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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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A Comprehensive Survey on Security of Single Source Cloud to distributed Enviroments Edge and Fog computing 1

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international conference on Power Engineering and Intelligent Systems, PEIS 2023

作者： Yadav, Dushyant Kumar Sahu, Hemlal Saradhi, Tejendra Dhanvinesh, K. Vishnubalaji, Loganatha Rana, Saurabh Chaudhary, Dharminder Department of Mathematics J.Y. Govt. College Raipur India Bennett University Noida India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9789819973835

ISBN: (纸本)9789819973828

The study of information security and privacy is currently quite popular. In parallel, several computing paradigms, such as cloud and edge computing, are already creating a unique ecosystem with various designs, storage possibilities, and processing capacities. The diversity of this ecosystem has several limitations, particularly in terms of security and privacy concerns. In the numerous paradigms discussed, this systematic literature review highlights overlaps, distinctions, direct assaults, and defences. The primary conclusion identifies the most critical security and privacy risks. The results highlight significant parallels and divergences between the cloud, edge, and fog computing models. The research also revealed that this ecosystem’s heterogeneity has problems and significantly hinders the adoption of security and privacy procedures from defending against security breaches and privacy leaks. The review investigations discovered many deployment strategies to improve safety and privacy flaws. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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Hierarchical Collaborative Resource Scheduling in Industrial Internet of Things based on Graph Neural Networks and Deep Reinforcement Learning 29

Hierarchical Collaborative Resource Scheduling in Industrial...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Meng, Qifeng Luo, Zihui Zheng, Xiaolong Liu, Liang Ma, Huadong Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9798350330717

The hierarchical cooperative resource scheduling architecture provides a promising direction for efficient collaborative processing of edge computing under the dynamic and intricate landscape of the Industrial Internet of Things (IIoT). However, existing scheduling algorithms often struggle to effectively capture the intricate information features inherent in hierarchical and collaborative domains, leading to suboptimal solutions. To tackle this challenge, we introduce a novel hierarchical cooperative resource scheduling framework based on Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL). We first leverage hierarchical GNN to facilitate seamless information exchange among internal nodes and adjacent nodes between layers in the hierarchical structure and transform it into node embeddings. These meticulously designed embeddings are then input into the policy model of DRL for the iterative learning process to generate higher-quality solutions by leveraging global feature information. Experiment results unequivocally demonstrate the superiority of our approach over baselines in terms of scheduling performance. Furthermore, our model exhibits robust generalization capabilities across various scenarios. © 2023 IEEE.

关键词： Deep Reinforcement Learning Edge computing Graph Neural Network Hierarchical Collaborative Industrial Internet of Things

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HSP: Hybrid Synchronous parallelism for Fast distributed Deep Learning 51

HSP: Hybrid Synchronous Parallelism for Fast Distributed Dee...

引用

51st international conference on parallel Processing (ICPP)

作者： Li, Yijun Huang, Jiawei Li, Zhaoyi Zhou, Shengwen Jiang, Wanchun Wang, Jianxin Cent South Univ Changsha Peoples R China

ISBN: (纸本)9781450397339

In the parameter-server-based distributed deep learning system, the workers simultaneously communicate with the parameter server to refine model parameters, easily resulting in severe network contention. To solve this problem, Asynchronous parallel (ASP) strategy enables each worker to update the parameter independently without synchronization. However, due to the inconsistency of parameters among workers, ASP experiences accuracy loss and slow convergence. In this paper, we propose Hybrid Synchronous parallelism (HSP), which mitigates the communication contention without excessive degradation of convergence speed. Specifically, the parameter server sequentially pulls gradients from workers to eliminate network congestion and synchronizes all up-to-date parameters after each iteration. Meanwhile, HSP cautiously lets idle workers to compute with out-of-date weights to maximize the utilizations of computing resources. We provide theoretical analysis of convergence efficiency and implement HSP on popular deep learning (DL) framework. The test results show that HSP improves the convergence speedup of three classical deep learning models by up to 67%.

关键词： distributed system deep learning parameter server

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distributed-Memory parallel JointNMF 23

Distributed-Memory Parallel JointNMF

引用

37th ACM international conference on Supercomputing (ACM ICS)

作者： Eswar, Srinivas Cobb, Benjamin Hayashi, Koby Kannan, Ramakrishnan Ballard, Grey Vuduc, Richard Park, Haesun Argonne Natl Lab Lemont IL 60439 USA Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA Oak Ridge Natl Lab Oak Ridge TN USA Wake Forest Univ Dept Comp Sci Winston Salem NC 27101 USA

ISBN: (纸本)9798400700569

Joint Nonnegative Matrix Factorization (JointNMF) is a hybrid method for mining information from datasets that contain both feature and connection information. We propose distributed-memory parallelizations of three algorithms for solving the JointNMF problem based on Alternating Nonnegative Least Squares, Projected Gradient Descent, and Projected Gauss-Newton. We extend well-known communication-avoiding algorithms using a single processor grid case to our coupled case on two processor grids. We demonstrate the scalability of the algorithms on up to 960 cores (40 nodes) with 60% parallel efficiency. The more sophisticated Alternating Nonnegative Least Squares (ANLS) and Gauss-Newton variants outperform the first-order gradient descent method in reducing the objective on large-scale problems. We perform a topic modelling task on a large corpus of academic papers that consists of over 37 million paper abstracts and nearly a billion citation relationships, demonstrating the utility and scalability of the methods.

关键词： High Performance computing Multimodal Inputs Nonnegative Matrix Factorization

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Enhancing Supercomputer Performance with Malleable Job Scheduling Strategies

Enhancing Supercomputer Performance with Malleable Job Sched...

引用

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

作者： Posner, Jonas Hupfeld, Fabian Finnerty, Patrick Univ Kassel Kassel Germany Kobe Univ Kobe Hyogo Japan

ISBN: (纸本)9783031488023;9783031488030

In recent years, supercomputers have experienced significant advancements in performance and have grown in size, now comprising several thousands nodes. To unlock the full potential of these machines, efficient resource management and job scheduling-assigning parallel programs to nodes-are crucial. Traditional job scheduling approaches employ rigid jobs that use the same set of resources throughout their lifetime, resulting in significant resource under-utilization. By employing malleable jobs that are capable of changing their number of resources during execution, the performance of supercomputers has potential to increase. However, designing algorithms for scheduling malleable jobs is challenging since it requires complex strategies to determine when and how to reassign resources among jobs while maintaining fairness. In this work, we extend a recently proposed malleable job scheduling algorithm by introducing new strategies. Specifically, we propose three priority orders to determine which malleable job to consider for resource reassignments and the number of nodes when starting a job. Additionally, we propose three reassignment approaches to handle the delay between scheduling decisions and the actual transfer of resources between jobs. This results in nine algorithm variants. We then evaluate the impact of deploying malleable jobs scheduled by our nine algorithm variants. For that, we simulate the scheduling of job sets containing varying proportions of rigid and malleable jobs on a hypothetical supercomputer. The results demonstrate significant improvements across several metrics. For instance, with 20% of malleable jobs, the overall completion time is reduced by 11% while maintaining high node utilization and fairness.

关键词： Malleability Job Scheduling High Performance computing

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Load Balancing and parallel Computation Model for Performance and Accuracy over the Cluster of Nodes 2

Load Balancing and Parallel Computation Model for Performanc...

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2nd international conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Vasuda, V. Srividya, K. Anusha, M. Vasavi College Of Engineering Hyderabad Telangana India

ISBN: (纸本)9781665466431

Cloud computing can be online based network engineering which contributed with a rapid advancement at the progress of communication technological innovation by supplying assistance to clients of assorted conditions with aid from online computing sources. It's terms of hardware and software apps together side software growth testing and platforms applications because tools. Large-scale heterogeneous distributed computing surroundings give the assurance of usage of a huge quantity of computing tools in a comparatively low price. As a way to lessen the software development and setup onto such complicated surroundings, high speed parallel programming languages exist which have to be encouraged by complex operating techniques. There are numerous advantages for consumers in terms of cost and flexibility that come with Cloud computing's anticipated uptake. Building on well-established research in Internet solutions, networks and utility computing, virtualization et cetera Service-Oriented Architectures and the Internet of Services (IoS) have implications for a wide range of technological issues such as parallel computing and load balancing as well as high availability and scalability. Effective load balancing methods are essential to solving these issues. Since such systems' size and complexity make it impossible to concentrate job execution on a few select servers, a parallel distributed solution is required. Adaptive task load model is the name of the method wesuggest in our article for balancing the workload (ATLM). We developed an adaptive parallel distributed computing paradigm as a result of this (ADPM). While still maintaining the model's integrity, ADPM employs a more flexible synchronization approach to cut down on the amount of time synchronous operations use. As well as the ATLM load balancing technique, which solves the straggler issue caused by the performance disparity between nodes, ADPM also applies it to ensure model correctness. The results indicate that

关键词： Application programs

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Leveraging Edge computing and Privacy-Enhanced Prediction Sharing for Urban Traffic Forecasting 29

Leveraging Edge Computing and Privacy-Enhanced Prediction Sh...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Yiping Ye, Chunyang Zhou, Hui Hainan University School of Comupter Science and Technology Haikou China

ISBN: (纸本)9798350330717

In today's urban transportation landscape, the accurate and timely prediction of traffic conditions holds immense significance for optimizing traffic flow and resource allocation. However, conventional centralized methods for traffic prediction come with the drawback of data collection from various roads, a process that can introduce latency and privacy concerns. To address these pressing challenges, our innovative approach seamlessly integrates road spatiotemporal data, edge computing, and privacy protection inspired by federated learning. We utilize Long Short-Term Memory (LSTM) networks to make real-time predictions of vehicle speeds on individual road segments. Furthermore, we introduce Graph Neural Networks (GNN) to effectively amalgamate predictive insights from the target road and its neighboring roads, effectively capturing intricate spatial relationships within traffic data. Our approach excels in reducing data requirements and alleviating computational burdens, enabling straightforward deployment on edge servers tailored to individual road segments. Most importantly, it prioritizes privacy by circumventing the exchange of raw data, instead opting for the selective sharing of predictions between segments. Our rigorous experimental results demonstrate that our approach outperforms traditional federated averaging algorithms, underscoring its remarkable security attributes while achieving superior predictive accuracy. © 2023 IEEE.

关键词： edge computing prediction sharing traffic forecasting

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Transformers, parallel Computation, and Logarithmic Depth 41

Transformers, Parallel Computation, and Logarithmic Depth

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41st international conference on Machine Learning, ICML 2024

作者： Sanford, Clayton Hsu, Daniel Telgarsky, Matus Department of Computer Science Columbia University New YorkNY United States Courant Institute New York University New YorkNY United States

We show that a constant number of self-attention layers can efficiently simulate—and be simulated by—a constant number of communication rounds of Massively parallel Computation, a popular model of distributed computing with wide-ranging algorithmic results. As a consequence, we show that logarithmic depth is sufficient for transformers to solve basic computational tasks that cannot be efficiently solved by several other neural sequence models and sub-quadratic transformer approximations. We thus establish parallelism as a key distinguishing property of transformers. Copyright 2024 by the author(s)

关键词： Distribution transformers

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Design and Evaluation of GPU-FPX: A Low-Overhead tool for Floating-Point Exception Detection in NVIDIA GPUs 23

Design and Evaluation of GPU-FPX: A Low-Overhead tool for Fl...

引用

32nd international Symposium on High-Performance parallel and distributed computing (HPDC) part of the ACM Federated computing Research conference (FCRC)

作者： Li, Xinyi Laguna, Ignacio Fang, Bo Swirydowicz, Katarzyna Li, Ang Gopalakrishnan, Ganesh Univ Utah Kahlert Sch Comp Salt Lake City UT 84112 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA Pacific Northwest Natl Lab Richland WA 99354 USA

ISBN: (纸本)9798400701559

Floating-point exceptions occurring during numerical computations can be a serious threat to the validity of the computed results if they are not caught and diagnosed. Unfortunately, on NVIDIA GPUs-today's most widely used type and which do not have hardware exception traps-this task must be carried out in software. Given the prevalence of closed-source kernels, efficient binary-level exception tracking is essential. It is also important to know how exceptions flow through the code, whether they alter the code's behavior and additionally whether these exceptions can be detected at the program outputs or are killed inside program flow-paths. In this paper, we introduce GPU-FPX, a tool that has low overhead, allows for deep understanding of the origin and flow of exceptions, and also how exceptions are modified by code optimizations. We measure GPU-FPX's performance over 151 widely used GPU programs, detecting 26 serious exceptions that were previously not reported. Our results show that GPU-FPX is 16x faster with respect to the geometric-mean runtime in relation to the only comparable prior tool, while also helping debug a larger class of codes more effectively. GPU-FPX and its benchmarks have been released.

关键词： Floating-point exceptions GPUs High-performance computing Machine Learning Binary Instrumentation Numerical Programs

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An Edge Resource Allocation Method based on Bundled Two-way Auction 29

An Edge Resource Allocation Method based on Bundled Two-way ...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Yan Yao, Hongze Gao, Jianwei Liu, Minghui Inner Mongolia University College of Computer Science Hohhot China

ISBN: (纸本)9798350330717

Mobile edge computing can effectively meets the resource requirements of complex vehicle applications with high computing power and low delay. When a vehicle terminal task is offloaded to the edge servers, most works focus on the demands of the vehicle terminal and the service quality of the offloaded task when selecting an optimal resource allocation schemes. But it also creates a new problem of the abuse of edge service resources. Aiming at the low utilization of edge resources in the Internet of vehicles, a resource allocation method based on the bundled two-way auction model is proposed in this paper. From the perspective of game, the edge resources are firstly bundled, and then the resource pricing, quotation and inquiry strategy and payment rules in the auction are analyzed. At the same time, the value gain of the vehicle application and the cost consumption of the edge resources in the auction are used as the targets to optimize the resource allocation scheme of edge service system. The experimental results show that the method proposed in this paper has higher resource utilization and faster convergence than the existing methods. © 2023 IEEE.

关键词： bundled mechanism Internet of vehicles mobile edge computing resource allocation two-way auction

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