among vehicles in a close proximal space. Moreover, VANETs even have capability to support the communication between the vehicles and some special kind to nearby equipment's that are fixed on roadsides commonly named as Road Side Units (RSU). The main goal of VANETs is to reduce vehicular traffic and to provide comfortable and safe journey to passengers. In VANETs there are certain limitations i.e. dynamic topology changes, high mobility of nodes, packet loss, rapid pattern movement and network fragmentation. There is a great risk involved during communication in VANETs. To provide safe and reliable communication among nodes, clustering techniques are designed. With the dynamic nature of VANETs, stability of clusters is a very key challenging task. Several clustering algorithms has been presented by the researchers based on the stability of cluster formation. This paper surveys and analyzes the state-of-the-art clustering techniques designed for stability of clusters and safe communication. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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Link Attributes Based Multi-service Routing for Software-Defined Satellite networks 24th

Link Attributes Based Multi-service Routing for Software-Def...

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24th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2023

作者： Lu, Xueyu Wei, Wenting Fu, Liying Zhang, Dong State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China

ISBN: (纸本)9789819982103

Satellite networks are the potential complementary of terrestrial networks, which are expected to provide full-coverage and broadband access anywhere, anytime. As satellite networks scale up, Software-Defined Satellite Network (SDSN) is a promising paradigm due to its higher flexibility in network management. However, in the SDSN with highly time-varying characteristics, the traditional terrestrial routing strategy can hardly meet the QoS requirements for diverse services. In this paper, we propose a Link-Attributes-based multi-service On-Demand Routing (LAODR) algorithm under SDSN architecture. It quantifies the reliability of the Inter-Satellite Links (ISL) and provides a fine-grained state description of the dynamic topology. Furthermore, we select the K-shortest path as the solution space and reasonably allocate link resources based on LAODR to meet the diverse service demands of users. We implement LAODR and conduct experiments by using real network topologies. The results validate that LAODR not only satisfies the QoS requirements of different types of services but also outperforms other routing algorithms in terms of mean end-to-end latency, packet loss ratio, throughput and node congestion degree. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network topology

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Enhanced Multiple Convolutional Neural networks Based CCS P System for Precise Classification in Membrane System-Based Applications 6th

Enhanced Multiple Convolutional Neural Networks Based CCS P ...

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6th international conference on Information Systems and Management Science, ISMS 2023

作者： Lakshmi, M. Gayathri Vinodhini, G. Arul Freeda Nagappan, G. Thomas, D. Gnanaraj Saveetha Engineering College Chennai India Chennai India Saveetha Engineering College Chennai India Department of Mathematics Madras Christian College Chennai India

ISBN: (纸本)9783031707889

In the realm of pseudo-random simultaneous physiological computing models, exemplified by the innovative cell-like P systems (CLPS) with channel sections and symport ad antiport rules known as CCS P systems, this paper introduces a progressive variant—CCSs P systems. This augmentation incorporates a synchronization rule, adding a nuanced layer to the original CCS P system's channel sections and symport/antiport rules. The study delves into the computational analysis of drug sensitivity data, meticulously generated by Rising for various cancer cell lines and an array of chemicals. With a specific focus on advancing anticancer treatments for brain tumours, the research confronts the inherent challenges of accurate segmentation in the clinical context. Given the diverse sizes, shapes, and individual variations of brain tumours, achieving precise segmentation is a formidable task. In response, this study pioneers a distinctive fully convolutional neural network, seamlessly integrating a feature reuse module for iterative structure utilization and a feature conformity module to enhance the synthesis of multiple feature maps while minimizing noise. The ubiquity of convolutional neural networks (CNNs) is undeniable, yet their time-intensive training, resource demands, and data complexity necessitate novel approaches. To tackle these challenges, the research introduces a groundbreaking hypergraph membrane system—an innovative distributed and parallel computation paradigm poised to implement the Multiple Convolutional Neural networks (MCNN). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Exploring 5G for Vehicular networks 7

Exploring 5G for Vehicular Networks

引用

7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Khurana, Meenu Sharma, Ishu Kaur, Gurtej Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781665454018

The ongoing revolution in wireless communications with the advent of 5G has opened plethora of opportunities for the applications demanding high capacity, low-latency, high reliability and security. 5G is expected to cater to the diverse and stringent requirements of the wireless applications owing to better manageability, scalability, privacy, security and efficiency offered by it in comparison to earlier frameworks. Vehicular Adhoc networks (VANETs) for intelligent transportation systems, is one such application which will be benefitted the most due to its diverse requirements for the types of applications ranging from safety, comfort to commercial. We have presented our perspective on the impact of basic building blocks of 5G framework in VANETs. Firstly, the application specific requirements of VANETs have been detailed. The existing VANET standard fulfilling these requirements is listed along with its limitations. Secondly, the features and key benefits of 5G in context to VANETs have been presented. Finally, the basic building blocks of 5G including device to device technology and edge-slicing are explored in VANETs and the associated challenges are highlighted. © 2022 IEEE.

关键词： Intelligent systems

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distributed Deep Learning with Data parallelism for Diabetic Retinopathy Classification 1

引用

7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Patil, Sanskruti S. Patil, Mahesh S. Chickerur, Satyadhyan Giraddi, Shantala Shahapur, Seetharam N. Hadalageri, Anup School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India Center for High Performance Computing KLE Technological University Karnataka Hubballi580031 India

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

The large-scale model training is typically slower and necessitates high-performance workstations and distributed Deep Learning (DDL). The DDL models trained on a massive volume of data can outperform single accelerators being used, i.e., the performance of deep learning (DL) models can be enhanced by using distributed and parallel deep learning methods. But the DDL models need to be redesigned and evaluated for particular hardware resources and applications, requiring the literature on various DDL model evaluations for assistance. Authors in the proposed work design and evaluate DDL with data parallelism for classifying Diabetic retinopathy (DR) images. A performance comparative study on the DDL strategy implemented on DenseNet is presented where the model is trained on multi-GPUs with counts from 1 to 8. The results show that the model takes less time when trained on multi-GPUs compared to single GPU. The detailed evaluation analysis of DDL with data parallelism is presented in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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Facilitating Decentralized and Opportunistic Learning in Pervasive computing

Facilitating Decentralized and Opportunistic Learning in Per...

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20th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Lee, Sangsu Julien, Christine Zheng, Xi Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Macquarie Univ Dept Comp N Ryde NSW Australia

ISBN: (纸本)9781665416474

We explore algorithms and develop tools to support the collaboration between devices to build personalized deep learning models for pervasive computing applications. In our work, we first investigate whether individual devices belonging to different users can learn robust models that are personalized to their user's own experiences while preserving the privacy of the data that resides on the devices. We term this approach Opportunistic Collaborative Learning (OppCL), and we build a resilient learning algorithm that ameliorates the negative effects when coping with unpredictable mobility patterns. Moreover, we extend OppCL from networks of homogeneous devices to heterogeneous ones by employing lossy compression techniques for deep learning models. Furthermore, we develop a programming environment for developers to test decentralized learning algorithms in scale.

关键词： pervasive computing federated learning collaborative deep learning distributed machine learning

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Memory Efficient Deep Neural Network Training 27th

Memory Efficient Deep Neural Network Training

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27th international European conference on parallel and distributed computing (Euro-Par)

作者： Shilova, Alena Inria Bordeaux Sud Ouest Bordeaux France Univ Bordeaux Bordeaux France

ISBN: (纸本)9783031061561;9783031061554

Recently Artificial Intelligence (AI) has demonstrated a huge progress in solving complex problems such as image classification, text generation, translation... Its success is due to a development of hardware and algorithms making possible the emergence of Deep Neural networks (DNNs). Such DNNs are composed of a number of operations from numerical linear algebra, whose order is defined with a Directed Acyclic Graph (DAG). These DNNs operate at the limit of the computational resources, and to go to deeper and more complex neural networks it is necessary either to design more powerful computational resources or optimize their usage. In our work, we address in particular the problem of lowering memory usage during the training of DNNs.

关键词： Neural networks Rematerialization Offloading Model parallelism

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Data Volume-aware Computation Task Scheduling for Smart Grid Data Analytic Applications

Data Volume-aware Computation Task Scheduling for Smart Grid...

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IEEE international conference on Communications (IEEE ICC)

作者： Guo, Binquan Li, Hongyan Yan, Ye Zhang, Zhou Wang, Peng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Tianjin Artificial Intelligence Innovat Ctr TAIIC Tianjin Peoples R China

ISBN: (纸本)9781538674628

Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.

关键词： Smart grid applications data analytics task scheduling job completion time branch and cut disjunctive formulation

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