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Spangle: A distributed In-Memory processing System for Large-Scale Arrays 37

Spangle: A Distributed In-Memory Processing System for Large...

引用

37th IEEE International conference on Data Engineering (IEEE ICDE)

作者： Kim, Sangchul Kim, Bogyeong Moon, Bongki Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781728191843

With increasing volumes of scientific data, a scalable and parallel computing framework is required for scientific analysis in computer simulations and experiments. Scientific data are commonly generated in multi-dimensional arrays, and the array data model is appropriate to store them for analysis, including for data mining and arithmetic computation. In this paper, we introduce an array processing system called Spangle. It is implemented on top of Apache Spark, a popular map-reduce framework for complex computation workloads. To support array data computation, we extended Resilient distributed Dataset (RDD) based on the array data model named ArrayRDD. ArrayRDD is an inherently parallel data structure that provides fault-tolerance. In addition, by adopting the array data model, Spangle provides an interface for expressing machine learning algorithms, which heavily rely on linear algebra. We tailored two popular algorithms, PageRank and Stochastic Gradient Descent, for large-scale datasets in Spangle.

关键词： Productivity Machine learning algorithms Stochastic processes Machine learning Linear algebra Programming parallel processing

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BalanceNet Orchestrator: A KQV-based Dynamic Task Allocation for distributed Deep Learning

BalanceNet Orchestrator: A KQV-based Dynamic Task Allocation...

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International conference on Information networking

作者： Teh-Jen Sun Thien-Thu Ngo Eui-Nam Huh Department of Artificial Intelligence Kyung Hee University Yongin-si South Korea Department of Computer Science and Engineering Kyung Hee University Yongin-si South Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

In Artificial Intelligence(AI), training expansive models with billions of parameters necessitates substantial computational resources. This requirement has led to the adoption of parallel computing frameworks. However, these frameworks often confront node performance imbalances due to disparities in computational capabilities and network conditions. To address this issue, we introduce the BalanceNet Orchestrator(BNO), a dynamic task allocation algorithm designed to equilibrate workloads in parallel training environments. BalanceNet Orchestrator assesses and adjusts to node-specific performance in real time, facilitating optimal workload distribution and resource utilization. This method significantly enhances training efficiency and accelerates model convergence, presenting an efficient approach for training large-scale AI models within parallel training architecture.

关键词： Training Computational modeling Heuristic algorithms Computer architecture parallel processing Dynamic scheduling Real-time systems

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SPIRIT: A Microservice-based Framework for Interactive Cloud Infrastructure Planning 27th

SPIRIT: A Microservice-Based Framework for Interactive Cloud...

引用

27th International European conference on parallel and distributed Computing (Euro-Par)

作者： Koulouzis, Spiros Bianchi, Riccardo van Der Linde, Robin Wang, Yuandou Zhao, Zhiming Univ Amsterdam Multiscale Networked Syst Res Grp Amsterdam Netherlands Virtual Lab Innovat Ctr LifeWatch ERIC Amsterdam Netherlands

ISBN: (纸本)9783031061561;9783031061554

The IaaS model provides elastic infrastructure that enables the migration of legacy applications to cloud environments. Many cloud computing vendors such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform offer a pay-per-use policy that allows for a sustainable reduction in costs compared to on-premise hosting, as well as enable users to choose various geographically distributed data centers. Using state-of-the-art planning algorithms can help application owners to estimate the size and characteristics of the underlying cloud inveterate. However, it's not always clear which is the optimal solution especially in multi-cloud environments with complex application requirements and QoS constraints. In this paper, we propose an open framework named SPIRIT, which allows a user to include cloud infrastructure planning algorithms and to evaluate and compare their solutions. SPIRIT achieves this by allowing users to interactively study infrastructure planning algorithms by adjusting parameters via a graphical user interface, which visualizes the results of these algorithms. In the current prototype, we have included from the IaaS Partial Critical Path algorithm. By taking advantage of SPIRIT's microservice-based architecture and its generic interfaces a user can add to the framework, new planning algorithms. SPIRIT can transform an abstract workflow described using the CWL to a concrete infrastructure described using the TOSCA specification. This way the infrastructure descriptions can be ranked on various key performance indicators.

关键词： Virtual infrastructure planning IaC Microservice IaaS Workflow IC-PCP

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Question Classification Method based on Self-Attention Mechanism and BiLSTM-AlexNet Model

Question Classification Method based on Self-Attention Mecha...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Yumeng Wang Yinshan Jia School of Artificial Intelligence and Software LiaoNing Petrochemical University Fushun China

With the rapid development of Natural Language processing and deep learning algorithms, research and applications based on intelligent question answering have gained widespread attention from researchers and the industry. As the primary step in intelligent question answering, the accuracy of question classification is crucial to answer generation. In order to solve the problems of single model and poor classification performance in Chinese question classification, this paper proposes a question classification model based on self-attention mechanism combined with BiLSTM-AlexNet model (named A-Bi-Al) . Firstly, word2vec-based word vector representation is performed, and then the local features of the question text are extracted using convolutional neural network (AlexNet). Secondly,bidirectional long short-term memory network (BiLSTM) is used to extract semantic crrelation information of context to obtain high-level feature expression at the sentence level, then feature weighting is performed through self-attention mechanism to reduce the influence of noise features. Finally, classification is performed through the output layer. In addition, the more smooth and adaptive Mish function is used instead of traditional activation function to enrich local feature representation in AlexNet, BiLSTM, and self-attention layers. Relevant experiments were conducted based on LTP and THUC- News datasets. The experimental results show that the model achieves good performance and can effectively improve the effect of question classification.

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Research on big data processing framework algorithm optimized by deep learning

Research on big data processing framework algorithm optimize...

引用

Electrical, Automation and Computer Engineering (ICEACE), International conference on

作者： Shuo Ma Business School Shandong University of Political Science and Law Jinan China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper proposes a deep learning Optimization big data processing framework based on Hadoop distributed architecture, Convolutional Neural network (CNN) and Whale Optimization Algorithm (WOA). First, the Hadoop distributed file system (HDFS) and MapReduce technologies are used to store and preprocess large-scale data in parallel, and reduce data redundancy and processing delay through efficient data distribution and cleaning. Secondly, as a feature extraction module, the CNN model extracts the spatial features of the data layer by layer by using multi-layer convolution and pooling operations to provide reliable feature representation for subsequent classification and prediction tasks. In order to further improve the performance of the model, this paper introduces whale optimization algorithm (WOA) to optimize the hyperparameters of the CNN model (such as learning rate, convolution kernel size, etc.). By simulating the hunting behavior of whales, WOA automatically adjusts the combination of hyperparameters during model training, thus significantly improving the accuracy and training efficiency of the model. The experimental results show that the proposed Hadoop-CNN-WOA framework has higher accuracy than the traditional single model in processing large-scale image classification tasks.

关键词： Training Deep learning Convolution Computational modeling distributed databases Big Data Feature extraction Whale optimization algorithms Classification algorithms Convolutional neural networks

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Research on Intelligent Scheduling Algorithm based on Cloud Computing

Research on Intelligent Scheduling Algorithm Based on Cloud ...

引用

International conference on Computer Research and Development (ICCRD)

作者： Xiaofeng Yan Jie Zhao Jianchao Han Chongqing Medical and Pharmaceutical College Chongqing China

With the rapid development of the information age, computer technology and network technology are more and more mature, and the application of Internet technology is more and more extensive. On the basis of the gradual development and maturity of distributed computing, parallel computing and grid computing, cloud computing based on big data came into being. The core idea of cloud computing is to connect a large number of computing resources, storage resources and service resources through the network to form a resource pool, and then conduct unified scheduling and management of resources according to user needs. This paper mainly studies the resource scheduling method based on neural network and genetic algorithm to dynamically provide data centric computing service system for users, and maximize the reliability, customization and security of the services provided.

关键词： Cloud computing Scheduling algorithms Heuristic algorithms Neural networks parallel processing Information age Grid computing

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A Moving Target Defense Approach for the distributed Dynamic network

A Moving Target Defense Approach for the Distributed Dynamic...

引用

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

作者： Lin Zhang Yunchuan Guo Siyuan Leng Zifu Li Fenghua Li Liang Fang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed network Address Shuffling (DNAS), is proposed to thwart attackers’ network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected Neural network (FCNN) to recognize the attacker’s scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning.

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WDMoE: Wireless distributed Large Language Models with Mixture of Experts

WDMoE: Wireless Distributed Large Language Models with Mixtu...

引用

IEEE conference on Global Communications (GLOBECOM)

作者： Nan Xue Yaping Sun Zhiyong Chen Meixia Tao Xiaodong Xu Liang Qian Shuguang Cui Ping Zhang Cooperative Medianet Innovation Center Shanghai Jiao Tong University China Department of Broadband Communication Peng Cheng Laboratory Shenzhen China Future Network of Intelligent Institute (FNii) the Chinese University of Hong Kong (Shenzhen) Shenzhen China Beijing University of Posts and Telecommunications Beijing China School of Science and Engineering (SSE) and the Future Network of Intelligent Institute (FNii) the Chinese University of Hong Kong (Shenzhen) Shenzhen China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but how wireless networks can support LLMs has not been extensively studied. In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, through server-device collaboration at the wireless network edge. Specifically, we decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at the edge server of the base station (BS), while distributing the expert networks across the mobile devices. This arrangement leverages the parallel capabilities of expert networks on distributed devices. Moreover, to overcome the instability of wireless communications, we design an expert selection policy by taking into account both the performance of the model and the end-to-end latency, which includes both transmission delay and inference delay. Evaluations conducted across various LLMs and multiple datasets demonstrate that WDMoE not only outperforms existing models, such as Llama 2 with 70 billion parameters, but also significantly reduces end-to-end latency.

关键词： Performance evaluation Large language models Wireless networks Neural networks Benchmark testing Mobile handsets Natural language processing Delays Servers Global communication

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distributed Out-of-Memory SVD on CPU/GPU Architectures

Distributed Out-of-Memory SVD on CPU/GPU Architectures

引用

IEEE High Performance Extreme Computing Virtual conference (HPEC)

作者： Boureima, Ismael Bhattarai, Manish Eren, Maksim E. Solovyev, Nick Djidjev, Hristo Alexandrov, Boian S. LANL Div Theoret Los Alamos NM 87545 USA IICT Sofia Bulgaria

ISBN: (数字)9781665497862

ISBN: (纸本)9781665497862

We propose an efficient, distributed, out-of-memory implementation of the truncated singular value decomposition (t-SVD) for heterogeneous high performance computing (HPC) systems. Various implementations of SVD have been proposed, with most only estimate the singular values as the estimation of the singular vectors can significantly increase the time and memory complexity of the algorithm. In this work, we propose an implementation of SVD based on the power method, which is a truncated singular values and singular vectors estimation method. Memory utilization bottlenecks in the power method used to decompose a matrix A are typically associated with the computation of the Gram matrix A(T) A, which can be significant when A is large and dense, or when A is super-large and sparse. The proposed implementation is optimized for out-of-memory problems where the memory required to factorize a given matrix is greater than the available GPU memory. We reduce the memory complexity of A(T) A by using a batching strategy where the intermediate factors are computed block by block, and we hide I/O latency associated with both host-to-device (H2D) and device-to-host (D2H) batch copies by overlapping each batch copy with compute using CUDA streams. Furthermore, we use optimized NCCL based communicators to reduce the latency associated with collective communications (both intra-node and inter-node). In addition, sparse and dense matrix multiplications are significantly accelerated with GPU cores (or tensors cores when available), resulting in an implementation with good scaling. We demonstrate the scalability of our distributed out of core SVD algorithm to successfully decompose dense matrix of size 1TB and sparse matrix of 1e-6 sparsity with size of 128 PB in dense format.

关键词： SVD out-of-memory latent features data compression distributed processing parallel programming big data heterogeneous HPC systems GPU CUDA NCCL cupy

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Research on power equipment diagnosis based on big data 2

Research on power equipment diagnosis based on big data

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2nd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Wang, Zichen Tianjin University of Technology School of Management Tianjin China

ISBN: (纸本)9781665442763

The continuous development of power system is an important guarantee for the rapid development of economy and science and technology in China. The emergence of smart grid has accelerated the development of power big data and artificial intelligence in power field. In smart grid, the state of power equipment determines the state of power system and is the decisive factor for the stable operation of power grid. spark distributed parallel processing system can meet the storage and calculation of big data, and provides a new research idea for realizing fault classification and diagnosis of power equipment under big data. The concept of deep learning provides a platform for the analysis and prediction of power equipment status under big data. This paper introduces the basic contents of power equipment handover test, analyzes the common fault types of power equipment in handover test, introduces the existing fault diagnosis methods, puts forward the scheme of power equipment state diagnosis-based on spark platform, and classifies the faults of power equipment by using the naive Bayesian network in spark, Taking the test data as sample input, the fault diagnosis of power equipment can be carried out. On this basis, through the research of deep learning network, the feasibility of power equipment fault diagnosis is analyzed. The test shows that the method used in this paper can diagnose and predict the state of power equipment effectively. © 2022 IEEE.

关键词： Big data

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