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

共 34256 条记录，以下是4531-4540 订阅

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PV-STATCOM based smart inverter for reliable distrubition system 5

PV-STATCOM based smart inverter for reliable distrubition sy...

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5th international conference on Intelligent computing and Control Systems, ICICCS 2021

作者： Pattanaik, Balachandra Balachandran, Ruthramurthy Prakash, S. Haro, Reta Dengesu Bule Hora University Department of Electrical and Computer Engineering Bule hora Ethiopia School of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia BIHER Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9781665412728

The design technique based on the well-established concept of PV-STATCOM is implemented. The main task towards the achievement of more reliable increased integration of distributed energy resources on to the grid requires new pattern shift to improve the efficiency as well as overall performance of the system. The inverter which can act as smart inverter by controlling active and reactive power, frequency control and regulation of voltage and power factor apart from its typical function of DC to AC conversion could be a potential solution. PV inverter based reactive power control strategies. The control algorithm is based on balancing the power production from a solar PV depends directly on solar isolation. Hence the solar PV remains idle when there is no isolation. Solar PV along with its inverter resembles the functionality of STATCOM. The PV-STATCOM concept allows utilization of solar farm throughout the day. During daytime, real power generation takes place normally and switches to compensate dynamic reactive power needs on critical times. The dual functionality of the proposed system to regulate the reliable voltage at PCC and mitigate low voltage issues is demonstrated through MATLAB simulation to make reliable effective use of available resources Arduino controller is used to switch between the functions depending on the system requirement. © 2021 IEEE.

关键词： Static synchronous compensators

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A distributed Privacy-Preserving Integrity Verification Framework for the Smart grid

A Distributed Privacy-Preserving Integrity Verification Fram...

引用

IEEE conference on Technologies for Homeland Security

作者： Gaurav S. Wagh Sumita Mishra B. Thomas Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester USA

ISBN: (纸本)9781665494052

Smart grid functionalities, such as real-time monitoring and load balancing, require smart metering data collection at frequent time intervals. There are several threats to this data collection process, including passive threats to customers' privacy and active threats to metering data integrity. Customer privacy can be breached with the fine grained metering data collection, and without appropriate measures for integrity verification, the metering data can be exploited to hamper the smart grid functionalities. distributed privacy-preserving frameworks are more robust than centralized frameworks against privacy threats. Several distributed privacy-preserving frameworks for smart metering data exist in the literature. However, these frameworks assume a semi-honest threat model that does not consider threats to data integrity. This paper introduces a distributed framework under a malicious adversarial model. The proposed framework is capable of verifying metering data's integrity while maintaining customer privacy. We evaluate our framework's performance via simulation and show its feasibility for real-world deployments. We also evaluate the framework's resilience to active and passive attacks, followed by a comparative analysis with existing related frameworks in the literature.

关键词： Threat modeling Data privacy Privacy Data integrity distributed databases US Department of Homeland Security Data collection

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Composite Control Strategy of Output Current of LCL Photovoltaic grid-Connected Inverter

Composite Control Strategy of Output Current of LCL Photovol...

引用

international conference on Electronics, Electrical and Information Engineering 2021, ICEEIE 2021

作者： Li, Shengqing Li, Xinluo Wu, Qiang Long, Xiafei College of Electrical and Information Engineering Hunan University of Technology Hunan Zhuzhou China

In order to further optimize the output current harmonic suppression effect of photovoltaic grid-connected inverters, a composite control strategy of LCL type photovoltaic grid-connected inverter output current is proposed. This strategy combines proportional complex integral (PCI) control and repetitive control (RC) in parallel, draws a composite control block diagram, introduces a transfer function, and designs PCI and RC control parameters. Prove that the compound control can reduce current harmonics, achieved the purpose of reducing the steady-state error of the fundamental frequency. And adopts a new PCI composite control strategy, which helps to save the cost of the control system. By building the MATLAB/Simulink simulation platform and establishing the PCI+RC composite control model of LCL photovoltaic grid-connected inverter, the comparison of the simulation results shows that compared with the PI+RC control strategy, the total harmonic distortion rate of the grid-connected current is reduced by 25.77. %, significantly improving the quality of grid-connected current. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Simulation platform

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Multi-Population-based parallelization of Ensemble of Differential Evolution Variants for Constrained Real Parameter Optimization

Multi-Population-based Parallelization of Ensemble of Differ...

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international conference on Embedded & distributed Systems (EDiS)

作者： Leyla Belaiche Laid Kahloul Manel Houimli Selma Sahraoui Saber Benharzallah Maroua grid Nedjma Abidallah LINFI Laboratory University of Biskra Biskra Algeria University of Biskra Biskra Algeria LAMIE Laboratory University of Biskra Batna Algeria

ISBN: (数字)9798350397543

ISBN: (纸本)9798350397550

Differential evolution (DE) algorithms face performance challenges, which lean on improving solutions quality, speed-up, and exploitation of computational resources. parallelism represents a suitable paradigm for overcoming the DE challenges. The ensemble of differential evolution variants (EDEV) algorithm is a recent DE algorithm. EDEV constitutes three DE variants (JADE, CoDE, and EPSDE), which may decrease its speedup. In this paper, a multi-population parallel ensemble of differential evolution variants (MPPEDEV) is proposed based on the synchronous master/slave parallel model. The performance of the proposed MPPEDEV is tested using a constrained real parameter problem proposed in CEC 2006. Compared to four state-of-the-art DE algorithms, which are JADE, CoDE, EPSDE, and EDEV, the results show that MPPEDEV outperforms EDEV in terms of execution time and solutions quality, depending on the population size as a control parameter. Furthermore, MPPEDEV and EDEV outperform JADE, CoDE, and EPSDE in terms of solutions' quality.

关键词： Codes Sociology parallel processing Main-secondary Statistics Optimization Faces

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A Connectivity-Enhanced Multi-Task Learning based on Anatomical Priors for 3D Class-Balanced Pulmonary Airway Segmentation

A Connectivity-Enhanced Multi-Task Learning based on Anatomi...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Yan Jia Yong Dou Peng Qiao Yuqing Cheng Kele Xu Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate and efficient airway segmentation is essential for evaluating pulmonary diseases, aiding diagnosis, reducing the preoperative burden of airway identification, and minimizing patient discomfort during prolonged surgeries. However, current pulmonary airway reconstruction techniques are hindered by two major challenges: difficulty in accurately reconstructing fine airway branches due to the tendency to overlook small targets, and insufficient structural connectivity leading to frequent branch discontinuities within the airway tree. These limitations directly affect the clinical applicability of reconstructed airways. To overcome these challenges, a novel 3D pulmonary airway segmentation multi-task framework is proposed, designed to enhance the performance of existing backbone models. This approach integrates Anatomical Prior-Based Multi-Task Learning (AP-MTL) through the use of Gaussian-constructed connectivity-enhanced isosurfaces, significantly improving the network’s ability to maintain airway continuity. Additionally, a Class-Balanced CT Density Distribution Reconstruction mechanism (DDR-CB) is introduced, further refining the model’s capability to detect and segment fine airway branches. As a result of these enhancements, the model demonstrates a 11.5% average improvement in segmentation accuracy and connectivity compared to the baseline. The source code is publicly accessible at https://***/inexhaustible419/APMTLAirwaySegment.

关键词： Image segmentation Three-dimensional displays Accuracy Lungs Atmospheric modeling Supervised learning Surgery Multitasking Image reconstruction Biomedical imaging

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SCGraph: Accelerating Sample-based GNN Training by Staged Caching of Features on GPUs

SCGraph: Accelerating Sample-based GNN Training by Staged Ca...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Yuqi He Zhiquan Lai Zhejiang Ran Lizhi Zhang Dongsheng Li National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Graph neural networks (GNNs) have been becoming important tools for processing structured graph data and successfully applied to multiple graph-based application scenarios. The existing GNN systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead of transferring vertex features between CPUs and GPUs is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. SCGraph classifies the graph vertices sorted by out-degrees. For high out-degree vertices, SCGraph sets grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, SCGraph expands training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on various benchmarks. Experimental results show that SCGraph improves the cache hit rate over GPUs up to 23.6%, and achieves up to 1.71x performance speedup over the state-of-the-art baselines while the convergence almost constant.

关键词： Training Memory management Loading Graphics processing units Benchmark testing Graph neural networks Data communication

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OOOPS: An Innovative Tool for IO Workload Management on Supercomputers 26

OOOPS: An Innovative Tool for IO Workload Management on Supe...

引用

26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Huang, Lei Liu, Si Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA

ISBN: (纸本)9781728190747

Modern supercomputer applications are demanding high-performance storage resources in addition to fast computing resources. However, these storage resources, especially parallel shared filesystems, have become the Achilles' heel of many powerful supercomputers. Due to the lack of mechanism of IO resource provisioning on the file server side, a single user's IO intensive work running on a small number of nodes can overload the metadata server and result in global filesystem performance degradation and even unresponsiveness. To tackle this issue, we developed an innovative tool, Optimal Overloaded IO Protection System (OOOPS). This tool is designed to control the IO workload from applications side. Supercomputer administrators can easily assign the maximum number of function calls of open() and stat() allowed per second. OOOPS can automatically detect and throttle intensive IO workload to protect parallel shared filesystems. It also allows supercomputer administrators to dynamically adjust how much metadata throughput one job can utilize during the job runs without interruption.

关键词： Supercomputers Performance analysis IO monitoring parallel filesystem Metadata server

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A micro grid information architecture with open source components

A micro grid information architecture with open source compo...

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Andrew Cyrus Smith Buhle Mkhize Boitumelo Mahlobo Sherrin John Isaac Council for Scientific and Industrial Research Pretoria South Africa

ISBN: (纸本)9781665458719

Presented is an information system architecture based on open source platforms to support the successful management of a micro grid. The paper justifies the use of open source platforms in the micro grid context. Also given are the benefits of a micro grid as it relates to distributed energy generation and subsequently the need explained for an information system that keeps a record of all devices and their interconnections, while simultaneously providing access to instantaneous data along various points in the micro grid. Three examples of services made possible by the architecture are given.

关键词： Multi-access edge computing Operating systems Digital twin distributed databases Computer architecture Power grids Security

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AccDP: Accelerated Data-parallel distributed DNN Training for Modern GPU-Based HPC Clusters

AccDP: Accelerated Data-Parallel Distributed DNN Training fo...

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international conference on High Performance computing

作者： Nawras Alnaasan Arpan Jain Aamir Shafi Hari Subramoni Dhabaleswar K Panda Department of Computer Science and Engineering The Ohio State University Columbus Ohio USA

Deep Learning (DL) has become a prominent machine learning technique due to the availability of efficient computational resources in the form of Graphics Processing Units (GPUs), large-scale datasets and a variety of models. The newer generation of GPUs are being designed with special emphasis on optimizing performance for DL applications. Also, the availability of easy-to-use DL frameworks—like PyTorch and TensorFlow— has enhanced productivity of domain experts to work on their custom DL applications from diverse domains. However, existing Deep Neural Network (DNN) training approaches may not fully utilize the newly emerging powerful GPUs like the NVIDIA A100—this is the primary issue that we address in this paper. Our motivating analyses show that the GPU utilization on NVIDIA A100 can be as low as 43% using traditional DNN training approaches for small-to-medium DL models and input data size. This paper proposes AccDP—a data-parallel distributed DNN training approach—to accelerate GPU-based DL applications. AccDP exploits the Message Passing Interface (MPI) communication library coupled with the NVIDIA’s Multi-Process Service (MPS) to increase the amount of work assigned to parallel GPUs resulting in higher utilization of compute resources. We evaluate our proposed design on different small-to-medium DL models and input sizes on the state-of-the-art HPC clusters. By injecting more parallelism into DNN training using our approach, the evaluation shows up to 58% improvement in training performance on a single GPU and up to 62% on 16 GPUs compared to regular DNN training. Furthermore, we conduct an in-depth characterization to determine the impact of several DNN training factors and best practices—including the batch size and the number of data loading workers— to optimally utilize GPU devices. To the best of our knowledge, this is the first work that explores the use of MPS and MPI to maximize the utilization of GPUs in distributed DNN training.

关键词： Training Deep learning Productivity Performance evaluation Computational modeling Message passing Neural networks

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Hermes: Memory-Efficient Pipeline Inference for Large Models on Edge Devices

Hermes: Memory-Efficient Pipeline Inference for Large Models...

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IEEE international conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Xueyuan Han Zinuo Cai Yichu Zhang Chongxin Fan Junhan Liu Ruhui Ma Rajkumar Buyya ParisTech Elite Institute of Technology Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China Shanghai Aerospace System Engineering Institute Shanghai China Cloud Computing and Distributed Systems (CLOUDS) Laboratory The University of Melbourne Melbourne Australia

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

The application of Transformer-based large models has achieved numerous success in recent years. However, the exponential growth in the parameters of large models introduces formidable memory challenge for edge deployment. Prior works to address this challenge mainly focus on optimizing the model structure and adopting memory swapping methods. However, the former reduces the inference accuracy, and the latter raises the inference latency. This paper introduces PIPELoAD, a novel memory-efficient pipeline execution mechanism. It reduces memory usage by incorporating dynamic memory management and minimizes inference latency by employing parallel model loading. Based on PIPELoAD mechanism, we present Hermes, a framework optimized for large model inference on edge devices. We evaluate Hermes on Transformer-based models of different sizes. Our experiments illustrate that Hermes achieves up to 4.24 x increase in inference speed and 86.7% lower memory consumption than the state-of-the-art pipeline mechanism for BERT and ViT models, 2.58 x increase in inference speed and 90.3% lower memory consumption for GPT-style models.

关键词： Performance evaluation Accuracy Computational modeling Memory management Pipelines Loading Transformers Load modeling

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