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graph computing and Its Application in Power Grid Analysis

CSEE Journal of Power and Energy Systems 2022年第6期8卷 1550-1557页

作者： Mike Zhou Jianfeng Yan Qianhong Wu China Electric Power Research Institute Beijing 100192China

Approaches to apply graph computing to power grid analysis are systematically explained using real-world application *** exploring the nature of the power grid and the characteristics of power grid analysis,the guidelines for selecting appropriate graph computing techniques for the application to power grid analysis are outlined.A custom graph model for representing the power grid for the analysis and simulation purpose and an in-memory computing(IMC)based graph-centric approach with a shared-everything architecture are *** algorithms,including network topology processing and subgraph processing,and graph computing application scenarios,including in-memory computing,contingency analysis,and Common Information Model(CIM)model merge,are presented.

关键词： CIM model merge contingency analysis graph computing in-memory computing network topology processing power grid analysis subgraph processing

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Design of graph computing accelerator based on reconfigurable PE array

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The Journal of China Universities of Posts and Telecommunications 2024年第5期31卷 49-63,70页

作者： Deng Junyong Jia Yanting Zhang Baoxiang Kang Yuchun Lu Songtao School of Electronic Engineering Xi'an University of Posts and TelecommunicationsXi'an 710121China

Due to the diversity of graph computing applications, the power-law distribution of graph data, and the high compute-to-memory ratio, traditional architectures face significant challenges regarding poor flexibility, imbalanced workload distribution, and inefficient memory access when executing graph computing tasks. graph computing accelerator, graphApp, based on a reconfigurable processing element(PE) array was proposed to address the challenges above. graphApp utilizes 16 reconfigurable PEs for parallel computation and employs tiled data. By reasonably dividing the data into tiles, load balancing is achieved and the overall efficiency of parallel computation is enhanced. Additionally, it preprocesses graph data using the compressed sparse columns independently(CSCI) data compression format to alleviate the issue of low memory access efficiency caused by the high memory access-to-computation ratio. Lastly, graphApp is evaluated using triangle counting(TC) and depth-first search(DFS) algorithms. Performance analysis is conducted by measuring the execution time of these algorithms in graphApp against existing typical graph frameworks, Ligra, and graphBIG, using six datasets from the Stanford Network Analysis Project(SNAP) database. The results show that graphApp achieves a maximum performance improvement of 30.86% compared to Ligra and 20.43% compared to graphBIG when processing the same datasets.

关键词： graph computing reconfigurable accelerator parallel computing triangle counting(TC)algorithm depth-first search(DFS)algorithm

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AMBEA: Aggressive Maximal Biclique Enumeration in Large Bipartite graph computing

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IEEE TRANSACTIONS ON COMPUTERS 2024年第12期73卷 2664-2677页

作者： Pan, Zhe Li, Xu He, Shuibing Zhang, Xuechen Wang, Rui Gao, Yunjun Chen, Gang Sun, Xian-He Zhejiang Univ State Key Lab Blockchain & Data Secur Hangzhou 310027 Peoples R China Hangzhou High TechZone Binjiang Inst Blockchain & Hangzhou 310051 Peoples R China Washington State Univ Vancouver Sch Engn & Comp Sci Vancouver WA 98686 USA IIT Dept Comp Sci Chicago IL 60616 USA

Maximal biclique enumeration (MBE) in bipartite graphs is a fundamental problem in data mining with widespread applications. Many recent works solve this problem based on the set-enumeration (SE) tree, which sequentially traverses vertices to generate the enumeration tree nodes representing distinct bicliques, then checks whether these bicliques are maximal or not. However, existing MBE algorithms only expand bicliques with untraversed vertices to ensure distinction, which often necessitate extensive node checks to eliminate non-maximal bicliques, resulting in significant computational overhead during the enumeration process. To address this issue, we propose an aggressive set-enumeration (ASE) tree that aggressively expands all bicliques to their maximal form, thus avoiding costly node checks on non-maximal bicliques. This aggressive enumeration may produce multiple duplicate maximal bicliques, but we efficiently eliminate these duplicates by leveraging the connection between parent and child nodes and conducting low-cost node checking. Additionally, we introduce an aggressive merge-based pruning (AMP) approach that aggressively merges vertices sharing the same local neighbors. This helps prune numerous duplicate node generations caused by subsets of merged vertices. We integrate the AMP approach into the ASE tree, and present the Aggressive Maximal Biclique Enumeration Algorithm (AMBEA). Experimental results show that AMBEA is 1.15x to 5.32x faster than its closest competitor and exhibits better scalability and parallelization capabilities on larger bipartite graphs.

关键词： Bipartite graph Computers Scalability Electronic commerce Computer science Sun Data security graph computing bipartite graph maximal biclique enumeration

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Onegraph: a cross-architecture framework for large-scale graph computing on GPUs based on oneAPI

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CCF TRANSACTIONS ON HIGH PERFORMANCE computing 2024年第2期6卷 179-191页

作者： Li, Shiyang Zhu, Jingyu Han, Jiaxun Peng, Yuting Wang, Zhuoran Gong, Xiaoli Wang, Gang Zhang, Jin Wang, Xuqiang Nankai Univ Colleage Comp Sci Tianjin 300350 Peoples R China State Grid Tianjin Informat & Commun Co Tianjin Peoples R China

The explosive growth of graph data sets has led to an increase in the computing power and storage resources required for graph computing. To handle large-scale graph processing, heterogeneous platforms have become necessary to provide sufficient computing power and storage. The most popular scheme for this is the CPU-GPU architecture. However, the steep learning curve and complex concurrency control for heterogeneous platforms pose a challenge for developers. Additionally, GPUs from different vendors have varying software stacks, making cross-platform porting and verification challenging. Recently, Intel proposed a unified programming model to manage multiple heterogeneous devices at the same time, named oneAPI. It provides a more friendly programming model for simple C++ developers and a convenient concurrency control scheme, allowing managing different vendors of devices at the same time. Hence there is an opportunity to utilize oneAPI to design a general cross-architecture framework for large-scale graph computing. In this paper, we propose a large-scale graph computing framework for multiple types of accelerators with Intel oneAPI and we name it as Onegraph. Our approach significantly reduces the data transfer between GPU and CPU and masks the latency by asynchronous transfer, which significantly improves performance. We conducted rigorous performance tests on the framework using four classical graph algorithms. The experiment results show that our approach achieves an average speedup of 3.3x over the state-of-the-art partitioning-based approaches. Moreover, thanks to the cross-architecture model of Intel oneAPI, the framework can be deployed on different GPU platforms without code modification. And our evaluation proves that Onegraph has only less than 1% performance loss compared to the dedicated programming model on GPUs in large-scale graph computing.

关键词： Heterogeneous programming graph computing Out-of-memory process Cross-architecture portability OneAPI

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Liberator: A Data Reuse Framework for Out-of-Memory graph computing on GPUs

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第6期34卷 1954-1967页

作者： Li, Shiyang Tang, Ruiqi Zhu, Jingyu Zhao, Ziyi Gong, Xiaoli Wang, Wenwen Zhang, Jin Yew, Pen-Chung Nankai Univ Inst Syst & Networks Coll Comp Sci Tianjin 300071 Peoples R China Rice Univ Dept Comp Sci Houston TX 77005 USA Univ Georgia Dept Comp Sci Athens GA 30602 USA Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

graph analytics are widely used including recommender systems, scientific computing, and data mining. Meanwhile, GPU has become the major accelerator for such applications. However, the graph size increases rapidly and often exceeds the GPU memory, incurring severe performance degradation due to frequent data transfers between the main memory and GPUs. To relieve this problem, we focus on the utilization of data in GPUs by taking advantage of the data reuse across iterations. In our studies, we deeply analyze the memory access patterns of graph applications at different granularities. We have found that the memory footprint is accessed with a roughly sequential scan without a hotspot, which infers an extremely long reuse distance. Based on our observation, we propose a novel framework, called Liberator, to exploit the data reuse within GPU memory. In Liberator, GPU memory is reserved for the data potentially accessed across iterations to avoid excessive data transfer between the main memory and GPUs. For the data not existing in GPU memory, a Merged and Aligned memory access manner is employed to improve the transmission efficiency. We also further optimize the framework by parallel processing of data in GPU memory and data in the main memory. We have implemented a prototype of the Liberator framework and conducted a series of experiments on performance evaluation. The experimental results show that Liberator can significantly reduce the data transfer overhead, which achieves an average of 2.7x speedup over a state-of-the-art approach.

关键词： graphics processing units Memory management Data transfer Parallel processing Lead Computer science Sparse matrices Data reuse GPU memory oversubscription graph computing partition-based method zero-copy

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graph computing Applications In Unit Commitment Historical Data Management

Graph Computing Applications In Unit Commitment Historical D...

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2023 IEEE Sustainable Power and Energy Conference, iSPEC 2023

作者： Wang, Yishen Zhou, Fei Zhu, Hong Liu, Siyan Tang, Zhuang Yang, He State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (纸本)9798350322699

The development of new types of power systems poses several key challenges for super-large-scale online analysis and optimization. These challenges include data merging, computation performance, and operation optimization. The uncertainty arising from renewable energy sources and load fluctuations has resulted in highly volatile power flow patterns compared to previous systems. As a result, the operating states of the system are more vulnerable to potential operational risks during critical periods. From the perspective of unit commitment, optimizing system resources involves increasingly complex constraints and variables. graph computing has shown exceptional performance in online power system analysis, particularly in topology-related analysis. This paper explores the application of graph computing techniques to enhance security-constrained unit commitment historical data management. It offers new insights into the utilization of graph computing to improve unit commitment. © 2023 IEEE.

关键词： data management graph computing graph database unit commitment

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A graph computing based Power System Transient Analysis Software 3

A Graph Computing based Power System Transient Analysis Soft...

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3rd Asian Conference on Frontiers of Power and Energy

作者： Lu, Jiangang Tan, Jun Guo, Wenxin Zhao, Ruifeng Tan, Huijuan Lan, Tian Guangdong Power Grid Co Ltd Power Dispatching Control Ctr Guangzhou Peoples R China

ISBN: (纸本)9798350378467;9798350367676

With the increasing penetration of inverter based renewable energy and distributed energy storages in nowadays' power systems, the system status and operation conditions are hard to track with traditional scheduling-based power system transient analysis approach, and more fast and efficient transient analysis methods are needed to address the power system dynamic security problems. This paper proposed a graph computing based power system transient analysis approach to achieve efficient and accurate solution for power system stability analysis. To overcome the data management challenges, a spatiotemporal graph model is constructed for efficient data management in power system transient analysis. Then, a graph computing based transient analysis method is proposed to leverage the parallel computing power to speed up the computational process. Finally, a power system transient analysis software is developed based on the graph computing based transient analysis method to validate the proposed approach.

关键词： graph database graph computing node-breaker graph model transient analysis

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Loss Sensitivity Calculation and Analysis Based on graph computing 9

Loss Sensitivity Calculation and Analysis Based on Graph Com...

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9th International Conference on Power and Renewable Energy

作者： Wu, Yuezhou Yang, Tianlin Lu, Jiangang Guo, Wenxin Zhao, Ruifeng Tan, Huijuan Guangdong Power Grid Co Ltd Power Dispatching Control Ctr Kaiping City Guangdong Peoples R China

ISBN: (纸本)9798350377477;9798350377460

Loss sensitivity in electricity systems refers to the responsiveness of power losses in electrical transmission and distribution networks to various influencing factors. This concept is critical for optimizing the efficiency and reliability of power delivery from generation sources to end consumers. Especially in the electricity market trading process, network losses, although only a small part of the total cost, have a significant impact on each specific transaction and grid user. Therefore, in-depth understanding and analysis of the impact of generation transactions on the grid network loss can provide a basis for the power purchase plan for the grid to provide reference for the grid network loss management, which is of great significance. The current mainstream calculation and analysis method for network loss sensitivity is the marginal loss coefficient method, which determines the network loss share of each bus by analyzing the impact of bus injected power on the total network loss changes, but the nature of its serial calculation cannot properly solve the balance between real-time analysis of network loss optimization and accuracy, and with the increase in system size, this problem has gradually become a major obstacle to the realization of real-time analysis. This paper presents a novel parallel computing method based on graph computing for determining the sensitivity of system network loss and node-injected power. graph computing is a parallel computing method that stores, computes, and analyzes the information in a system by constructing a topological graph model and based on it. The method alleviates the computational burden associated with calculating network loss sensitivity as the scale of the power system expands by leveraging the parallel computing capabilities of graph databases. Furthermore, it facilitates the graphical calculation and analysis of network loss sensitivity through the utilization of graph storage.

关键词： Loss Sensitivity graph computing Generators Loss Sensitivity Loads Loss Sensitivity Flexible Load

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graph computing Based Security Constrained Unit Commitment in Hydro-thermal Power Systems Incorporating Pumped Hydro Storage

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 485-496页

作者： Longfei Wei Guangyi Liu Shen Yan Renchang Dai Yachen Tang Hitachi ABB Power Grids San JoseCAUSA School of Electrical Engineering Xi'an University of TechnologyXi'anChina Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-ChampaignChampaignILUSA Puget Sound Energy BellevueWAUSA Envision Digital Inc. Redwood CityCAUSA

This paper proposes a graph computing based mixed integer programming(MIP)framework for solving the security constrained unit commitment(SCUC)problem in hydro-thermal power systems incorporating pumped hydro storage(PHS).The proposed graph computing-based MIP framework considers the economic operations of thermal units,cascade hydropower stations and PHS stations,as well as their technical impacts towards the network ***,the hydro-thermal power system data and unit information are stored in a graph structure with nodes and edges,which enables nodal and hierarchical parallel computing for the unit commitment(UC)solution calculation and network security analysis.A MIP model is then formulated to solve the SCUC problem with the mathematical models of thermal units,cascade hydropower stations and PHS *** addition,two optimization approaches including convex hull reformulation(CHR)and special ordered set(SOS)methods are introduced for speeding up the MIP calculation *** ensure the system stability under the derived UC solution,a parallelized graph power flow(PGPF)algorithm is proposed for the hydro-thermal power system network security ***,case studies of the IEEE 118-bus system and a practical 2749-bus hydro-thermal power system are introduced to demonstrate the feasibility and validity of the proposed graph computing-based MIP framework.

关键词： graph computing hydro-thermal system mixed integer programming network security unit commitment

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graph computing Based Distributed Parallel Power Flow for AC/DC Systems with Improved Initial Estimate

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Journal of Modern Power Systems and Clean Energy 2021年第2期9卷 253-263页

作者： Wei Feng Chen Yuan Qingxin Shi Renchang Dai Guangyi Liu Zhiwei Wang Fangxing Li the University of Tennessee 1525 Coleman Road KnoxvilleTennessee 37909USA Global Energy Interconnection Research Institute North America San JoseCalifornia 95134USA

The sequential method is easy to integrate with existing large-scale alternating current(AC)power flow solvers and is therefore a common approach for solving the power flow of AC/direct current(DC)hybrid *** this paper,a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is *** proposed approach is capable of speeding up the entire computation process without compromising the accuracy of ***,the AC/DC network is intuitively represented by a graph and stored in a graph database(GDB)to expedite data *** the interconnection of AC grids via high-voltage direct current(HVDC)links,the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow *** each area,the fast-decoupled power flow(FDPF)is employed with node-based parallel computing(NPC)and hierarchical parallel computing(HPC)to quickly identify system ***,to reduce the alternate iterations in the sequential method,a new decoupled approach is utilized to achieve a good initial estimate for the Newton-Raphson *** the improved initial estimate,the sequential method can converge in fewer ***,the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power *** performance is verified on standard IEEE 300-bus system,extended large-scale systems,and a practical 11119-bus system in China.

关键词： AC/DC system distributed parallel computing graph computing initial estimate power flow analysis

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