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检索条件"主题词=Partitioning Algorithms"

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Counting Triangles of Graphs via Matrix partitioning

Counting Triangles of Graphs via Matrix Partitioning

引用

IEEE Workshop on Machine Learning for Signal Processing

作者： Georgios Kollias Vassilis Kalantzis Lior Horesh Shashanka Ubaru Panagiotis A. Traganitis IBM Research Thomas J. Watson Research Center Yorktown Heights NY USA Dept. of Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Counting the number of triangles is an important task in the computation of several network-related metrics such as transitivity ratio, link recommendation, near-clique subgraph detection, and clustering coefficient. This contribution presents a new algorithm based on non-overlapping subgraph decomposition and matrix partitioning. The part of the adjacency matrix associated with each submatrix is replaced by a low-rank approximation resulting to complexity savings. The proposed algorithm is also suitable for high-latency architectures as well as graphs whose entries are updated over time. Several practical and theoretical aspects are discussed while numerical experiments on real-world graphs demonstrate the potential of the proposed algorithm.

关键词： Measurement Machine learning algorithms Message passing Signal processing algorithms Clustering algorithms Signal processing Parallel processing Approximation algorithms partitioning algorithms Matrix decomposition

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Submodel partitioning in Hierarchical Federated Learning: Algorithm Design and Convergence Analysis

Submodel Partitioning in Hierarchical Federated Learning: Al...

引用

IEEE International Conference on Communications (ICC)

作者： Wenzhi Fang Dong-Jun Han Christopher G. Brinton Elmore Family School of Electrical and Computer Engineering Purdue University

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Hierarchical federated learning (HFL) has demon-strated promising scalability advantages over the traditional “star-topology” architecture-based federated learning (FL). How-ever, HFL still imposes significant computation, communication, and storage burdens on the edge, especially when training a large-scale model over resource-constrained Internet of Things (IoT) devices. In this paper, we propose hierarchical independent submodel training (HIST), a new FL methodology that aims to address these issues in hierarchical settings. The key idea behind HIST is a hierarchical version of model partitioning, where we partition the global model into disjoint submodels in each round, and distribute them across different cells, so that each cell is responsible for training only one partition of the full model. This enables each client to save computation/storage costs while alleviating the communication loads throughout the hierarchy. We characterize the convergence behavior of HIST for non-convex loss functions under mild assumptions, showing the impact of several attributes (e.g., number of cells, local and global aggregation frequency) on the performance-efficiency tradeoff. Finally, through numerical experiments, we verify that HIST is able to save communication costs by a wide margin while achieving the same target testing accuracy.

关键词： Training Costs Accuracy Federated learning Computational modeling partitioning algorithms Internet of Things

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Scaling Knowledge Graph Embedding with Parallel TransE and Graph partitioning

Scaling Knowledge Graph Embedding with Parallel TransE and G...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Khouloud Ammar Wissem Inoubli Sami Zghal Engelbert Mephu Nguifo University of Tunis El Manar Faculty of Sciences of Tunis LIPAH-LR11ES14 Tunis Tunisia Univ. Artois CNRS UMR 8188 CRIL Lens France Universite de Jendouba Jendouba Tunisia University Clermont Auvergne CNRS Clermont Auvergne INP LIMOS Clermont-Ferrand France

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Knowledge graph embedding has emerged as a fundamental technique to represent entities and relationships in knowledge graphs within low-dimensional vector spaces. Among these methods, translation-based approaches stand out by treating relations as translations from head entities to tail entities, achieving state-of-the-art results. However, the training process of these methods can be prohibitively time-consuming, especially for large knowledge graphs, posing significant challenges in practical applications. As knowledge graphs grow in size and complexity, surpassing the capacities of existing systems, there is an urgent need for scalable solutions in knowledge representation learning. These graphs, comprising millions of nodes and billions of edges, serve as powerful data structures for representing and understanding complex networks of knowledge. Translation-based models, particularly TransE, have been instrumental in encoding structured information about entities and their relationships in low-dimensional embedding spaces. However, the current implementation of TransE is constrained to single-node machines, limiting its scalability and applicability. To address these limitations, this paper proposes leveraging parallel computing technique, such as parallel TransE with graph partitioning, to enhance scalability and efficiency in knowledge graph embedding.

关键词： Training Systematics Scalability Computational modeling Knowledge graphs Tail Parallel processing Prediction algorithms Vectors partitioning algorithms

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Automatic Layout of One-Line Diagrams for Large Transmission Systems Based on Graph partitioning

Automatic Layout of One-Line Diagrams for Large Transmission...

引用

IEEE General Meeting Power& Energy Society

作者： Chen Yang Jingyu Wang Dongyuan Shi School of Electrical and Electronic Engineering Huazhong University of Science and Technology

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

One-line diagrams are widely used in power system applications, but their automatic layout remains a challenge. Currently, most approaches are dedicated to solving the layout of small-scale power transmission systems to produce aesthetically pleasing one-line diagrams. To extend them to large-scale systems, this paper proposes an integrated framework for the automatic layout of one-line diagrams based on graph partitioning. Graph partitioning allows a large graph to be divided into multiple subgraphs, and the layout for each subgraph is optimized using mixed-integer linear programming (MILP). These optimized subgraphs are then reassembled into a large graph using the subgraph assembly algorithm and the tie-line routing strategy. The case studies demonstrate that the proposed framework can efficiently generate one-line diagrams that satisfy aesthetic criteria. The output layout can preserve the relative position of nodes as in the original layout, which significantly improves its readability. Moreover, the time consumption of the proposed framework is acceptable for practical use.

关键词： Layout Software algorithms Power transmission Parallel processing Routing Software partitioning algorithms Mixed integer linear programming Assembly Optimization

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Adaptive DNN partitioning Method for Pipeline Parallel Training with Time-Varying Resources

Adaptive DNN Partitioning Method for Pipeline Parallel Train...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Shengang Deng Zhi Ling Mingrui Zhu Shuangwu Chen Xiaofeng Jiang Jian Yang University of Science and Technology of China Hefei China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Pipeline parallelism provides a flexible and scalable approach for training large deep neural networks (DNNs). To realize efficient pipeline parallel training, deploying model partitions that adapt to the resource configurations is crucial. However, due to the time-varying nature prevalent in real-world applications, model partitions can quickly become outdated, resulting in imbalanced workload distribution. Consequently, maintaining load-balanced model partitions under time-varying resource scenarios poses a challenge. In this paper, we propose an adaptive partitioning method that dynamically adjusts model partitions in response to variations in available resources, ensuring high training efficiency. First, we derive the execution duration model and redistribution cost model of pipeline training systems. Then, we perform a joint optimization of the two factors with the goal of minimizing the training time. Based on this, we implement a prototype system called ARPipe, in which a Fast partitioning Decision (FPD) algorithm is proposed to quickly determine optimized model partitions. Experimental evaluations show that under real-world circumstances with time-varying hardware resources, ARPipe can reduce training time by up to 30% in comparison to the state-of-the-art method GPipe when training the ResNet50 model on the CIFAR10 dataset.

关键词： Training Adaptation models Costs Computational modeling Pipelines Prototypes Parallel processing partitioning algorithms Optimization Load modeling

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Dynamic partitioning Technology of Regional Chain for Power Trading System

Dynamic Partitioning Technology of Regional Chain for Power ...

引用

IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Guanxia Shan Xiuli Chen Rongyan Cai Xiaorui Qian Kai Xiao Shuchen Pan State Grid Fujian Electric Power Company Fuzhou Fujian China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

In the power trading system, the traditional centralized management model faces problems such as security, transparency and efficiency. This paper further understands and explains the blockchain dynamic partitioning technology, which can improve the performance and security of the power trading system. First, this paper builds a blockchain architecture and implements smart contracts and transaction rule definitions on the Ethereum platform to automatically execute processes. Then, this paper designs a dynamic partitioning mechanism, analyzes the characteristics of transaction data, uses the K-means algorithm to partition transaction data according to frequency and node load, continuously monitors node status in real time, and dynamically adjusts the partitioning mechanism to balance the load. After that, an event-driven model is used to trigger adjustments every time a transaction occurs so that the system can respond quickly to changes. Experimental results show that by using this technology, the system can reach 624TPS, and the transaction confirmation time remains at a baseline level of 0.8 seconds. Blockchain dynamic partitioning technology brings innovative solutions to the power trading system and effectively addresses the challenges brought by the existing system.

关键词： Adaptation models Time-frequency analysis Heuristic algorithms Smart contracts Power markets Real-time systems Blockchains partitioning algorithms Security Monitoring

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Resilient Graph partitioning for Quantum Networks

Resilient Graph Partitioning for Quantum Networks

引用

IEEE Future Networks World Forum (FNWF)

作者： Abdelkrim Menina Hilal Sultan Duranoglu Tunc Ayse Nur Yilmaz Riccardo Bassoli Frank H.P. Fitzek Deutsche Telekom Chair of Communication Networks Technische Universität Dresden Dresden Germany Computer Engineering Department Hasan Kalyoncu University Gaziantep Türkiye

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

Quantum entanglement is important for quantum communications for long range transmission. Such long transmission is achieved through entanglement swapping, a method monopolizing considerable resources as the quantum network topology expands. Therefore, in this work we propose a method to reduce the resources required for entanglement swapping using network partitioning while ensuring resiliency. This method involves the fidelity of distributing entangled states that depends on the source, the link and the bell state analyzer utilized to quantify the required fidelity for a functioning network. By partitioning the network to redundant sub-graphs, we show the advantage of such approach in reducing the required functional fidelity.

关键词： Quantum entanglement Network topology Scalability LoRa Parallel processing Real-time systems partitioning algorithms Topology Quantum networks Resilience

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Multilayer Graph partitioning to Enable a Decentralized Path Planning for Large and Heterogeneous AGV Fleets

Multilayer Graph Partitioning to Enable a Decentralized Path...

引用

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Thomas Peitscher Dennis Lünsch Peter Detzner Department Robotics and Cognitive Systems Fraunhofer IML Germany Department AI and Autonomous Systems Fraunhofer IML Germany Distributed and Networked Systems Ruhr University Bochum Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In recent years, automated guided vehicles (AGVs) have become more and more important in production facilities. Especially the increasing number of AGVs in a fleet poses various challenges for centralized path planning approaches. To be prepared for future requirements and to overcome these challenges, one feasible approach is the decentralization of the path planning. For this purpose, decentralization is applied to a graph-based path planning by splitting the planning problem into multiple subgraphs, such that the path planning for AGVs can be performed in *** this paper we propose a solution that enables the splitting of graphs for heterogeneous AGV fleets into workload optimized domains. This contains a generic pipeline to prepare a multilayer graph to apply various partitioning algorithms. Additionally, this pipeline considers the balancing of routing requests and the spatial shapes of AGV fleets to optimize the domains for a decentralized path planning. Furthermore, we present a multilevel partitioning algorithm and an evaluation of workload optimized domain creation for heterogeneous AGV fleets.

关键词： Remotely guided vehicles Computer aided software engineering Shape Pipelines Nonhomogeneous media Routing Path planning Production facilities partitioning algorithms Planning

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Multi-Stage Dynamic partitioning Method for Receiving-End Grid Based on Voltage Steady and Transient State

Multi-Stage Dynamic Partitioning Method for Receiving-End Gr...

引用

IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Yan Li Ran Sun Jianbo Wang Hongzuo Guo Mingyang Liu Xingwei Chen Xianxue Sha Lixia Sun Henan Electric Power Company State Grid Corporation of China Zhengzhou China Henan Electric Power Research Institute State Grid Corporation of China Zhengzhou China School of Electrical and Power Engineering Hohai University Nanjing China

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

As the integration of high proportion renewable energy connecting the receiving-end power grid expands, the operational patterns and characteristics of power grid have fundamentally changed. Traditional steady state partitioning strategies are increasingly inadequate for modern new type power grid, thereby compromising their rationality and effectiveness. Therefore, this paper proposes a multi-stage dynamic partitioning approach for the receiving-end power grid that comprehensively considers steady-state and transient state, achieving reasonable and precise partitioning of power grid. The first stage comprises an initial categorization based on the sensitivity of reactive power to voltage during steady state periods. The process adheres to the geographical positioning and network topology constraints, ensuring that each segment incorporates adaptable reactive power generator. Then the transient voltage stability index feature is captured during different fault scenarios, which is employed for the second phase dynamic partitioning of power grid by using hierarchical clustering algorithms to further cluster on the basis of the initial partition. Finally, simulations of the improved IEEE 39-bus system are conducted to verify the rationality and effectiveness of the proposed method.

关键词： Reactive power Heuristic algorithms Power system dynamics Clustering algorithms Voltage Power grids Steady-state partitioning algorithms Indexes Transient analysis

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partitioning of Large-Scale Power Electronics-Based Power Systems for Small-Signal Stability Analysis

Partitioning of Large-Scale Power Electronics-Based Power Sy...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Nupur Yaosuo Xue Fred Wang Center for Ultra-Wide-Area Resilient Electric Energy Transmission Networks (CURENT) The University of Tennessee Knoxville Oak Ridge National Laboratory Oak Ridge Knoxville Tennessee USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The nodal admittance matrix (NAM)-based approach is suitable for analyzing the small-signal stability of large-scale power electronics-based power systems (PEPSs) as it preserves the system structure by utilizing the admittance matrix. Previously, NAM-based area partition has been proposed, which divides the system into various subareas and interconnections for easier analysis of the low-dimension matrix compared to the entire system-based high-dimension matrix. However, no partition algorithm has been presented for the NAM-based area partition method. This paper focuses on implementing the spectral partitioning algorithm for partitioning large-scale PEPSs into a low-dimension matrix to reduce the computation complexity of the analysis. These spectral components facilitate data transformation into a new space, enabling the application of traditional clustering methods like k-means. To evaluate the performance of the partitioning method, the subareas and interconnections obtained from the spectral clustering algorithm are incorporated into the NAM-based area partition method for a large system with 140 buses. The computational times of the original method, where the NAM-based criterion is directly applied to the entire system, are compared with those of the NAM-based partition method in MATLAB. PSCAD simulations of the whole system and the obtained subareas are conducted to validate the effectiveness of the proposed algorithm.

关键词： Computational modeling Stability criteria Clustering algorithms Energy conversion Power system stability partitioning algorithms Admittance Computational complexity PSCAD MATLAB

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