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

共 34068 条记录，以下是131-140 订阅

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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...

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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 ...

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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...

引用

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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Graph-Theoretic distributed Approach for Cyber Contingency Impact Assessment in Smart grids

Graph-Theoretic Distributed Approach for Cyber Contingency I...

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2024 IEEE international conference on Modeling, Simulation and Intelligent computing, MoSICom 2024

作者： Hareesh Kumar Reddy, M. Vignesh, V. Indian Institute of Technology Tirupati Electrical Engineering Tirupati India

ISBN: (纸本)9798331533311

The rapid integration of digital technologies into power grids has transformed traditional grids into smart grids, increasing interdependence between cyber and physical components. This interconnection introduces vulnerabilities, where cyber disturbances can significantly impact the operation and stability of the power system. Consequently, evaluating, ranking, and prioritizing cyber contingencies is crucial for maintaining grid reliability and resilience. A novel approach for quantifying cyber contingencies is presented, utilizing the distributed Cyber-Dependent Optimal Power Flow (CDOPF) problem. A graph-theoretic framework models the cyberphysical smart grid, eliminating the need for complex and computationally expensive cyber-physical co-simulations. Nonconvex constraints are convexified using a DC power flow relaxation, enabling more efficient solutions. The distributed algorithms are incorporated using RIAPS (Resilient Information Architecture Platform for Smart grid), an opensource platform designed to enhance the dependability, resilience, and performance of distributed systems within the smart grid. RIAPS provides a robust framework for decentralized control, fault tolerance, and real-time monitoring, which is crucial for ensuring the resilience of cyber-physical systems in dynamic, distributed environments. The platform supports scalable and efficient integration of distributed algorithms across the grid's infrastructure, enabling fast decision-making and enhanced grid security. The method is tested on the IEEE 39-bus system with varying partition sizes, demonstrating that the distributed approach outperforms centralized methods in scalability and accuracy. It effectively ranks cyber contingencies and measures their impact on overall grid stability, improving grid security and decision-making capabilities. © 2024 IEEE.

关键词： Quadratic programming

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Research on interconnection technology of heterogeneous platforms for privacy computing for energy big data 3

Research on interconnection technology of heterogeneous plat...

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3rd international conference on Energy and Power Engineering, Control Engineering (EPECE)

作者： Zhai, Yujia Huang, Xiuli Yu, Pengfei State Grid Smart Grid Res Inst Co Ltd State Grid Lab Power Cyber Secur Protect & Monito Nanjing Peoples R China

ISBN: (纸本)9798350366105;9798350366099

Energy data, especially power data, has the advantages of covering a wide range of industries, high value density, and good accuracy, relying on energy big data can widely describe all kinds of terminal production and life activities. However, the privacy computing platforms of various vendors often adopt different technical architectures and algorithm protocols in the early implementation process, which makes the implementation of various platforms have great differences, resulting in the direct interconnection between heterogeneous privacy computing platforms of various organizations. Aiming at the interoperability needs and problems of energy big data privacy computing platforms with different technical architectures, this paper decouple the management plane and data plane of the privacy computing platform, and in accordance with the principle of business priority, proposes a new privacy computing interconnection model of "unified business collaboration component + flexible configuration algorithm engine" suitable for energy big data. Through the design of the east-west and North-South interface framework of the privacy computing platform and the security access principle of the algorithm engine, the whole process control, cross-platform interconnection, and internal and external network penetration modeling and analysis of energy big data collaboration with the outside world are realized, which meets the key data security protection requirements of the State grid such as distributed computing and storage of data inside and outside the platform. It is of great significance to promote energy big data to empower external institutions such as governments, banks and operators.

关键词： Privacy computing energy big data cross-heterogeneous platforms connectivity technologies

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Asynchronous distributed-Memory parallel Algorithms for Influence Maximization 24

Asynchronous Distributed-Memory Parallel Algorithms for Infl...

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2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Singhal, Shubhendra Pal Hati, Souvadra Young, Jeffrey Sarkar, Vivek Hayashi, Akihiro Vuduc, Richard Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

Influence maximization (IM) is the problem of finding the k most influential nodes in a graph. We propose distributed-memory parallel algorithms for the two main kernels of a state-of-the-art implementation of one IM algorithm, influence maximization via martingales (IMM). The baseline relies on a bulk-synchronous parallel approach and uses replication to reduce communication and achieve approximate load balance, at the cost of synchronization and high memory requirements. By contrast, our method fully distributes the data, thereby improving memory scalability, and uses fine-grained asynchronous parallelism to improve network utilization and the cost of doing more communication. We show our design and implementation can achieve up to 29.6x speedup over the MPI-based state-of-the-art on synthetic and real-world network graphs. Moreover, ours is the first implementation that can run IMM to find influencers in the 'twitter' graph (41M nodes and 1.4B edges) in 200 seconds using 8K CPU cores of NERSC Perlmutter supercomputer.

关键词： Influence maximization FA-BSP PGAS IMM

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A Sparsity-Aware distributed-Memory Algorithm for Sparse-Sparse Matrix Multiplication 24

A Sparsity-Aware Distributed-Memory Algorithm for Sparse-Spa...

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2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Hong, Yuxi Buluc, Aydin Univ Calif Berkeley Appl Math & Computat Res Div Berkeley CA 94720 USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

Multiplying two sparse matrices (SpGEMM) is a common computational primitive used in many areas including graph algorithms, bioinformatics, algebraic multigrid solvers, and randomized sketching. distributed-memory parallel algorithms for SpGEMM have mainly focused on sparsity-oblivious approaches that use 2D and 3D partitioning. Sparsity-aware 1D algorithms can theoretically reduce communication by not fetching nonzeros of the sparse matrices that do not participate in the multiplication. Here, we present a distributed-memory 1D SpGEMM algorithm and implementation. It uses MPI RDMA operations to mitigate the cost of packing/unpacking submatrices for communication, and it uses a block fetching strategy to avoid excessive fine-grained messaging. Our results show that our 1D implementation outperforms state-of-the-art 2D and 3D implementations within CombBLAS for many configurations, inputs, and use cases, while remaining conceptually simpler.

关键词： parallel computing numerical linear algebra sparse matrix-matrix multiplication SpGEMM RDMA 1D algorithm sparsity-aware 1D SpGEMM algorithm 1D SpGEMM algorithm

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