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The research of scientific computing environment on scilab

8th Asia-Pacific Web Conference and Workshops (APWeb 2006)

作者： Zhang, ZL Wang, ZY Qi, DY Lin, WW Zhang, D Li, YJ S China Univ Technol Coll Comp Sci & Engn Guangzhou 510640 Peoples R China Xuchang Univ Comp Network Ctr Xuchang 461000 Peoples R China

ISBN: (纸本)3540311580

The paper focuses on the implements of distributed parallel computing oil Scilab, a wide-used Grid environment. Netbutterfly Grid computing System (NGCS) is designed, Some of key issues, such as the architecture of NGCS job distributing, system communications, fault tolerance and security problem are analyzed and discussed. And a new science computing method is advanced. Finally, an example on Grid computing environments is presented. The experiment shows that it's the results are satisfying.

关键词： distributed parallel computing grid computing Scilab globus toolkit

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A parallel Power Flow Algorithm for Ladder-shaped Shipboard Power System 7

A Parallel Power Flow Algorithm for Ladder-shaped Shipboard ...

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7th International Symposium on Computational Intelligence and Design (ISCID)

作者： Fang, Huili Yang, Genke Pan, Changchun Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China

ISBN: (纸本)9781479970056

Power flow calculation of shipboard power system aims at determining operation conditions of the whole system, such as voltages of every bus, power distribution and power loss in shipboard grid, according to given operation conditions and network structure. Ladder-shaped shipboard power system is a newly developed network with greater reliability and flexibility. In this paper, a parallel power flow calculation algorithm is proposed which specifically considers the features of ladder shipboard power system. This algorithm divides the whole system into concentrated power supply network and several distribution subnets, and adopts node potential method and forward-backward sweep calculation to different calculation levels respectively. The test results indicate that this parallel algorithm has better performance of convergence and greater flexibility.

关键词： load flow calculation ladder-shaped shipboard power system distributed parallel computing

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Efficient Level-Based Top-Down Data Cube Computation Using MapReduce 14th

Efficient Level-Based Top-Down Data Cube Computation Using M...

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14th International Conference on Data Warehousing and Knowledge Discovery (DaWaK)

作者： Lee, Suan Kim, Jinho Moon, Yang-Sae Lee, Wookey Kangwon Natl Univ Dept Comp Sci Chunchon Kangwon South Korea Inha Univ Dept Ind Engn Inchon South Korea

ISBN: (纸本)9783662478042;9783662478035

Data cube is an essential part of OLAP(On-Line Analytical Processing) to support efficiently multidimensional analysis for a large size of data. The computation of data cube takes much time, because a data cube with d dimensions consists of 2(d) (i.e., exponential order of d) cuboids. To build ROLAP (Relational OLAP) data cubes efficiently, many algorithms (e.g., GBLP, PipeSort, PipeHash, BUC, etc.) have been developed, which share sort cost and input data scan and/or reduce data computation time. Several parallel processing algorithms have been also proposed. On the other hand, MapReduce is recently emerging for the framework processing huge volume of data like web-scale data in a distributed/parallel manner by using a large number of computers (e.g., several hundred or thousands). In the MapReduce framework, the degree of parallel processing is more important to reduce total execution time than elaborate strategies like short-share and computation-reduction which existing ROLAP algorithms use. In this paper, we propose two distributed parallel processing algorithms. The first algorithm called MRLevel, which takes advantages of the MapReduce framework. The second algorithm called MRPipeLevel, which is based on the existing PipeSort algorithm which is one of the most efficient ones for top-down cube computation. (Top-down approach is more effective to handle big data, compared to others such as bottom-up and special data structures which are dependent on main-memory size.) The proposed MRLevel algorithm tries to parallelize cube computation and to reduce the number of data scan by level at the same time. The MRPipeLevel algorithm is based on the advantages of the MRLevel and to reduce the number of data scan by pipelining at the same time. We implemented and evaluated the performance of this algorithm under the MapReduce framework. Through the experiments, we also identify the factors for performance enhancement in MapReduce to process very huge data.

关键词： Data cube ROLAP MapReduce Hadoop distributed parallel computing

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OPS: Optimized Shuffle Management System for Apache Spark 20

OPS: Optimized Shuffle Management System for Apache Spark

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49th International Conference on parallel Processing (ICPP)

作者： Cheng, Yuchen Wu, Chunghsuan Liu, Yanqiang Ren, Rui Xu, Hong Yang, Bin Qi, Zhengwei Shanghai Jiao Tong Univ Shanghai Peoples R China City Univ Hong Kong Hong Kong Peoples R China Intel Corp Santa Clara CA USA

ISBN: (纸本)9781450388160

In recent years, distributed computing frameworks, such as Hadoop MapReduce and Spark, are widely used for big data processing. With the explosive growth of the amount of data, companies tend to store intermediate data of the shuffle phase on disk instead of memory. Therefore, intensive network and disk I/O are both involved in the shuffle phase. To optimize the overhead of the shuffle phase, we propose OPS, an open-source distributed computing shuffle management system based on Spark, which provides an independent shuffle service for Spark. By using early-merge and early-shuffle strategy, OPS alleviates the I/O overhead in the shuffle phase and efficiently schedules the I/O and computing resources. OPS also proposes a slot-based scheduling algorithm to predict and calculate the optimal scheduling result of the reduce task. Besides, OPS provides a taint-redo strategy to ensure the fault tolerance of computing jobs. We evaluated the performance of OPS on a 100-node Amazon AWS EC2 cluster. Overall, OPS optimizes the overhead of shuffle by nearly 50%. In the test cases of HiBench, OPS improves end-to-end completion time by nearly 30% on average.

关键词： distributed parallel computing shuffle performance optimization

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Data Provenance for Multi-Agent Models 13

Data Provenance for Multi-Agent Models

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13th Annual IEEE International Conference on e-Science (e-Science)

作者： Davis, Delmar B. Featherston, Jonathan Fukuda, Munehiro Asuncion, Hazeline U. Univ Washington Bothell Sch Sci Technol Engn & Math Bothell WA 98011 USA

ISBN: (纸本)9781538626863

Multi-agent simulations are useful for exploring collective patterns of individual behavior in social, biological, economic, network, and physical systems. However, there is no provenance support for multi-agent models (MAMs) in a distributed setting. To this end, we introduce ProvMASS, a novel approach to capture provenance of MAMs in a distributed memory by combining inter-process identification, lightweight coordination of in-memory provenance storage, and adaptive provenance capture. ProvMASS is built on top of the Multi-Agent Spatial Simulation (MASS) library, a framework that combines multi-agent systems with large-scale fine-grained agent-based models, or MAMs. Unlike other environments supporting MAMs, MASS parallelizes simulations with distributed memory, where agents and spatial data are shared application resources. We evaluate our approach with provenance queries to support three use cases and performance measures. Initial results indicate that our approach can support various provenance queries for MAMs at reasonable performance overhead.

关键词： data provenance agent-based systems distributed parallel computing

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A novel short-term load forecasting method based on mini-batch stochastic gradient descent regression model

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期211卷

作者： Lizhen, Wu Yifan, Zhao Gang, Wang Xiaohong, Hao Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China Lanzhou Univ Technol Lanzhou Peoples R China

Short-term Load Forecasting (STLF) is the basis of smart distribution network system operation, planning, and dispatching. The traditional linear regression prediction method has the problems of slow prediction speed and low prediction accuracy. In order to solve the problem, an improved regression model based on mini-batch stochastic gradient descent is proposed in this paper. Combined with the big data analysis and processing platform, the collected data is conformed, and the parallel computing model Map-Reduce is used to parallelize mini-batch stochastic gradient descent algorithm for improving the processing ability of mini-batch stochastic gradient descent algorithm in big data load forecasting, and shorten load forecasting time. Meanwhile, in order to clean up the duplicated data and bad data generated by the smart meter and sensor before calculation, an adaptive sorted neighborhood method is proposed to detect the repeatedly recorded data, and the K-means clustering method is used to eliminate the noise data .The experimental results show that the parallelized minibatch stochastic gradient descent algorithm is much faster than the traditional regression analysis algorithm when the data volume is large. The average absolute percentage error of the load forecasting model for Belgium and a transformer station in Baiyin city of Gansu Province in China is 1.902% and 2.058% respectively, which satisfies the requirements of load forecasting.

关键词： Big data analyses Mini -batch stochastic gradient descent Load forecasting distributed parallel computing Map -Reduce framework

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Multi-level interactive unit commitment of regional power system

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 125卷 106464-106464页

作者： Ji, Xingquan Zhang, Yumin Han, Xueshan Ye, Pingfeng Xu, Bo Yu, Yongjin Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China State Grid Energy Res Inst Co Beijing 102209 Peoples R China

distributed energy resource (DER) including wind power, solar energy and energy storage system (ESS) are connected to the active distribution network (ADN) in various combination ways, which makes the distribution network have interaction. As a bridge connecting the transmission grid (TG) and micro grid (MG), ADN breaks the traditional operation pattern of TG + ADN + MG. Considering the physical connections and shared information among TG, ADN and MG, this paper proposes a decentralized and parallel analytical target cascading (ATC) algorithm for interactive unit commitment (UC) implementation in regional power systems. To explore the synergistic ability of the TG + ADN + MG coping with uncertainties of DER, i.e., wind power, the primary and secondary frequency regulation of TG are implemented to cope with uncertainties. Furthermore, the distributional uncertainty of wind power is well modeled by data driven, which is proposed in our previous work (Zhang et al., 2019) [1]. Both the startup/shutdown variables of the thermal units and the variables in TG + ADN + MG are integrated into the multi-level interactive UC model to optimize simultaneously, thus realizing the optimal goal of the whole network, resources complementary and optimal allocation of power system. An improved 6-bus system is used to test the proposed model, the numerical results show that the proposed decentralized algorithm is a fully parallelized procedure. And it also demonstrates the parallel implementation significantly enhances computations efficiency of the ATC algorithm.

关键词： Analytical target cascading algorithm distributed parallel computing Primary and secondary frequency regulation Unit commitment

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Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 156卷 107615-107615页

作者： Yan, Wang-Ji Cao, Shi-Ze Ren, Wei-Xin Yuen, Ka-Veng Li, Dan Katafygiotis, Lambros Univ Macau State Key Lab Internet Things Smart City Taipa Macao Peoples R China Univ Macau Dept Civil & Environm Engn Taipa Macao Peoples R China Hefei Univ Technol Dept Civil Engn Hefei Peoples R China Shenzhen Univ Dept Civil Engn Shenzhen Guangdong Peoples R China Hongkong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

This study was devoted to investigating stochastic model updating in a Bayesian inference framework based on a frequency response function (FRF) vector without any post processing such as smoothing and windowing. The statistics of raw FRFs were inferred with a multivariate complex-valued Gaussian ratio distribution. The likelihood function was formulated by embedding the theoretical FRFs that contained the model parameters to be updated in the class of the probability model of the raw FRFs. The Transitional Markov chain Monte Carlo (TMCMC) used to sample the posterior probability density function implies considerable computational toll because of the large batch of repetitive analyses of the forward model and the increasing expense of the likelihood function calculations with large-scale loop operations. The vectorized formula was derived analytically to avoid time-consuming loop operations involved in the likelihood function evaluation. Furthermore, a distributed parallel computing scheme was developed to allow the TMCMC stochastic simulation to run across multiple CPU cores on multiple computers in a network. The case studies demonstrated that the fast-computational scheme could exploit the availability of high-performance computing facilities to drastically reduce the time-to-solution. Finally, parametric analysis was utilized to illustrate the uncertainty propagation properties of the model parameters with the variations of the noise level, sampling time, and frequency bandwidth. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Bayesian theory Frequency response function Model updating Structural health monitoring Vectorization computation distributed parallel computing

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A Two-Level Cross-Sectional Optimization Approach for Automotive Body Forward Conceptual Design

A Two-Level Cross-Sectional Optimization Approach for Automo...

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第19届亚太汽车工程年会暨2017中国汽车工程学会年会

作者： Qin Huan Liu Zijian Liu Yu Zhong Haolong State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University College of Mechanical and Vehicle Engineering Hunan University

Conceptual design is vital in automotive body development and it has a great effect on later design *** conventional automobile practice,reverse conceptual design is employed,which usually is time-consuming,laborious and *** this paper,forward conceptual design is employed to shorten production ***,an exact structural analysis approach for spatial semi-rigid framed structures,i.e.,the transfer stiffness matrix method(TSMM) proposed in our previous study,is adopted for the static and dynamic analyses of body-in-white(BIW) ***,a two-level cross-sectional optimization approach is proposed for automotive BIW lightweight design,and genetic algorithm(GA) is utilized to solve the optimization ***,an object-oriented MATLAB toolbox,using distributed parallel computing technique,is developed to promote the conceptual design of BIW ***,relevant numerical examples demonstrate the validity of the proposed method.

关键词： BIW structure forward conceptual design transfer stiffness matrix method cross-sectional optimization MATLAB toolbox distributed parallel computing

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Research on Massive Video Image Processing based on Cloud Server

Research on Massive Video Image Processing based on Cloud Se...

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International Conference on Image, Vision and computing (ICIVC)

作者： Yang Haigen Ding Qian Huang Yusheng Yu Longyan Xu Wenting Han Longbao Fu Xiao Guo Feng Engineering Research Center of Wider and Wireless Communication Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P.R. China Beijing Institute of Mechanical and Electrical Engineering Beijing P.R. China China International Engineering Consulting Corporation Beijing P.R. China Nanjing University of Posts and Telecommunications Nanjing P.R. China

The following topics are dealt with: feature extraction; learning (artificial intelligence); convolutional neural nets; object detection; image segmentation; image classification; image colour analysis; computer visio... 详细信息

ISBN: (纸本)9781728123264

关键词： Massive video processing Intelligent video analysis distributed parallel computing Cloud computing Cloud computing parallel PROCESSING (COMPUTERS) real-time analysis VIDEO IMAGES Clouds Videotapes server systems VIDEO ANALYSIS time series analysis

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