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distributed parallel Super-Block-Based Star Detection and Centroid Calculation

IEEE SENSORS JOURNAL 2018年第19期18卷 8096-8107页

作者： Wang, Zhen Jiang, Jie Zhang, Guangjun Beijing Univ Aeronaut & Astronaut Sch Instrumentat Sci & Optoelect Engn Minist Educ Key Lab Precis Optomechatron Technol Beijing 100191 Peoples R China

Due to the inadequacies of the speed of star detection and centroid calculation (SDCC), the applications of high-resolution and high-frame-rate image sensors are greatly limited in star trackers, restricting the performance of the star trackers. To resolve this problem, a novel distributed parallel super-block-based SDCC method is proposed in this paper. In contrast to the previous SDCC methods, the proposed method divides the star images into several sub-images and processes these sub-images in parallel. Then, by using a super-block-based method and a distributed shared memory-based method to process the stars in sub-images (sub-stars) and in adjacent sub-images (boundary stars), respectively, the position information of all the stars are detected during the parallel scan of the sub-images. The proposed method exhibits a speed that is up to 4 x M times faster than the previous SDCC methods in processing the star images that are divided into M sub-images. Experimental results demonstrate that the proposed method is correct and effective. It is applicable to the use of most high-performance image sensors and resolves the performance limitation of star trackers to achieve better attitude accuracy and attitude update rate.

关键词： distributed parallel star detection centroid location star tracker distributed shared memory image sensors application

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distributed parallel Sparse Multinomial Logistic Regression

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IEEE ACCESS 2019年 7卷 55496-55508页

作者： Lei, Dajiang Du, Meng Chen, Hao Li, Zhixing Wu, Yu Chongqing Univ Posts & Telecommun Coll Comp Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Software Engn Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Inst Web Intelligence Chongqing 400065 Peoples R China

Sparse Multinomial Logistic Regression (SMLR) is widely used in the field of image classification, multi-class object recognition, and so on, because it has the function of embedding feature selection during classification. However, it cannot meet the time and memory requirements for processing large-scale data. We have reinvestigated the classification accuracy and running efficiency of the algorithm for solving SMLR problems using the Alternating Direction Method of Multipliers (ADMM), which is called fast SMLR (FSMLR) algorithm in this paper. By reformulating the optimization problem of FSMLR, we transform the serial convex optimization problem to the distributed convex optimization problem, i.e., global consensus problem and sharing problem. Based on the distributed optimization problem, we propose two distribute parallel SMLR algorithms, sample partitioning-based distributed SMLR (SP-SMLR), and feature partitioning-based distributed SMLR (FP-SMLR), for a large-scale sample and large-scale feature datasets in big data scenario, respectively. The experimental results show that the FSMLR algorithm has higher accuracy than the original SMLR algorithm. The big data experiments show that our distributed parallel SMLR algorithms can scale for massive samples and large-scale features, with high precision. In a word, our proposed serial and distribute SMLR algorithms outperform the state-of-the-art algorithms.

关键词： Alternating Direction Method of Multipliers big data distributed parallel sparse multinomial logistic regression

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A distributed parallel Algorithm for Web Page Inverted Indexes Construction On the Cluster Computing Systems

A Distributed Parallel Algorithm for Web Page Inverted Index...

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International Forum on Information Technology and Applications (IFITA 2009)

作者： Liang Zhengyou Chen Tao Guangxi Univ Dept Comp Elect & Informat Nanning 530004 Peoples R China

ISBN: (纸本)9780769536002

Against the low index speed of serial algorithm for Web page inverted indexes construction, according to a characteristic of merge-sort algorithm meets the theory of scheduling divisible loads in parallel and distributed system, the paper proposed a new parallel algorithm basing on the triple sort-merge for Web page inverted indexes construction. The algorithm distributed parallel dealt with the two tasks parsing term and sorting these term postings which spent lots of time in the construction of inverted indexes, each term was represented as a triple, the time complexity of the algorithm was analyzed This paper also applied a Java middleware named ProActive, designed and implemented a distributive parallel Web page indexer named P_Indexer on the cluster computing systems. The algorithm analysis and experimental results showed the parallel algorithm reaches high efficiency and good scalability.

关键词： inverted indexes text search Web page indexer distributed parallel ProActive middleware

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DP-Pack: distributed parallel Packing for FPGAs 17

DP-Pack: Distributed Parallel Packing for FPGAs

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17th International Conference on Field-Programmable Technology (FPT)

作者： Chen, Qiangpu Shen, Minghua Xiao, Nong Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781728102139

Packing is one of the most critical stages in the FPGA physical syntheses flow. In this paper, we propose DP-Pack, a distributed parallel packing approach. DP-Pack consists of two primary steps. First, all of the minimal circuit units are assigned into several subsets where the conflicting units are located in the same subset and the non-conflicting units are distributed in different subsets. Then, the non-conflicting subsets are partitioned by round-robin such that the number of subsets in each processor core is equal approximately, leading to good load balance in parallel packing. Second, the parallelization between processor cores is implemented by the MPI-based message queue in a distributed platform. Note that DP-Pack has been integrated into the VTR 7.0 tool. Experimental results show that our DP-Pack scales to 8 processor cores to provide about 1.4 similar to 3.2x runtime advantages with acceptable quality degradation, comparing to the academic state-of-the-art AAPack.

关键词： distributed parallel FPGA Packing

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Research on distributed parallel Dimensionality Reduction Algorithm Based on PCA Algorithm 3

Research on Distributed Parallel Dimensionality Reduction Al...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Wang, Linlin Qilu Univ Technol Coll Comp Sci & Technol Jinan Shandong Peoples R China

ISBN: (纸本)9781538662434

PCA algorithm is a typical data dimensionality reduction method, which projects high-dimensional data to a lower-dimensional space to obtain a low-dimensional data set that can maximally represent these characteristics of the original data set. The PCA algorithm can effectively achieve dimensionality reduction for high-dimensional data and is widely used in various fields. Aimed at the tedious calculation process of PCA algorithm and the time-consuming of processing massive stream data, this paper proposes a distributed parallel dimensionality reduction algorithm that called DP-PCA by improving the PCA algorithm. Based on the theory of PCA algorithm, DP-PCA algorithm includes three parts of improvement research. Firstly, the original data set is preprocessed by using the "mean" method. Secondly, the solution process of correlation coefficient matrix is improved. Thirdly, this paper designs a distributed parallel dimensionality reduction scheme for DP-PCA algorithm. In addition, this paper deploys DP-PCA algorithm on Storm platform to realize parallelization of the algorithm, and tests the DP-PCA algorithm. Experiments show that DP-PCA algorithm improves computational efficiency and reduces the dimensionality reduction time, and improves the speedup ratio.

关键词： PCA algorithm dimensionality reduction distributed parallel correlation coefficient matrix Storm platform

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Research of distributed parallel Information Retrieval Based on JPPF

Research of Distributed Parallel Information Retrieval Based...

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2010 International Conference of Informationa Science and Management Engineering(2010年信息科学与管理工程国际学术会议 ISME 2010)

作者： Jing Xiong Jianliang Wang Jianliang Xu Department of Computer Science Ocean University of China Qingdao China School of Computer and Infor Department of Management Shandong Jiaotong Vocational College Weifang China Department of Computer Science Ocean University of China Qingdao China

Introduces distributed parallel method into information retrieval area. Takes information retrieval system based on database as an example, and proposes a distributed parallel retrieval strategy using java parallel processing framework JPPF. Some retrieval experiments building on MS SQL Server 2000 have been done by using the system. The experiment results have demonstrated that the distributed parallel computing techniques can be applied to significantly improve the performance of retrieval system.

关键词： distributed parallel information retrieval JPPF

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Local Parameter Optimization of LSSVM for Industrial Soft Sensing With Big Data and Cloud Implementation

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第5期16卷 2917-2928页

作者： Zhang, Xinyu Ge, Zhiqiang Zhejiang Univ Dept Control Sci & Engn Hangzhou 310027 Peoples R China

Due to the advantages of high prediction accuracy, least squares support vector machine (LSSVM) has been widely utilized for soft sensor developments in industrial processes. The hyper-parameters of LSSVM are often determined by minimizing the predicted error of validation set based on the intelligent optimization algorithm, which may lead to excessive optimization and model overfitting when validation set are selected improperly. Meanwhile, online parameters optimization is difficult to implement, which results in poor effect of local modeling. This paper proposes UMDA-LOS-LSSVM that is a LSSVM with parameters optimization in local objective set (LOS-LSSVM) by univariate marginal distribution algorithm (UMDA) based on the idea of local modeling. First, the local objective set is extracted in the candidate set based on the testing samples. Then, UMDA is utilized for minimize the predicted error of the objective set and provides the optimized parameters. Finally, training and testing of LSSVM are carried out based on the optimal parameters. In addition, this paper provides the distributed parallel form of the proposed method, which can be used for big data modeling and soft sensor development. The proposed method is applied in a CO2 absorbing column unit to estimate the residual CO2 content, which is implemented through an industrial big data distributed analytics platform. The results show a significant improvement of proposed method based soft sensor, compared to traditional methods.

关键词： Optimization Training Testing Mathematical model Data models Informatics Big Data Cloud computing distributed parallel estimation of distribution algorithm (EDA) local objective set least squares support vector machine (LSSVM)

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A Prototype for Pipeline-Composable Task-Based Visualization Algorithms 12

A Prototype for Pipeline-Composable Task-Based Visualization...

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12th IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)

作者： Petersen, Marvin Werner, Kilian Schnorr, Andrea Kuhlen, Torsten Wolfgang Garth, Christoph Rhein Westfal TH Aachen Aachen Germany Tech Univ Kaiserslautern Kaiserslautern Germany

ISBN: (数字)9781665491563

ISBN: (纸本)9781665491563

For next generation platforms, the paradigm of task-based parallelism has the potential to overcome some of the accompanying challenges. This paradigm has already been applied by the visualization community to specific algorithms and problems. However, one advantage of the task-based paradigm-the interleaving of workshould lead to better utilization of resources and ultimately lower execution times if the paradigm is applied to whole pipelines. In order to investigate this potential, we build a prototype framework for composable task-based parallel visualization algorithms. With this we explore the combination of a strictly task-based approach with the addition of a pipeline layer for visualization algorithms. This additional layer eases the composition of larger task-based parallel visualization applications without the need to explicitly define the exact connection in a task graph between algorithms. In this manner, task-based visualization algorithms can be designed towards a common interface, be easily combined, and still benefit from the advantages of the task-based paradigm across algorithm boundaries, such as latency hiding. We explore the design implications of this combination and show initial results of the scalability and the impact of task interleaving on the runtime of exemplary pipelines.

关键词： task-based parallelism pipeline composition scientific visualization distributed parallel HPX

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Automatic Penalty and Degree Continuation for parallel Pre-Conditioned Mesh Curving on Virtual Geometry

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COMPUTER-AIDED DESIGN 2022年 146卷 103208-103208页

作者： Ruiz-Girones, Eloi Roca, Xevi Barcelona Supercomp Ctr BSC Comp Applicat Sci & Engn Barcelona 08034 Spain

We present a distributed parallel mesh curving method for virtual geometry. The main application is to generate large-scale curved meshes on complex geometry suitable for analysis with unstructured high-order methods. Accordingly, we devise the technique to generate geometrically accurate meshes composed of high-quality elements. To this end, we advocate for degree continuation on a penalty based second-order optimizer that uses global tight tolerances to converge the distortion residuals. To reduce the method memory footprint, waiting time, and energy consumption, we combine three main ingredients. First, we propose a matrix-free GMRES solver pre-conditioned with successive over relaxation by blocks to reduce the memory footprint three times. We also propose an adaptive penalty technique, to reduce the number of non-linear iterations. Third, we propose an indicator of the required linear solver tolerance to reduce the number of linear iterations. On thousands of cores, the method curves meshes composed of millions of quartic elements featuring highly stretched elements while matching a virtual topology. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： High-order mesh curving distributed parallel Pre-conditioner p-continuation

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SparkGC: Spark based genome compression for large collections of genomes

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BMC BIOINFORMATICS 2022年第1期23卷 297-297页

作者： Yao, Haichang Hu, Guangyong Liu, Shangdong Fang, Houzhi Ji, Yimu Nanjing Vocat Univ Ind Technol Sch Comp & Software Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Jiangsu HPC & Intelligent Proc Engineer Res Ctr Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Inst High Performance Comp & Bigdata Nanjing 210023 Peoples R China

Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark's in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://***/haichangyao/SparkGC.

关键词： Genome compression Reference-based compression Spark distributed parallel

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