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parallel optimization of tree structures for natural language parsing

Journal of Experimental and Theoretical Artificial Intelligence 1991年第1期3卷 69-85页

作者： Sampson, Geoffrey Mewith View Mt. Pleasant Bentham Lancaster LA2 7JY United Kingdom

A recurring problem in cognitive science involves fitting a labelled tree structure to raw data. My APRIL parser analyses natural-language grammar by optimizing a labelled tree over an input string using the stochastic optimization technique of simulated annealing. Stochastic optimization is inherently processingintensive, so it is desirable to speed the process up by exploiting the power of parallel computing;but the task of optimizing a tree structure does not at first sight lend itself to being shared between multiple processors. I define an algorithm which allows this to be done at the cost of requiring individual processors to operate with imperfect knowledge of the current solution;and I describe results of an implementation of this algorithm which suggest that the cost may be worth paying. © 1991 Taylor and Francis Ltd.

关键词： Annealing parser parallel optimization Probabilistic parsing Simulated annealing Stochastic optimization Tree optimization

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parallel optimization Based on Artificial Bee Colony Algorithm 2

Parallel Optimization Based on Artificial Bee Colony Algorit...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Debo Li Yongxin Feng Jun Zhong Jielian Zhou Libao Yin Junhao Zhou Electric Power Research Institute of Guangdong Power Grid Corporation

ISBN: (纸本)9781509036202

This paper aims to tackle the shortcomings of the standard artificial bee colony algorithm(ABC) such as slow convergence,long solving time and being easy to fall into local *** study the state transformation formula and propose a parallelized ABC algorithm with Message Passing Interface(MPI).We use the traveling salesman problem(TSP) as the case *** experiments show that the parallel ABC algorithm has an advantage in speed over the standard algorithm *** and convergence speed.

关键词： artificial bee colony algorithm traveling salesman problem parallel optimization combinatorial optimization swarm intelligence

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parallel optimization OF HYPERSPECTRAL UNMIXING BASED ON SPARSITY CONSTRAINED NONNEGATIVE MATRIX FACTORIZATION

PARALLEL OPTIMIZATION OF HYPERSPECTRAL UNMIXING BASED ON SPA...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wu, Zebin Ye, Shun Wei, Jie Liu, Jianjun Wei, Zhihui Sun, Le NJUST Sch Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China NJUST Lianyungang Res Inst Lianyungang 222006 Peoples R China Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing 210094 Peoples R China

ISBN: (纸本)9781479911141

Hyperspectral unmixing is a typical problem of blind source separation, which can be solved by nonnegative matrix factorization (NMF). Sparsity based NMF will increase the efficiency of unmixing, but its computational complexity limits the possibility of utilizing it in time-critical applications. In this paper, method of parallel hyperspectral unmixing based on sparsity constrained nonnegative matrix factorization on Graphics Processing Units (CSNMF-GPU) is investigated and compared in terms of both accuracy and speed. The realization of the proposed method using Compute Unified Device Architecture (CUDA) on GPU are described and evaluated. The experimental results comparing with the serial implementations based on both simulated and real hyperspectral data demonstrate the effectiveness of the proposed parallel optimization approach.

关键词： hyperspectral unmixing parallel optimization sparsity nonegative matrix factorization

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parallel optimization Strategy of Heap Sort Algorithm under Multi-core Environment 7

Parallel Optimization Strategy of Heap Sort Algorithm under ...

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Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2015)

作者： Wei Zhenhua Liu Zhifeng Li Guoliang East China Inst Technol Minist Educ Engn Res Ctr Nucl Technol Applicat Nanchang 330013 Jiangxi Peoples R China East China Inst Technol Sch Software Nanchang 330013 Jiangxi Peoples R China

ISBN: (纸本)9781467371438

With the popularity of multi-core and many-core platform, multi-core-oriented parallel programming and optimization has become a research hotspot in computer field. However, the vast majority of programmers are still continuing the traditional serial programming habits. Meanwhile, the mainstream algorithms are still in serial. Therefore, how to parallelize the serial programs effectively and write the multi-core programs efficiently is becoming an urgent problem in the field of multi-core programming. In this paper, the optimization of the heap sort algorithm has been achieved adopting the parallel technology based on openMP. Firstly, based on the hypercube model, the heap data to be sorted are divided into n (n is the computer thread amount) blocks called sub-heap which will be sorted respectively. Thus, the scale of the problem is decomposed into 1/n;secondly, the sorted sub-heaps will be merged using merge sort algorithm;finally, the parallel optimized heap sort algorithm is compared to the traditional heap sort algorithm. When the data quantity comes up to ten million bytes, the execution efficiency of the optimized parallel heap sort algorithm of this paper is improved significantly compared with the traditional serial heap sort algorithm on the experimental computer. Furthermore, the efficiency gap between the two algorithms will be intensified prominently with the increasing data quantity.

关键词： Multi-core OpenMP Heap Sort Algorithm Hypercube Model parallel optimization

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parallel optimization of O (1) Rectangular Kernel Max/Min Filters Algorithm 11

Parallel Optimization of O (1) Rectangular Kernel Max/Min Fi...

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11th International Conference on Graphics and Image Processing (ICGIP)

作者： Hu, Tao Chen, Quanyi Ren, Xianyi Wang, Changwei Shenzhen Inst Informat Technol Shenzhen Peoples R China Jiangxi Univ Sci & Technol Ganzhou Peoples R China

ISBN: (纸本)9781510635241

The max/min filters are the most basic operator in gray-scale morphology. van Herk proposed an efficient O(1) rectangular kernel max/min filters algorithm which is independent of the size of filter window. This paper mainly studies the parallel optimization of van Herk's max/min filters algorithm by using the single-instruction, multiple data (SIMD) technology and OpenMP parallel optimization technology for multi-core processors. First, the Intel Sreaming SIMD Extensions 2 (SSE2) is used to perform data-level parallel optimization for van Herk's max/min filters algorithm in column direction, and a method based on histogram statistics is proposed to accelerate the calculation in row direction, the parallel speed-up ratio can reach 2. Then, the OpenMP parallel optimization technology is used to further perform thread-level parallel optimization for the SSE2 optimized version. The total speedup ratio under 4 cores and 4 threads CPU is about 7. Finally, compared with the rectangular kernel gray morphological operators in the powerful machine vision software MVTec HALCON17.12, the optimized algorithm in this paper has a great advantage in large window filtering size.

关键词： SIMD SSE2 OpenMP parallel optimization Max/Min filters gray-scale morphology

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parallel optimization of Relion: Performance Comparison based on Cluster for CPU/GPU and KNL 18

Parallel Optimization of Relion: Performance Comparison base...

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2nd International Conference on Algorithms, Computing and Systems (ICACS)

作者： Zhou, Heng Ni, FuChuan Zhao, Liang Zheng, Fang Huazhong Agr Univ Coll Informat Wuhan Hubei Peoples R China

ISBN: (纸本)9781450365093

Relion is the 3D reconstruction program with the Bayes algorithm of electron cryo-microscope (cryo-EM) data. We analyzed the characteristics of the Relion program, and designed a parallelization scheme. We use the optimization methods commonly used in the code optimization for Relion programs, such as memory access optimization, multithread optimization, vectorization and conversing coarse grained parallel to fine-grained parallelism. Finally, the overall running time of the entire Relion program was reduced by 379s. At the same time, we tested the program on GPU and KNL platform and compared the results of the Relion program on the KNL cluster platform and the GPU cluster platform. The results show that the optimization effect of Relion on the GPU platform is better than KNL.

关键词： Relion KNL cluster CPU/GPU cluster parallel optimization

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Video Colorization Using parallel optimization in Feature Space

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2014年第3期24卷 407-417页

作者： Sheng, Bin Sun, Hanqiu Magnor, Marcus Li, Ping Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Braunschweig Univ Technol Comp Graph Lab D-38106 Braunschweig Germany

We present a new scheme for video colorization using optimization in rotation-aware Gabor feature space. Most current methods of video colorization incur temporal artifacts and prohibitive processing costs, while this approach is designed in a spatiotemporal manner to preserve temporal coherence. The parallel implementation on graphics hardware is also facilitated to achieve realtime performance of color optimization. By adaptively clustering video frames and extending Gabor filtering to optical flow computation, we can achieve real-time color propagation within and between frames. Temporal coherence is further refined through user scribbles in video frames. The experimental results demonstrate that our proposed approach is efficient in producing high-quality colorized videos.

关键词： Gabor feature space parallel optimization user strokes video colorization

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Energy-Saving Operation Synergy for Multiple Metro-Trains Using Map-Reduce parallel optimization

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第2期71卷 1319-1332页

作者： Shang, Mengying Zhou, Yonghua Fujita, Hamido Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Iwate Prefectural Univ Reg Res Ctr Takizawa 0200193 Japan Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18011 Spain

In subway systems, increasing attention has been paid to improve energy efficiency due to remarkable energy consumption for rail transportation operation. The optimization of single-train driving control and multi-train operation synergy are two critical and closely related aspects for energy saving, but they are rarely solved in an integrative manner. The comprehensive study on these two aspects is a complex, large-scale, multi-variable and constrained task, which involves nested, coupled and mutual-input optimization processes. This paper proposes an energy-efficient integrated model, which includes optimizing single-train operation control in an inner model and multi-train operation synergy, committing to the utilization of regenerative braking energy (RBE) in an outer model. A parallelized particle swarm optimization based on Map-Reduce (PPSO-MR) algorithm is proposed to solve the integrated model to reduce the execution time. Two concepts of temporal-spatial record and time shift are introduced to derive the movement of a train at inter-stations for the operation adjustment of multiple trains on a subway line. Experimental results show that the proposed control model solved by the holistic optimization approach can achieve lower energy consumption with fast execution time. This study provides the novel model and algorithm to obtain energy-saving schedules serving the multi-train operation coordination.

关键词： optimization Schedules Energy consumption Switches Public transportation Mathematical models Energy efficiency Energy saving regenerative braking multi-vehicle coordination parallel optimization

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Fully parallel optimization of Coordinated Electricity and Natural Gas Systems on High-Performance Computing

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IEEE TRANSACTIONS ON SMART GRID 2023年第5期14卷 3499-3511页

作者： Gong, Lin Peng, Yehong Zhang, Chenxu Fu, Yong Jackson State Univ ECE & CS Dept Jackson MS 39217 USA Mississippi State Univ ECE Dept Starkville MS 39759 USA

Intensified interactions between electric power and natural gas infrastructures raise significant demands to coordinate their system operations. This paper proposes a fully parallel optimization method that can achieve a rapid decision-making on the day-ahead coordinated operation of electricity and natural gas systems on the high-performance computing (HPC) platform. The proposed method can flexibly tailor decomposition strategies to solve the optimization problem according to unique features of problem models in both electric power and natural gas systems. Particularly, the operation problem of power system is split by function and time into numbers of singe-unit subproblems and single-period network subproblems, while the operation problem of natural gas system is decomposed into multiple area subproblems. All the scalable subproblems are solved and coordinated quickly in a fully parallel manner on HPC for improving computational efficiency and tractability of complex electricity-gas co-optimization problem. By optimally coordinate electricity and natural gas systems under different operating conditions, the proposed method could improve the energy economics as well as the system resilience to various outages due to extreme events. Numerical results demonstrate the effectiveness and efficiency of our proposed co-optimization method and its HPC implementation.

关键词： Natural gas Power systems Computational modeling Generators Systems operation Meteorology parallel processing Electric power and natural gas systems high-performance computing parallel optimization

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A parallel optimization Algorithm based on FANOVA Decomposition

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2014年第7期30卷 961-974页

作者： Ivanov, Momchil Kuhnt, Sonja TU Dortmund Univ D-44221 Dortmund Germany Dortmund Univ Appl Sci & Arts D-44227 Dortmund Germany

The analysis and modeling of complex industrial processes, like the forming of car parts, is often performed with the help of computer simulations. optimization of such computer experiments usually relies on metamodel-based sequential strategies. The existing sequential algorithms, however, share the limitation that they only allow a single simulation at a time. In this article, we present a very elegant way to produce a parallel optimization procedure, based on a technique from the sensitivity analysis toolbox-the functional analysis of variance graph. The proposed novel simultaneous optimization scheme is called the ParOF algorithm. It is compared with a very effective black-box procedure-the well known efficient global optimization (EGO) algorithm, based on analytical test cases and an optimization study of a sheet forming simulation. Besides demonstrating the advantages of our parallel optimization method, the results show that it can successfully be applied to sheet metal forming for the purpose of quality improvement of the ready parts. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： parallel optimization FANOVA decomposition simulations deep drawing process metamodels

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