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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big Data Science and Engineering / 14th IEEE Int Symposium on parallel and distributed Proc with Applicat (IEEE Trustcom/BigDataSE/ISPA)

作者： Wu, Renke Huang, Linpeng Yu, Peng Shen, Kai Shi, Qiuwei Zhou, Haojie Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China Jiangnan Inst Comp Technol State Key Lab Mathemat Engn & Adv Comp Wuxi Peoples R China

ISBN: (纸本)9781509032051

distributed parallel computing platform performs well in processing big data. However, due to the platform's complexity and distributed characteristics, it is hard to design and achieve. For example, during the platform's design phase, variations are unpredictable. To address these issues, a highly structured object-oriented framework for systematic modeling, which has high flexibility, reusability and maintainability, is needed. In this paper, we present and implement an empirical development approach to achieve the specific modeling framework, based on meta-model evolution with hot-spots (variable parts) recognition. Our work expands this idea into the following directions. Firstly, we define a generic and stable meta-model. We give out concept models, object models and dynamic models of main objects. General data partitioning, job division and inter-node communication mechanism are designed. Secondly, based on the meta-model, we complete framework's incremental adaptation using an iterative process according to design variations. Practical experience of our empirical case study shows, the approach gets a relatively mature framework for design theory instruction. The integrated framework clearly expresses main objects and their relationship. This work promotes the development of parallel computing framework to have a qualitative leap, and eventually have more extensive application and popularization of the established framework.

关键词： big data processing platform distributed parallel computing development approach modeling meta-model evolution

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Workload Prediction and Weighted Rule-Based Task Scheduling for Face Certification System on distributed parallel computing

Workload Prediction and Weighted Rule-Based Task Scheduling ...

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International Conference on Grid and distributed computing

作者： Kim, Tae Young Lee, Jong Sik Inha Univ Sch Informat Engn Inchon 402751 South Korea

ISBN: (纸本)9783642271793

This paper presents a workload prediction and weighted rule-based task scheduling for face certification on distributed parallel computing. To compose a large-scale certification system. such as a criminal surveillance system for a public security, the system requires an enormous processing power. Thus a grid and distributed parallel computing is an essential approach for a large scale certification system. However his kind of approach is generally comprised of heterogeneous resources. And differential characteristics of each resource have influence on a performance of system. Therefore, an efficient task distribution and scheduling is necessary to improve a performance of system. There are various kinds of scheduling for task distribution. However existing methods cannot provide a suitable task distribution for a face certification system. Therefore, this paper proposes a task scheduling which includes a queue management policy with workload-prediction and weighted rule-based resource selection. The proposed method predicts the volume of certification task for a task queue management policy and selects the suitable certification server using performance weighted rules. Simulation result shows that the proposed method has better performance than other scheduling methods.

关键词： Task Scheduling Workload Prediction Rule-based System distributed parallel computing

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Implement distributed parallel computing Based on EJB

Implement Distributed Parallel Computing Based on EJB

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2004 International Symposium on distributed computing and Applications to Business, Engineering and Science

作者： Huang Zhehuang~1 Jiang Xiufeng~2 Wang Meiqing~3 College of Mathematics and Computer Science,Fuzhou University Fuzhou,Fujian 350002,China

The EJB standard of Sun Company is a typical designing criterion of *** this paper we build up a distributed parallel computing system based on EJB and implement the parallel algorithms of matrix multiplication with the system. Experimental results show that the system can obtain a rapid speedup with the increase of computing nodes and the sizes of matrixes.

关键词： EJB distributed parallel computing Component

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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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EFFICIENT parallel computing IN distributed WORKSTATION ENVIRONMENTS

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parallel computing 1993年第11期19卷 1221-1234页

作者： CAP, CH STRUMPEN, V UNIV ZURICH INST INFORMATCH-8057 ZURICHSWITZERLAND

The typical workstation in a LAN is idle for long periods of time. Within the concept of a hypercomputer this free, distributed computing power can be placed at the disposal of the user. The main problem with this approach is the permanently changing load situation in the network. We show that heterogeneous partitioning with respect to the load situation at startup and dynamic load balancing throughout the entire computation are essential techniques for obtaining high efficiency with the hypercomputer approach. We describe a parallel programming platform called THE PARFORM, which supports these two features and therefore proves faster than related approaches. Performance measurements and a scalability model for an explicit finite difference solver of a partial differential equation conclude the paper.

关键词： distributed parallel computing IDLE WORKSTATIONS DYNAMIC LOAD BALANCING SCALABILITY

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KNN-DBSCAN: a DBSCAN in high dimensions

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ACM TRANSACTIONS ON parallel computing 2025年第1期12卷 1-27页

作者： Chen, Youguang Ruys, William Biros, George UT Austin Oden Inst Austin TX 78712 USA

Clustering is a fundamental task in machine learning. One of the most successful and broadly used algorithms is DBSCAN, a density-based clustering algorithm. DBSCAN requires & varepsilon;-nearest neighbor graphs of the input dataset, which are computed with range-search algorithms and spatial data structures like KD-trees. Despite many efforts to design scalable implementations for DBSCAN, existing work is limited to low-dimensional datasets, as constructing & varepsilon;-nearest neighbor graphs can be expensive in high-dimensions. This article introduces a modified DBSCAN, using k-nearest neighbor (kNN) graphs to improve efficiency. We outline conditions for kNN-DBSCAN to match DBSCAN's results and present a parallel implementation using OpenMP and MPI for shared and distributed memory systems. Testing on datasets up to 32 dimensions, we achieve remarkable scalability. Our implementation clusters one billion 3D points in under one second on 28K cores at TACC's Frontera system. In a larger run, we cluster 65 billion points in 20 dimensions in under 40 seconds using 114,688 cores. Our method is up to 37x faster than state-of-the-art parallel DBSCAN on a 20-dimensional dataset with 4 million points. Code is available at https://***/ut-padas/knndbscan.

关键词： Density-based clustering nearest neighbor search minimum spanning tree distributed parallel computing performance analysis

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A parallel Sequential SBAS Processing Framework Based on Hadoop distributed computing

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REMOTE SENSING 2024年第3期16卷 466页

作者： Wu, Zhenning Lv, Xiaolei Yun, Ye Duan, Wei Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Inst Software Beijing 100190 Peoples R China

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth's crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and st

关键词： InSAR sequential SBAS Hadoop distributed parallel computing

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An object-oriented MATLAB toolbox for automotive body conceptual design using distributed parallel optimization

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ADVANCES IN ENGINEERING SOFTWARE 2017年 106卷 19-32页

作者： Qin, Huan Liu, Zijian Liu, Yu Zhong, Haolong Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China

Appropriate structural analysis and optimization methods are of great significance for the conceptual design of automotive body-in-white (BIW) structure. This paper simplifies BIW structure as a spatial semi-rigid framed structure to provide early-stage predictions. Then a novel exact transfer stiffness matrix method (TSMM) is proposed for both static and dynamic analyses of three-dimensional semi-rigid framed structures. The matrix storage and equation solution techniques for large-scale engineering structures are also considered. Additionally, a size optimization mathematical model for BIW conceptual structure is formulated and solved by genetic algorithm (GA). Afterwards, to promote the conceptual design of BIW structure, an object-oriented MATLAB toolbox, based on TSMM, is developed. The Unified Modeling Language (UML) and strategy design pattern are employed to facilitate the development of the toolbox. distributed parallel computing technique is adopted to speed up the former sequential optimization algorithm with simple modifications. Lastly, the validity of this easy-used toolbox is demonstrated by a benchmarking auto-body. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： BIW structure Conceptual design Transfer stiffness matrix method Size optimization Object-oriented MATLAB toolbox distributed parallel computing

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Efficient distributed parallel Aligning Reads and Reference Genome with Many Repetitive Subsequences Using Compact de Bruijn Graph 12

Efficient Distributed Parallel Aligning Reads and Reference ...

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12th International Symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Li, Yao Zhong, Cheng Chen, Danyang Zhang, Jinxiong Yin, Mengxiao Guangxi Univ Sch Comp Elect & Informat Nanning Peoples R China Guangxi Univ Key Lab Parallel Distributed Comp Technol Nanning Peoples R China

ISBN: (纸本)9781665496391

A large number of reads generated by the next generation sequencing platform will contain many repetitive subsequences. Effective localizing and identifying genomic regions containing repetitive subsequences will contribute to the subsequent genomic data analysis. To accelerate the alignment between large-scale short reads and reference genome with many repetitive subsequences, this paper develops a compact de Bruijn graph based short-read alignment algorithm on distributed parallel computing platform. The algorithm uses resilient distributed data sets (RDDS) to perform calculations in memory, and executes the broadcast method to distribute short reads and reference genome to the computing nodes to reduce the data communication time on the cluster system, and the number of RDD partitions is set to optimize the performance of parallel aligning algorithm. Experimental results on real datasets show that compared with the compact de Bruijn graph based sequential short-read alignment algorithm, our implemented distributed parallel alignment algorithm achieves good acceleration on the premise of obtaining the same correct alignment percentage as a whole, and compared with existing distributed parallel alignment algorithms, the implemented parallel algorithm can more quickly complete the alignment between large-scale short reads and reference genome with highly repetitive subsequences.

关键词： read alignment highly repetitive subsequences compact de Bruijn graph Hash indexing distributed parallel computing

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Research and Practice of distributed parallel Search Algorithm on Hadoop_MapReduce

Research and Practice of Distributed Parallel Search Algorit...

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International Conference on Control Engineering and Communication Technology (ICCECT)

作者： Duan, AiLing Si, HaiFang Henan Univ Technol Sch Informat Sci & Engn Zhengzhou 450001 Peoples R China

ISBN: (纸本)9781467344999;9780769548814

Hadoop_Mapreduce is winning more and more attention with its open source distributed parallel computing technology, high efficiency and economy. This paper describes the Hadoop_Mapreduce and its related technologies, studies in detail the thought behind the Hadoop_MapReduce algorithm and the system architecture, and for the field of internet search relating massive amounts of data, discusses the algorithm parallelization method of the algorithm and its feasibility, and applies MapReduce model on the Hadoop platform to realize inverted index by means of parallelization.

关键词： Hadoop_MapReduce algorithm distributed parallel computing Inverted index

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