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

共 24647 条记录，以下是701-710 订阅

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A Review of Implementing Exploring Mobile Edge computing within the Framework of 5G and 6G Networks

A Review of Implementing Exploring Mobile Edge Computing wit...

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2023 international conference on advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Batra, Raman Kumar, Sanjeev Bargavi, Manju Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The integration of edge computing is critical to the development of Beyond 5G (B5G) and 6G networks, as the volume of data and processing demands continue to rise. Using the radio access network (RAN), mobile edge computing (MEC) is essential for putting advanced cloud computing features in close proximity to end users. Even though MEC and its possible applications have been thoroughly studied, there hasn't been much conversation about the crucial elements required for MEC to be deployed successfully in order to satisfy various application demands. The important considerations that must be made while establishing edge infrastructure in B5G and 6G networks are outlined in this article. These factors include things like user density, server location, capacity, edge architecture, and security protocols. Moreover, it includes concrete assessments of edge computing-dependent applications, such forecasting and video conversion using deep learning. Additionally, the study provides insightful viewpoints that clarify open research questions related to edge computing within the framework of 6G networks. © 2023 IEEE.

关键词： and Cloud computing distributed computing Edge processing Next-generation 6G communication networks

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Empirical Study of Molecular Dynamics Workflow Data Movement: DYAD vs. Traditional I/O Systems

Empirical Study of Molecular Dynamics Workflow Data Movement...

引用

1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Lumsden, Ian Devarajan, Hariharan Marquez, Jack Brink, Stephanie Boehme, David Pearce, Olga Yeom, Jae-Seung Taufer, Michela Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9798350364613;9798350364606

This experimental work examines data movement in molecular dynamics (MD) workflows, comparing the Dynamic and Asynchronous Data Streamliner (DYAD) middleware with traditional, industry-standard I/O systems such as XFS and Lustre. DYAD moves MD simulation frames to analytics processes, providing enhanced flexibility and efficiency for dynamic data transfers and in situ analytics. At the same time, traditional I/O storage systems provide durability and scalability for high-performance computing (HPC) systems. The study integrates MD workflows with common simulation codes, facilitating immediate capture and transfer of MD frames to a staging area. It explores various molecular models, from simple to complex, assessing data management performance and scalability. Different producer-consumer pairs, molecular models, and data transaction frequency enable testing across small to large-scale HPC scenarios, from single-node configurations to large, distributed environments. The findings reveal that adaptive mechanisms for minimizing synchronization, direct network communication between producer and consumer processes, and optimizations of both data movement and synchronization are crucial for performance and scalability in MD workflows.

关键词： Producer-Consumer Paradigm Molecular Dynamics Workflows Dynamic Data Management High-Performance computing In Situ Analytics

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Towards a Privacy-Aware Electric Vehicle Architecture 30

Towards a Privacy-Aware Electric Vehicle Architecture

引用

30th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Plappert, Christian Stancke, Jonathan Jaeger, Lukas Fraunhofer Inst Secure Informat Technol Darmstadt Germany

ISBN: (纸本)9781665469586

Connected vehicles need to generate, store, process, and exchange a multitude of information with their environment. Much of this information is privacy-critical and thus regulated by privacy laws like the GDPR ha Europe. In this paper, we analyze and rate exemplary data (flows) of the electric driving domain with regard to their criticality based on a reference architecture. We classify the corresponding ECUs based on their processed privacy-critical data and propose technical mitigation measures and technologies in form of generic privacy-enhancing building blocks according to the classification and requirements derived from the GDPR.

关键词： automotive cybersecurity and privacy threat mitigation electric vehicles connected car General Data Protection Regulation (GDPR) trusted computing Trusted Platform Module (TPM) Device Identifier Composition Engine (DICE) Hardware Security Module (HSM) ISO 15118

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DistSim: A performance model of large-scale hybrid distributed DNN training 23

DistSim: A performance model of large-scale hybrid distribut...

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20th ACM international conference on computing Frontiers (CF)

作者： Lu, Guandong Chen, Runzhe Wang, Yakai Zhou, Yangjie Zhang, Rui Hu, Zheng Miao, Yanming Cai, Zhifang Li, Li Leng, Jingwen Guo, Minyi Shanghai Jiao Tong Univ Shanghai Qi Zhi Inst Shanghai Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798400701405

With the ever-increasing computational demand of DNN training workloads, distributed training has been widely adopted. A combination of data, model and pipeline parallelism strategy, called hybrid parallelism distributed training, is imported to tackle the problem of deploying large-scale models. However, how to evaluate the hybrid strategy and the utilization of each device remains a challenge since existing works either profile on a real large-scale cluster with high time and money costs or only analyze a specific type of parallelism without considering the hybrid parallelism. In this work, we proposed DistSim, an event-based performance model to accurately analyze each device's computation and communication activities with low profiling costs. DistDim breaks down the model into events according to the given distributed strategy, which can be profiled on two nodes. Then DistSim leverages the hierarchy of different parallel strategies to generate the computation and communication event-flow from layer level to model level and finally the activity timeline of each device participating in training. Experiment shows that DistSim can reach <4% errors when predicting distributing training batch time and <5% errors when predicting a single device's activity time in various hybrid strategy settings. We also provide a use-case of DistSim, automatically evaluate and search the best distributed training strategy, and find a hybrid strategy with at most 7.37x throughput improvement.

关键词： distributed DNN training performance model

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819708000

The proceedings contain 165 papers. The special focus in this conference is on Algorithms and Architectures for parallel Processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite Network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge computing;distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated computing;multi-agent Cooperative Intrusion Detection Based on Generative Data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient Data Traceability;research on the Evolution Path of Network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge computing Networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID Data;MPQUIC Transmission Control Strategy for SDN-Based Satellite Network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution Neural Network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for Data Center Network;Optimizing GNN Inference Processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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PrometheusMigrate: Efficient Live Migration of Confidential Virtual Machine with Software Abstraction 15

PrometheusMigrate: Efficient Live Migration of Confidential ...

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15th IEEE international conference on Joint Cloud computing (JCC)

作者： Ji, Chenhui Li, Dingji Mi, Zeyu Xia, Yubin Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst SEIEE Shanghai Peoples R China Minist Educ Engn Res Ctr Domain Specif Operating Syst Beijing Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350387339

With the rise of cloud computing, the use of virtual machines in data centers has become genuinely common. To achieve load balance and disaster recovery between different hosts when a virtual machine is providing services, the concept of live migration has been proposed and has now become one of the essential capabilities in data centers. On the other hand, users' concerns about data privacy when applications are deployed on cloud vendors' servers are also rising, and for this reason, confidential virtual machines, a new type of virtualization hardware, have been proposed. However, since the memory of confidential VMs is usually encrypted, existing live migration solutions cannot be applied to confidential VMs directly, so hardware vendors have come up with various solutions to support this critical feature. For example, the mainstream confidential virtualization hardware platform - AMD-SEV, adds a series of interfaces in their secure firmware to assist the hypervisor to achieve live migration capability. However, this solution can cause the live migration time to be too long due to the limited number of secure processors and computing power. In this paper, we propose a new software design: PROMETHEUSMIGRATE. PROMETHEUSMIGRATE presents a novel software abstraction that bypasses SEV's secure firmware with guaranteed security, thereby dramatically accelerating the total live migration time as well as downtime of the AMD-SEV platform.

关键词： confidential virtual machine live migration AMD SEV

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819708079

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Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams 25th

Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams

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25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

Graphics Processing Units (GPUs) are widely used as powerful hardware accelerators for data-intensive tasks. However, their efficacy can be hindered by constraints in device memory and data transfer speeds via the PCI-E link. This paper explores the use of GPUs for executing scalable sorting operations that exceeds the GPU’s global memory by a factor of at least two orders of magnitude. To address this challenge, we leverage the power of NVIDIA’s asynchronous CUDA streams. Our solution enhances GPU performance by implementing a pipeline model to offload sorting operations from the Solid-State Drive (SSD) to GPU memory via the host’s main memory. This strategy maximizes the multiway concurrency level to reduce the overhead associated with data transfer within the GPU memory hierarchy. We perform extensive performance evaluations by sorting 128 billion data elements initially stored on an external NVMe SSD. The results confirm the effectiveness of our approach, showcasing a speed-up of 4.3× compared to the results for CPU-based out-of-memory sorting of skewed data based on Intel’s Threading Building Blocks (TBB) sorting algorithm. Our results include the cost of transfer times to and from the main memory, a factor often overlooked in similar studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graphics processing unit

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An Efficient parallel Adaptive GMG Solver for Large-Scale Stokes Problems 29th

An Efficient Parallel Adaptive GMG Solver for Large-Scale St...

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

作者： Saberi, S. Meschke, G. Vogel, A. Ruhr Univ Bochum High Performance Comp Univ Str 150 D-44801 Bochum Germany Ruhr Univ Bochum Inst Struct Mech Univ Str 150 D-44801 Bochum Germany

ISBN: (纸本)9783031396977;9783031396984

We study the performance and scalability of the adaptive geometric multigrid method with the recently developed restricted additive Vanka (RAV) smoother for the finite element solution of large-scale Stokes problems on distributed-memory clusters. A comparison of the RAV smoother and the classical multiplicative and additive Vanka smoothers is presented. We present three cache policies for the smoother operators that provide a balance between cached and on-the-fly computation and discuss their memory footprint and computational cost. It is shown that the restricted additive smoother with the most efficient cache policy has the smallest memory footprint and is computationally cheaper in comparison with the other smoothers and can, therefore, be used for large-scale problems even when the available main memory is constrained. We discuss the parallelization aspects of the smoother operators and show that the RAV operator can be replicated exactly in parallel with a very small communication overhead. We present strong and weak scaling of the GMG solver for 2D and 3D examples with up to roughly 540 million degrees of freedom on up to 2048 MPI processes. The GMG solver with the restricted additive smoother is shown to achieve rapid convergence rates and scale well in both the strong and weak scaling studies, making it an attractive choice for the solution of large-scale Stokes problems on HPC systems.

关键词： Multigrid Stokes flow Finite element method Massively parallel

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819708109

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