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A parallel algorithm for the initial screening of space debris collisions prediction using the SGP4/SDP4 models and GPU acceleration

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ADVANCES IN SPACE RESEARCH 2017年第9期59卷 2398-2406页

作者： Lin, Mingpei Xu, Ming Fu, Xiaoyu Beihang Univ Sch Astronaut Beijing 100191 Peoples R China

Currently, a tremendous amount of space debris in Earth's orbit imperils operational spacecraft. It is essential to undertake risk assessments of collisions and predict dangerous encounters in space. However, collision predictions for an enormous amount of space debris give rise to large-scale computations. In this paper, a parallel algorithm is established on the Compute Unified Device Architecture (CUDA) platform of NVIDIA Corporation for collision prediction. According to the parallel structure of NVIDIA graphics processors, a block decomposition strategy is adopted in the algorithm. Space debris is divided into batches, and the computation and data transfer operations of adjacent batches overlap. As a consequence, the latency to access shared memory during the entire computing process is significantly reduced, and a higher computing speed is reached. Theoretically, a simulation of collision prediction for space debris of any amount and for any time span can be executed. To verify this algorithm, a simulation example including 1382 pieces of debris, whose operational time scales vary from 1 min to 3 days, is conducted on Tesla C2075 of NVIDIA. The simulation results demonstrate that with the same computational accuracy as that of a CPU, the computing speed of the parallel algorithm on a GPU is 30 times that on a CPU. Based on this algorithm, collision prediction of over 150 Chinese spacecraft for a time span of 3 days can be completed in less than 3 h on a single computer, which meets the timeliness requirement of the initial screening task. Furthermore, the algorithm can be adapted for multiple tasks, including particle filtration, constellation design, and Monte-Carlo simulation of an orbital computation. (C) 2017 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： parallel algorithm Space debris Initial screening GPU acceleration CUDA

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A parallel algorithm for Finding All Elementary Circuits of a Directed Graph

A Parallel Algorithm for Finding All Elementary Circuits of ...

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第37届中国控制会议

作者： Wenbo Lu Qianchuan Zhao Cangqi Zhou Center for Intelligent and Networked Systems Department of Automation Tsinghua University School of Computer Science and Engineering Nanjing University of Science and Technology

Graph can be used as a significant model to simulate the real world. We can find this kind of models in many different fields, such as transportation, navigation, logistics, management and social networks. In such graphs, a circuit is often regarded as a closed-loop way by which information propagates. There are several algorithms for finding all elementary *** these algorithms, Jonson’s algorithm suffers the lowest time complexity so far. In this paper, we introduce a parallel algorithm based on Johnson’s algorithm. We demonstrate that it is capable to handle large-scale graphs with a computing *** on both synthetic and real-world data sets demonstrate the effectiveness of the proposed algorithm.

关键词： Graph Theory Elementary Circuits Map Reduce parallel algorithm

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A parallel algorithm for N-Way Interval Set Intersection

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PROCEEDINGS OF THE IEEE 2017年第3期105卷 542-551页

作者： Layer, Ryan M. Quinlan, Aaron R. Univ Utah Dept Human Genet Salt Lake City UT 84112 USA Univ Utah Human Genet & Biomed Informat Salt Lake City UT 84112 USA

The comparison of sets of genome intervals (e.g., genes, repeats, ChIP-seq peaks) is essential to genome research, especially as modern sequencing technologies enable ever larger and more complex experiments. Relationships between genomic features are commonly identified by their intersection: that is, if feature sets contain overlapping intervals then it is inferred that they share a common biological function or origin. Using this technique, researchers identify genomic regions that are common among multiple (or unique to individuals) data sets. While there have been recent advances in algorithms for pairwise intersections between two sets of genomic intervals, few advances have been made to the intersection of many sets of genomic intervals. Identifying intersections among many interval sets is particularly important when attempting to distill biological insights from the massive, multidimensional data sets that are common to modern genome research. For such analyses, speed and efficiency are crucial, given the size and sheer number of data sets involved. To solve this problem, we present a novel ``slice-then-sweep'' algorithm that, given N interval sets, efficiently reveals the subset of intervals that are common to all N sets. We demonstrate that our algorithm is more efficient in the sequential case and has a vastly higher capacity for parallelization with a 19x speedup over the existing algorithm.

关键词： Bioinformatics computational biology genome analysis genomic interval intersection parallel algorithm

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Design and Implementation of Word2Vec parallel algorithm Based on HPC

Design and Implementation of Word2Vec Parallel Algorithm Bas...

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Chinese Automation Congress (CAC)

作者： Yi, Xianyong Zheng, Rongge Wang, Aoyu Qin, Hao Chen, Yufeng Shandong Univ Sch Mech Elect & Informat Engn Weihai Weihai Peoples R China

ISBN: (纸本)9781538635247

Word2Vec, (Word to Vector) processes natural language by calculating the cosine similarity. However, the serial algorithm of original Word2Vec fails to satisfy the demands of training of corpus text because of the explosive growth of information. It has become the bottleneck owing to its comparatively low processing efficiency. The High Performance Computing (HPC) specializes in improving the calculation efficiency;therefore, the training efficiency of corpus texts can be greatly improved by parallelizing Word2Vec algorithm. After analyzing the characteristics of the Word2Vec algorithm in detail, we design and implement a parallel Word2Vec algorithm and use it to train corpus text on HPC. Furthermore, the corpus texts of different sizes are collected and trained, and the speed-up ratio is calculated by using the serial algorithm and parallel algorithm of Word2Vec, respectively. The experimental results show that there is a higher speed-up ratio when using the Word2Vec parallel algorithm running on HPC.

关键词： HPC Word2Vec parallel algorithm Natural Language Processing

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Enhanced Gray Scale Skeletonization of Fingerprint Ridges Using parallel algorithm 1st

Enhanced Gray Scale Skeletonization of Fingerprint Ridges Us...

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1st International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R)

作者： Dyre, Shoba Sumathi, C. P. SDNB Vaishnav Coll Women Dept Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9789811048593;9789811048586

Thinning of fingerprint ridges plays a vital role in fingerprint identification systems as it simplifies the subsequent processing steps like fingerprint classification and feature extraction. In this paper, we analyze some of the parallel thinning algorithms and have proposed a methodology for skeletonization of fingerprint ridges directly on gray scale images as significant amount of information and features are lost during the binarization process. This algorithm is based on conditionally eroding the gray level ridges iteratively until a one pixel thick ridge is obtained. Refinement procedures have also been proposed to improve the quality of ridge skeleton. Experiments conducted on sample fingerprint images collected using an optical fingerprint Reader exhibit desirable features of the proposed approach.

关键词： Gray scale image Fingerprint Skeletonization parallel algorithm Iterative

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A New parallel algorithm for Monotone Circuit Value Problem with Bounded Genus 7

A New Parallel Algorithm for Monotone Circuit Value Problem ...

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7th International Conference on Information Science and Technology (ICIST)

作者： Lai, Xiaochen Shi, Wenzhe Liu, Xin Fu, Manqing Li, Shouwei Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Dalian 116024 Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China Paderborn Univ Heinz Nixdorf Inst D-33102 Paderborn Germany Paderborn Univ Dept Comp Sci D-33102 Paderborn Germany

ISBN: (纸本)9781509054015

We propose a new complexity class to characterize the efficiently parallelizable parameterized problems called fixed-parameter parallel-tractable (FPPT). Although FPPT = FPP and they can be transformed one another, FPPT is much simpler and reasonable than FPP. The viewpoint of FPPT allows us to concentrate on important intrinsic issues, such as to understand the contribution of each parameter to transfer a problem from NC to P-complete. We also develop a new parallel algorithm for the monotone circuit value problem parameterized by the genus of the underlying graphs, which only based on depth-first search and the PC-tree data structure. Our algorithm implies this problem is in FPPT.

关键词： parameterized complexity monotone circuit value problem parallel algorithm

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Inferring Genome-Wide Gene Regulatory Networks with GPU or CPU parallel algorithm

Inferring Genome-Wide Gene Regulatory Networks with GPU or C...

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International Conference on Computer Network, Electronic and Automation (ICCNEA)

作者： Zheng, Ming Zhang, Shugong Zhuo, Mugui Liu, Guixia Wuzhou Univ Guangxi Coll Wuzhou Peoples R China Wuzhou Univ Univ Key Lab Profess Software Technol Wuzhou Peoples R China Jilin Univ Coll Math Changchun Jilin Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781538639818

Expression of gene block, with the GPU parallel thread structure characteristic calculation, according to the structural characteristics of GPU thread design of double parallel mode, and the use of texture cache memory to achieve high efficiency;on the basis of CPU two level cache capacity of basic blocks further subdivided into sub blocks to improve the cache hit rate, the technology to reduce the number of memory accesses the use of data, reduce the thread migration in the core between the use of thread binding technology;according to the calculated capacity allocation of multi-core CPU and GPU CPU and GPU gene in the mutual information calculation task to calculate the load balance of CPU and GPU;in the design of the new threshold calculation algorithm based on the design and implementation of memory efficient construction of global gene control network CPU /GPU parallel algorithm. The experimental results show that compared with the existing algorithms, this algorithm speed is more obvious, and is able to build more large-scale global gene regulation Control network.

关键词： Genome-wide Gene regulatory network CPU /GPU cooperative computing Efficient access cache parallel algorithm

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parallel SURF algorithm for 3D Reconstruction

Parallel SURF Algorithm for 3D Reconstruction

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2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques(SMONT 2019)

作者： Suping Wu Bing Feng School of Information Engineering Ningxia University

ISBN: (纸本)9781510885455

In this paper, we proposed a parallel SURF algorithm for 3d reconstruction to solve the problem of rapid feature extraction and matching in multi-view 3d reconstruction. SURF algorithm is an effective algorithm for feature extraction. Compared with the classic SIFT algorithm for feature extraction, it has improved somewhat in speed. However, SURF algorithm is still a time-consuming process. In this paper, we improved SURF algorithm. Aiming at the time-consuming problem of feature detection and description, we proposed a parallel algorithm based on multi-core OpenMP and CUDA architecture, and apply it to three-dimensional reconstruction. The experimental results show that the proposed algorithm achieves a certain acceleration ratio under the condition of 100% accuracy compared with the original algorithm.

关键词： SURF parallel algorithm feature extraction OpenMP CUDA 3D reconstruction

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parallel algorithm for short-term time series forecasting on BSN mesh 8

Parallel algorithm for short-term time series forecasting on...

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8th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Gupta, Ashish Sarkar, Bikash Kanti Birla Inst Technol Dept Comp Sci & Engn Ranchi 835215 Bihar India

ISBN: (纸本)9781509030385

Time series forecasting is an efficient approach for future prediction based on past observations. It is useful for real world applications such as, predicting gain, loss and market trends in future, production planning, weather forecasting, flood forecasting, etc. Having larger network size and symmetrical behavior, the recently proposed Bi-Swapped Networks (BSN) is an excellent class of networks for the efficient parallel implementation of such applications in comparison to OTIS networks. It is well accepted that moving average is the best-suited approach for short-term time series forecasting. In this article, we present the parallel mapping of the weighted moving averages of time series forecasting over root n x root n BSN mesh. It requires 5 root n-1 intra-group (electronic) and 1 inter-part (optical) moves. We also present network scalability and compare the mapping of proposed parallel algorithm over BSN mesh with its counterpart OTIS mesh network [6]. The proposed approach claims to be cost effective and demands fewer communication moves.

关键词： Bi-Swapped Networks BSN mesh OTIS Opto-electronic parallel algorithm

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A parallel Joinless algorithm for Co-location Pattern Mining Based on Group-Dependent Shard 19th

A Parallel Joinless Algorithm for Co-location Pattern Mining...

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19th International Conference on Web Information Systems Engineering (WISE)

作者： Yang, Peizhong Wang, Lizhen Wang, Xiaoxuan Fang, Yuan Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Yunnan Peoples R China

ISBN: (纸本)9783030029258;9783030029241

Spatial co-location patterns, whose instances are frequently located together in geography, are particularly valuable for discovering spatial dependencies. Since its inception, lots of co-location pattern mining algorithms have been developed, but the computational cost remains prohibitively expensive with large data size. In this work, we propose to parallelize joinless algorithm on MapReduce framework. Our approach partitions computation in such a way that each machine independently executes joinless algorithm to finish a group of mining tasks. Such partitioning eliminates computational dependencies and reduces communication cost between machines. Moreover, a novel pruning technique is suggested to improve mining performance. The experimental results on synthetic and real-world data sets show that the parallel joinless algorithm is efficient and scalable.

关键词： Spatial data mining Co-location patterns Group-dependent shards parallel algorithm MapReduce

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