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

共 34221 条记录，以下是131-140 订阅

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HPC master design: experience from Pisa 33

HPC master design: experience from Pisa

引用

33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Danelutto, Marco Univ. of Pisa Dept. of Computer Science Italy

ISBN: (纸本)9798331524937

HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware resources require application of HPC techniques when using much smaller machines. Indeed, proper parallel programming tools and applications are needed also to exploit parallel hardware resources in personal computers (laptops, desktops, servers). This paper outlines key challenges in designing master's degree programs in HPC and shares lessons learned from various experiences in developing and implementing such programs in Italy and Europe. © 2025 IEEE.

关键词： parallel programming

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distributed parallelized Password Cracking Framework for Enhanced Cybersecurity using Authentication Codes 3

Distributed Parallelized Password Cracking Framework for Enh...

引用

international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Balasubramaniam, N. Sweetlin, Dhalia J. Anirudh, R. Anna Univ Dept Informat Technol Chennai India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

An innovative approach to password cracking by leveraging a distributed computing model is developed. The sys- tem comprises a client-server architecture where clients receive segmented password ranges for parallelized hashing attempts. The Java-based implementation employs MD5 hashing and divides the password into smaller units for distribution among multiple clients. Each client independently processes its assigned password range, attempting to match the hash against a pre- determined target. The server orchestrates the distribution of password segments and collects results from clients, facilitating the cracking process. Security measures include secure communication protocols, and ethical considerations center around legality, user consent, and emphasizing the educational value of responsible hacking practices. The work explores the tech- nical challenges of distributed password cracking, addressing efficiency, scalability, and security implications, while fostering a deeper understanding of cybersecurity and distributed systems.

关键词： MD5 SHA256 password cracking distributed systems parallel computing

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distributed Large Memory and Message Passing Interface in Cloud Environment using OpenStack Cloud Platform 15

Distributed Large Memory and Message Passing Interface in Cl...

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deshmukh, Pranjali P. Prof. Ram Meghe Institute of Technology & Research Badnera Amravati India

ISBN: (纸本)9798331300579

distributed large memory offers the use of large virtual memory by using remote memory distributed over nodes in a cluster. The message passing interface (MPI) plays important role in DLM. MPI-based DLM manages the large virtual memory over the cluster using MPI batch queuing system. In distributed memory system message MPI is important parallel programming. As the number of virtual machines increases MPI and shared memory parallel programming combined to perform efficient communication in virtual machines [1,2]. Various tasks communicate with each other by sharing memory on the different disk through shared memory distributed computing. If various tasks are trying to access same the same memory location for reading and writing purpose, sometimes it may cause deadlock. In this case synchronization mechanism should handle such situation to keep the integrity of the data [2]. MapReduce and Hadoop are having inbuilt functions to handle distributed shared memory mechanism. The distributed computing with MPI can process thousands of parallel processes in cloud environment. © Grenze Scientific Society, 2024.

关键词： Cloud platforms

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Research and Application of Low Voltage distributed Power Supply Control System 1

引用

7th international conference on Cognitive computing (ICCC) Held as Part of the Services conference Federation Meeting (SCF)

作者： She, Shida Yu, Tongwei Ge, Junxiong Hong, Haimin Yan, Zhenhong Wang, Tong Zhang, Wuyang Shi, Mingfeng State Grid Liaoning Elect Power Co Ltd Elect Power Res Inst Shenyang 110006 Peoples R China China Gridcom Co Ltd Shenzhen 518109 Peoples R China

ISBN: (数字)9783031518263

ISBN: (纸本)9783031518256;9783031518263

With the launch of the National Energy Administration's rooftop photovoltaic pilot construction in all counties and cities, power companies in various provinces and cities of the State grid are facing severe safety management challenges. For example, distributed power sources such as photovoltaic and wind power are affected by natural weather, and their output is intermittent and difficult to control. Large-scale integration into the power system will have an impact on the power system, making it unable to operate normally, and even leading to power system collapse and damage to power ***, it is necessary to form a low-cost solution that meets the basic requirements of stable and safe operation of the power grid. In this paper, we propose a regulation scheme of low-voltage distributed generation, which uses the edge computing capability deployed in the distribution transformer fusion terminal, and the communication capability of power line carrier and the intelligent circuit breaker integrated with function fusion to realize the observation and control of distributed energy and electrical equipment. The proposed scheme has been piloted and verified in two typical power distribution areas in Shenyang, and the pilot application has achieved the expected goals.

关键词： Photovoltaic distributed Generation Regulation Power Line Carrier Fusion Terminal Intelligent Circuit Breaker

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Inference method for power large-scale model operation based on distributed computing resource scheduling

Inference method for power large-scale model operation based...

引用

2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Huang, Xiaoguang Qin, Yu Liu, Yaqing Cui, Yingbao Guo, Ting Gu, Qingtong State Grid Information and Communication Industry Group Co. Ltd Beijing102211 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Electric power computing resources are distributed in the respective regions of provincial data centers and transmission and transformation equipment, and the scheduling efficiency of computing resources is limited by conditions such as long distribution locations. Composite data model inference has been widely used in power equipment maintenance analysis, and large-scale model inference requires the use of distributed computing resources. In order to improve the scheduling and fusion efficiency of distributed computing power resources, this paper adopts a lightweight language model to infer and calculate the scheduling parameters of computing power resources in different regions. At the same time, in order to make more rational use of computing power resources, the intelligent models and algorithms used for power equipment maintenance are optimized. Configure the adaptation parameters of the model according to different application scenarios, and adjust the fusion method of input data for the fault diagnosis model and the classification strategy for the fault prediction model separately. Using a fine-tuning language model to map the predicted results to the semantics of power business, and then enhancing the rationality and interoperability of the answer results based on historical knowledge memory, ultimately pushing the reasoning results of the reasons and the classification results of the equipment defect diagnosis classifier to the equipment diagnosis designer. © 2025 SPIE.

关键词： Data modeling distributed computing Education and training Video Machine learning Semantics Video coding Instrument modeling Power grids Target detection

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Accelerating distributed ML Training via Selective Synchronization 25

Accelerating Distributed ML Training via Selective Synchroni...

引用

25th IEEE international conference on Cluster computing (CLUSTER)

作者： Tyagi, Sahil Swany, Martin Indiana Univ Bloomington Bloomington IN 47405 USA

ISBN: (纸本)9798350307924

In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not linearly scale-out due to high communication cost of aggregation. To mitigate this overhead, alternatives like Federated Averaging (FedAvg) and Stale-Synchronous parallel (SSP) either reduce synchronization frequency or eliminate it altogether, usually at the cost of lower final accuracy. In this paper, we present SelSync, a practical, low-overhead method for DNN training that dynamically chooses to incur or avoid communication at each step either by calling the aggregation op or applying local updates based on their significance. We propose various optimizations as part of SelSync to improve convergence in the context of semi-synchronous training. Our system converges to the same or better accuracy than BSP while reducing training time by up to 14x.

关键词： Cluster computing distributed systems Data-parallel training Federated learning Deep learning

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A Spark-based Task Allocation Solution for Machine Learning in the Edge-Cloud Continuum 20

A Spark-based Task Allocation Solution for Machine Learning ...

引用

20th Annual international conference on distributed computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Belcastro, Loris Marozzo, Fabrizio Presta, Aleandro Talia, Domenico Univ Calabria DIMES Arcavacata Di Rende Italy

ISBN: (纸本)9798350369458;9798350369441

The widespread utilization of Internet of Things (IoT) devices has resulted in an exponential increase in data at the Internet's edges. This trend, combined with the rapid growth of machine learning (ML) applications, necessitates the execution of learning tasks across the entire spectrum of computing resources - from the device, to the edge, to the cloud. This paper investigates the execution of machine learning algorithms within the edge-cloud continuum, focusing on their implications from a distributed computing perspective. We explore the integration of traditional ML algorithms, leveraging edge computing benefits such as low-latency processing and privacy preservation, along with cloud computing capabilities offering virtually limitless computational and storage resources. Our analysis offers insights into optimizing the execution of machine learning applications by decomposing them into smaller components and distributing these across processing nodes in edge-cloud architectures. By utilizing the Apache Spark framework, we define an efficient task allocation solution for distributing ML tasks across edge and cloud layers. Experiments on a clustering application in an edgecloud setup confirm the effectiveness of our solution compared to highly centralized alternatives, in which cloud resources are extensively used for handling large volumes of data from IoT devices.

关键词： Machine learning Internet of Things edge computing cloud computing edge-cloud continuum Apache Spark

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distributed Collaborative Control of Reconfigurable Battery Energy Storage Based on Consensus Algorithms 9

Distributed Collaborative Control of Reconfigurable Battery ...

引用

9th international conference on Power and Renewable Energy

作者： Wang, Manshang Xu, Yiran Guo, Jinkun Jiang, Libo Zheng, Tianwen Zhenjiang Power Supply Co State Grid Jiangsu Elect Power Co Ltd Zhenjiang Jiangsu Peoples R China Tsinghua Univ Sichuan Energy Internet Res Inst Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350377477;9798350377460

To enhance the high-reliability operation capability of reconfigurable battery energy storage systems, a distributed cooperative control method of reconfigurable battery energy storage based on consensus algorithms is proposed in this paper, which can improve the frequency and power characteristics of the system. Firstly, the mathematical model of virtual synchronous generator(VSG) control, the structure of reconfigurable battery energy storage system, and the parallel VSG distributed architecture are introduced;Secondly, combined with the mathematical model of VSG control, the overall scheme of frequency recovery and power output reasonable distribution of VSG parallel system is designed;Then, according to the structural characteristics of reconfigurable battery energy storage, a battery pack reconfiguration strategy is proposed, which can meet the power output demand through the free combination of battery packs;Finally, a simulation model is built in Matlab/Simulink environment, and the simulation results verify the correctness and effectiveness of the proposed method.

关键词： Reconfigurable battery energy storage Consensus algorithms Collaborative control Frequency recovery Power distribution

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Automated Calibration of parallel and distributed computing Simulators: A Case Study

Automated Calibration of Parallel and Distributed Computing ...

引用

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

作者： McDonald, Jesse Horzela, Maximilian Suter, Frederic Casanova, Henri Univ Hawaii Manoa Honolulu HI 96822 USA Karlsruhe Inst Technol Karlsruhe Germany Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9798350364613;9798350364606

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or "calibrated," based on ground truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable trade-offs between simulation accuracy and simulation speed.

关键词： Simulation of distributed computing platforms and applications Simulation accuracy and scalability Simulation calibration

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A Multi-label Support Vector Machine-based Transformer Fault Prediction Method 26

A Multi-label Support Vector Machine-based Transformer Fault...

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26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Winter 2023

作者： Shao, Zhimin Shao, Qi Wang, Yong Zhang, Junsan State Grid Shandong Electric Power Company State Grid Corporation of China Jinan China Qingdao China

ISBN: (纸本)9798350345865

Large power transformers typically use oil-immersed transformers, and the insulation oil can play a role in heat dissipation and insulation. By analyzing the gas composition in the oil, transformer failures can be predicted. Support vector machines are a data mining algorithm suitable for small sample data, but they are aimed at binary classification problems. However, there are multiple types of transformer failures. Therefore, a multi-label radial basis support vector machine is proposed for transformer failure prediction. The model analyzes gases with strong correlations among various gas content to construct a transformer failure prediction model. Machine learning algorithms can quickly mine potential rules in data, providing references for reasonable maintenance plans for transformers. © 2023 IEEE.

关键词： Support vector machines

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