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ANG: a combination of Apriori and graph computing techniques for frequent itemsets mining

JOURNAL OF SUPERcomputing 2019年第2期75卷 646-661页

作者： Zhang, Rui Chen, Wenguang Hsu, Tse-Chuan Yang, Hongji Chung, Yeh-Ching Tsinghua Univ Grad Sch Shenzhen Shenzhen 518057 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China BathSpa Univ Ctr Creat Comp Bath Avon England Tsinghua Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China

The Apriori algorithm is one of the most well-known and widely accepted methods for the association rule mining. In Apriori, it uses a prefix tree to represent k-itemsets, generates k-itemset candidates based on the frequent (k-1)-itemsets, and determines the frequent k-itemsets by traversing the prefix tree iteratively based on the transaction records. When k is small, the execution of Apriori is very efficient. However, the execution of Apriori could be very slow when k becomes large because of the deeper recursion depth to determine the frequent k-itemsets. From the perspective of graph computing, the transaction records can be converted to a graph G(V,E), where V is the set of vertices of G that represents the transaction records and E is the set of edges of G that represents the relations among transaction records. Each k-itemset in the transaction records will have a corresponding connected component in G. The number of vertices in the corresponding connected component is the support of the k-itemset. Since the time to find the corresponding connected component of a k-itemset in G is constant for any k, the graph computing method will be very efficient if the number of k-itemsets is relatively small. Based on Apriori and graph computing techniques, a hybrid method, called Apriori and graph computing (ANG), is proposed to compute the frequent itemsets. Initially, ANG uses Apriori to compute the frequent k-itemsets and then switches to the graph computing method when k becomes large (where the number of k-itemset candidates is relatively small). The experimental results show that ANG outperforms both Apriori and the graph computing method for all test cases.

关键词： Apriori graph computing Frequent itemset mining Data mining

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graph computing based Distributed State Estimation with PMUs

Graph Computing based Distributed State Estimation with PMUs

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IEEE Power and Energy Society General Meeting

作者： Yi Lu Chen Yuan Xiang Zhang Hua Huang Guangyi Liu Renchang Dai Zhiwei Wang Se Grid Sichun Eecric Power ConyGEIRI Norh AericGEIRI Norh AericNARI Technoogy Co. LdGEIRI Norh AericGEIRI Norh AericGEIRI Norh Aeric

ISBN: (数字)9781728155081

ISBN: (纸本)9781728155098

Power system state estimation plays a fundamental and critical role in the energy management system (EMS). To achieve a high performance and accurate system states estimation, a graph computing based distributed state estimation approach is proposed in this paper. Firstly, a power system network is divided into multiple areas. Reference buses are selected with PMUs being installed at these buses for each area. Then, the system network is converted into multiple independent areas. In this way, the power system state estimation could be conducted in parallel for each area and the estimated system states are obtained without compromise of accuracy. IEEE 118-bus system and MP 10790-bus system are employed to verify the results accuracy and present the promising computation performance.

关键词： Distributed computing graph computing State estimation Weighted least square (WLS)

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Accelerating Parallel graph computing with Speculation 19

Accelerating Parallel Graph Computing with Speculation

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16th ACM International Conference on computing Frontiers (CF)

作者： Ji, Shuo Zhao, Yinliang Yi, Qing Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Univ Colorado Colorado Springs CO 80907 USA

ISBN: (纸本)9781450366854

Nowadays distributed graph computing is widely used to process large amount of data on the internet. Communication overhead is a critical factor in determining the overall efficiency of graph algorithms. Through speculative prediction of the content of communications, we develop an optimization technique to significantly reduce the amount of communications needed for a class of graph algorithms. We have evaluated our optimization technique using five graph algorithms, Single-source shortest path, Connected Components, PageRank, Diameter, and Random Walk, on the Amazon EC2 clusters using different graph datasets. Our optimized implementations have reduced communication overhead by 21-93% for these algorithms, while keeping the error rates under 5%.

关键词： distributed computing graph computing speculative computing

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Performance Prediction for Distributed graph computing

Performance Prediction for Distributed Graph Computing

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International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Ji, Shuo Zhao, Yinliang Li, Yuxiang Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian Shaanxi Peoples R China Henan Univ Sci & Technol Sch Informat Engn Luoyang Peoples R China

ISBN: (纸本)9781728104669

Performance estimation for executing graph algorithms on the distributed systems, especially with sacrificing the accuracy of results to improve the runtime performance, is a pre-requisite for optimizing system parameters to achieve an applicable tradeoff between the runtime and the inaccuracy of results. This paper presents an experimental approach that predicts the runtime and the inaccuracy of conducting graph algorithms on the BSP-based distributed graph computing systems to optimize system parameters by using an artificial neural network (ANN) model. It samples different scales of subgraphs from the complete input graphs and executes the underlying algorithm on each subgraph to capture its characteristics. Then it essentially predicts the performance of executing the underlying algorithm on the complete graph by learning the scalability that how the runtime and the inaccuracy of results vary with different scales of graphs with an ANN network, which is trained off-line based on the captured characteristics of the subgraphs. We conducted all experimental studies on the Amazon EC2 Cloud. The experimental results demonstrate that the prediction approach can effectively predict the runtime with a relative error rate under 8% averagely and the inaccuracy of results with a relative error rate under 25% averagely compared to the actual performance results.

关键词： distributed computing graph computing performance prediction

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graph Database and graph computing for Cyber-Physical Power Systems 2

Graph Database and Graph Computing for Cyber-Physical Power ...

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2nd IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Liu, Xinzhan Wang, Di Xu, Luo Guo, Qinglai Huang, Yu Wu, Zanhong Tsinghua Univ Guangdong Prov Power Dispatching & Control Ctr China Southern Power Grid Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

ISBN: (纸本)9781538685495

Cyber-physical Power System (CPPS) presents a typical coupling graph structure composed of nodes and lines/links on both physical side and cyber side. With the rapid development of smart grids and coupling between cyber networks and physical networks, the CPPS network becomes larger and more complex. Since the natural graphic features of CPPS networks and its analysis methods are mostly inseparable from the graph theory, the application of graph computing systems and graph database will greatly help the analysis of CPPS problems. First, the representative graph computing systems are briefly introduced in this paper. And then, considering two-layer coupling structure of CPPS, the architecture consisting of the graph database and the graph computing system for CPPS is proposed.

关键词： Cyber-physical power system (CPPS) smart grid graph computing graph database

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Exploring big graph computing - An empirical study from architectural perspective

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2017年 108卷 122-137页

作者： Nai, Lifeng Xia, Yinglong Tanase, Ilie G. Kim, Hyesoon Georgia Inst Technol Atlanta GA 30332 USA Huawei Res Amer Santa Clara CA USA IBM TJ Watson Res Ctr Yorktown Hts NY USA

graph computing is widely applied in a large number of big data applications. Despite its importance, high performance graph computing remains a challenge, especially for large-scale graphs. In this paper, by analyzing from the architectural perspective, we study computational behaviors of graph computing in real-world use cases. We benchmark a set of representative graph algorithms implemented on a unified framework and conduct experiments to analyze comprehensive performance characteristics. In the characterization, we observed multiple insights, including irregular memory patterns, significant diverse behavior across different computations, highly data dependent behaviors, etc., using large-scale synthetic and real-world graphs. To the best of our knowledge, this is the first comprehensive architectural study on the full-scope of graph computing. It can improve our understanding on graph computing and help high performance computing research for graph-based big data applications. (C) 2016 Elsevier Inc. All rights reserved.

关键词： graph computing Architectural characterization Big data

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Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement With graph-computing-Based Power Flow Calculation

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IEEE ACCESS 2019年 7卷 167010-167018页

作者： Shi, Qingxin Yuan, Chen Feng, Wei Liu, Guangyi Dai, Renchang Wang, Zhiwei Li, Fangxing Univ Tennessee Dept EECS Knoxville TN 37996 USA GEIRI North Amer San Jose CA 95134 USA

The voltage stability is an essential security concern when a system is operating in peak load hours or subjected to an N-1 contingency. Among various voltage stability indices, Local Thevenin Index (LTI) has been a popular one, but mostly applied to the measurement-based framework. The reason is that it requires significant computing effort if applied to model-based approach, even though the model-based LTI calculation can provide more accurate results. In this paper, a new model-based LTI calculation is proposed using graph-computing-based power flow calculation, which is fast enough to fully utilize the advantage of model-based LTI. Then, when the voltage stability index of one or multiple buses exceeds the security limit, a demand response scheme is activated to increase the voltage security margin at the lowest cost. The simulation study of a thousand-level-bus system verifies the computation accuracy and efficiency of the proposed model-based LTI. It also demonstrates that the demand response action can enhance the system security effectively. Thus, the proposed work has advantages if applied to a large-scale Energy Management System (EMS).

关键词： Power system security voltage stability monitoring voltage stability index graph computing demand response security enhancement local Thevenin index

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A Faster Non-Linear Iteration Solver using graph computing and its Application in Power Flow Calculation 9th

A Faster Non-Linear Iteration Solver using Graph Computing a...

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9th International Conference on Applied Energy (ICAE)

作者： Liu, Kewen Liu, Guangyi Xie, Kai Wang, Zhiwei Global Energy Interconnect Res Inst North Amer San Jose CA 95134 USA Global Energy Interconnect Res Inst Beijing 102209 Peoples R China

To solve large scale fast matrix calculation problems, a Non-Linear Iteration Solver (NLIS) based on a BSP model using a graph database & computing technology is proposed in this paper. A node and edge level parallel iterative algorithm is implemented by using a graph computing algorithm combined with a message passing mechanism. IEEE testing cases and actual operation data of a provincial power system in China are used to verify the method, then expand the MP 10790 to over 100k and 1 million nodes systems for the calculation stress test on a conventional server. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： graph theory graph database graph computing power system analysis graph data modeling PageRank Algorithm Bulk Synchronous Parallel computing Model

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graphPIM: Enabling Instruction-Level PIM Offloading in graph computing Frameworks 23

GraphPIM: Enabling Instruction-Level PIM Offloading in Graph...

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23rd IEEE International Symposium on High Performance Computer Architecture (HPCA)

作者： Nai, Lifeng Hadidi, Ramyad Sim, Jaewoong Kim, Hyojong Kumar, Pranith Kim, Hyesoon Georgia Inst Technol Atlanta GA 30332 USA Intel Labs Portland OR USA

ISBN: (纸本)9781509049851

With the emergence of data science, graph computing has become increasingly important these days. Unfortunately, graph computing typically suffers from poor performance when mapped to modern computing systems because of the overhead of executing atomic operations and inefficient utilization of the memory subsystem. Meanwhile, emerging technologies, such as Hybrid Memory Cube (HMC), enable the processing-in-memory (PIM) functionality with offloading operations at an instruction level. Instruction offloading to the PIM side has considerable potentials to overcome the performance bottleneck of graph computing. Nevertheless, this functionality for graph workloads has not been fully explored, and its applications and shortcomings have not been well identified thus far. In this paper, we present graphPIM, a full-stack solution for graph computing that achieves higher performance using PIM functionality. We perform an analysis on modern graph workloads to assess the applicability of PIM offloading and present hardware and software mechanisms to efficiently make use of the PIM functionality. Following the real-world HMC 2.0 specification, graphPIM provides performance benefits for graph applications without any user code modification or ISA changes. In addition, we propose an extension to PIM operations that can further bring performance benefits for more graph applications. The evaluation results show that graphPIM achieves up to a 2.4x speedup with a 37% reduction in energy consumption.

关键词： processing-in-memory PIM graph computing hybrid memory cube HMC

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A Faster Non-Linear Iteration Solver using graph computing and its Application in Power Flow Calculation

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Energy Procedia 2017年 142卷 2534-2540页

作者： Kewen Liu Guangyi Liu Kai Xie Zhiwei Wang Global Energy Interconnection Research Institute 102209 Beijing China Global Energy Interconnection Research Institute North Amercia 95134 San Jose CA United States

关键词： graph theory graph database graph computing power system analysis graph data modeling PageRank Algorithm Bulk Synchronous Parallel computing Model

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