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A review of parallel computing applications in calibrating watershed hydrologic models

ENVIRONMENTAL MODELLING & SOFTWARE 2022年第0期151卷 1页

作者： Asgari, Marjan Yang, Wanhong Lindsay, John Tolson, Bryan Dehnavi, Maryam Mehri Univ Guelph Dept Geog Environm & Geomat Guelph ON Canada Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada Univ Toronto Dept Comp Sci Toronto ON Canada

In recent decades, parallel computing has been increasingly applied to address the computational challenges of calibrating watershed hydrologic models. The purpose of this paper is to review these parallelization studies to summarize their contributions, identify knowledge gaps, and propose future research directions. These studies parallelized models based on either random-sampling-based algorithms or optimization algorithms and demonstrated considerable parallel speedup gain and parallel efficiency. However, the speedup gain/efficiency decreases as the number of parallel processing units increases, particularly after a threshold. In future, various combinations of hydrologic models, optimization algorithms, parallelization strategies, parallelization architectures, and communication modes need to be implemented to systematically evaluate a suite of parallelization scenarios for improving speedup gain, efficiency, and solution quality. A standardized suite of performance evaluation metrics needs to be developed to evaluate these parallelization approaches. Interactive multiobjective optimization algorithms and/or integrated sensitivity analysis and calibration algorithms are potential future research fields, as well.

关键词： parallel computing Hydrologic model calibration Optimization algorithms parallel speedup parallel efficiency

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A parallel computing simulation-based multi-objective optimization framework for economic analysis of building energy retrofit: A case study in Iran

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JOURNAL OF BUILDING ENGINEERING 2022年 45卷

作者： Tavakolan, Mehdi Mostafazadeh, Farzad Eirdmousa, Saeed Jalilzadeh Safari, Amir Mirzaei, Kaveh Univ Tehran Sch Civil Engn Dept Construct Engn & Management Fac Engn POB 11155-4563 Tehran Iran

The building sector represents a large share of rising global energy demand. Improving energy efficiency in existing building stock is a crucial strategy. Adopting the best energy retrofitting strategy in a specific building is a challenging task due to a plethora of possible combinations of retrofit measures and mutually contrasting objective functions. In addition, peculiar conditions of Iran, such as extremely subsidized energy prices, and step utility tariffs, escalate the challenges of building energy retrofit. Accordingly, the current study presents a simulation-based multi-objective optimization framework characterized by parallel processing structure and results-saving archive. The framework is implemented by integrating MATLAB (R) as an optimization engine with EnergyPlus as a dynamic energy simulator to minimize primary energy consumption and discounted payback period while maximizing the net present value. The algorithm explores a vast domain of possible solutions including, building envelope, cooling and heating systems, and renewable energy sources. The framework is applied to a single-family residence located in Iran. Three different scenarios are examined with reference to prospective energy pricing policies to evaluate their effect on the attractiveness of energy retrofit projects. For each scenario, final solutions are selected from respective Pareto fronts according to cost-optimality and energyefficiency criteria and considering budget constraints. The results indicate that even though significant reductions in primary energy consumption can be achieved, implementing energy retrofit under the current energy pricing policy in Iran would not yield economic benefits. However, the elimination of subsidies along with offering incentives for building energy retrofits presents promising outcomes.

关键词： Building energy retrofit Multi-objective optimization parallel computing Energy efficiency Genetic algorithm Energy pricing policy

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Development and verification of Geant4-based parallel computing Monte Carlo simulations for nuclear logging applications

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ANNALS OF NUCLEAR ENERGY 2022年 172卷

作者： Wang, Yang Liang, Jingang Zhang, Qiong Wang, Xinyang Tang, Wei Chen, Ye Univ Elect Sci & Technol China Chengdu Peoples R China Tsinghua Univ Beijing Peoples R China KEK High Energy Accelerator Res Org Japan Tsukuba Ibaraki Japan

Monte Carlo is one of the most common methods to solve radiation transport problems. However, due to its statistical nature, Monte Carlo is time-consuming when applied to large-scale problems. Therefore, parallel computing, a powerful tool in accelerating Monte Carlo calculations, has been receiving increasing attention along with new developments of computing hardware in recent years. This work documents the application of parallel computing in Geant4 simulations pertaining to nuclear logging tools. A user-specific interface is developed to enable Geant4 multithreading functionality for two nuclear logging models and the parallel Monte Carlo performance is evaluated on a designated HPC (high performance computing) platform. Upon the validation of the parallel-mode simulation, the performance of the Geant4-based simulation in multi-threaded mode (G4-MT) and with the G4-MPI native interface to MPI are compared to obtain the best combination of threads and processes on each node. Strong and weak scalabilities on multiple threads are then investigated by computing the speedup and the parallel efficiency of the simulation. The results show that combining multi-threaded and MPI-based execution can significantly reduce execution time in parallel mode. And the paper documents the improvement of Monte Carlo simulation efficiency by optimizing the configuration of computational resources for parallel execution while keeping close-to-linear acceleration.

关键词： parallel computing Geant4 Nuclear logging tool

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parallel computing for genome sequence processing

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BRIEFINGS IN BIOINFORMATICS 2021年第5期22卷 bbab070-bbab070页

作者： Zou, You Zhu, Yuejie Li, Yaohang Wu, Fang-Xiang Wang, Jianxin Cent South Univ Sch Comp Sci & Engn Hunan Prov Key Lab Bioinformat Changsha 410083 Hunan Peoples R China Old Dominion Univ Comp Sci Norfolk VA 23529 USA Univ Saskatchewan Coll Engn Saskatoon SK Canada Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada Cent South Univ Sch Comp Sci & Engn Changsha Hunan Peoples R China

The rapid increase of genome data brought by gene sequencing technologies poses a massive challenge to data processing. To solve the problems caused by enormous data and complex computing requirements, researchers have proposed many methods and tools which can be divided into three types: big data storage, efficient algorithm design and parallel computing. The purpose of this review is to investigate popular parallel programming technologies for genome sequence processing. Three common parallel computing models are introduced according to their hardware architectures, and each of which is classified into two or three types and is further analyzed with their features. Then, the parallel computing for genome sequence processing is discussed with four common applications: genome sequence alignment, single nucleotide polymorphism calling, genome sequence preprocessing, and pattern detection and searching. For each kind of application, its background is firstly introduced, and then a list of tools or algorithms are summarized in the aspects of principle, hardware platform and computing efficiency. The programming model of each hardware and application provides a reference for researchers to choose high-performance computing tools. Finally, we discuss the limitations and future trends of parallel computing technologies.

关键词： parallel computing multi-core computing cluster computing heterogeneous computing genome sequence processing

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SIFT-aided path-independent digital image correlation accelerated by parallel computing

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OPTICS AND LASERS IN ENGINEERING 2020年第0期127卷 105964-000页

作者： Yang, Junrong Huang, Jianwen Jiang, Zhenyu Dong, Shoubin Tang, Liqun Liu, Yiping Liu, Zejia Zhou, Licheng South China Univ Technol Sch Civil Engn & Transportat State Key Lab Subtrop Bldg Sci Guangzhou 510640 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Peoples R China

Current iterative digital image correlation (DIC) algorithms can efficiently converge at the deformation vector with high accuracy when they are fed with reliable initial guess. Thus, the adaptability of DIC method is dominated to a large extent by the estimation of initial guess. In recent years, image feature-based technique, especially the scale-invariant feature transform (SIFT), was introduced to DIC for the estimation of initial guess in the case of large and complex deformation, due to its robustness in handling the images with translation, rotation, scaling, and localized distortion. However, feature extraction and matching in SIFT are very time consuming, which limits the applications of the SIFT-aided DIC. In this study, we developed a SIFT-aided path-independent DIC method and accelerated it by introducing the parallel computing on graphics processing unit (GPU) or multi-core CPU. In our method, SIFT features are used to estimate the initial guess for the inverse compositional Gauss-Newton (IC-GN) algorithm at each point of interest (POI). The experimental study shows that the developed method can deal with large and inhomogeneous deformation with high accuracy. parallel computing (especially on GPU) accelerates significantly the proposed DIC method. The achieved computation speed satisfies the need for real-time processing with high resolution for the images of normal sizes.

关键词： Digital image correlation Initial guess Scale-invariant feature transform parallel computing Graphics processing unit

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A collective entity linking algorithm with parallel computing on large-scale knowledge base

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JOURNAL OF SUPERcomputing 2020年第2期76卷 948-963页

作者： Xia, Yingchun Wang, Xingyue Gu, Lichuan Gao, Qijuan Jiao, Jun Wang, Chao Anhui Agr Univ Sch Informat & Comp Hefei Peoples R China Minist Agr Key Lab Agr Elect Commerce Hefei 230036 Peoples R China

Entity linking is a central concern of automatic knowledge question answering and knowledge base population. Traditional collective entity linking approaches only consider one of the entity contexts or semantic relations between entities. Thus, these approaches always have poor performance on Web documents. The efficiency of collective entity linking needs to be improved as well. This paper proposes a collective entity linking algorithm based on topic model and graph. Constructing the topic model can represent mentions and candidate entities by using topic distributions. It makes full use of context in documents. Entity semantic relations are represented by document similarities which are computed through the topic model. parallel computing is used to reduce long running time which is caused by topic model construction. Entity graph is constructed according to the relations between entities in the knowledge graph. Hypertext-Induced Topic Search exploits the entity graph to compute hub value and authority value of candidate entities. And the authority value is the basis for entity linking. Experimental results on open-domain corpus (NLPCC2014) demonstrate the validity of the proposed method. Experimental results show that the proposed approach has 5.2% improvement in F-1-measure than AGDISTIS on corp NLPCC2014.

关键词： Collective entity linking Topic model Graph parallel computing

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Monitoring and prediction of big process data with deep latent variable models and parallel computing

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JOURNAL OF PROCESS CONTROL 2020年 92卷 19-34页

作者： Yang, Zeyu Ge, Zhiqiang Zhejiang Univ Inst Ind Proc Control Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

Process monitoring and quality prediction are crucial for maintaining favorable operating conditions and have received considerable attention in previous decades. For majority complicated cases in chemical and biological industrial processes with particular nonlinear characteristics, traditional latent variable models, such as principal component analysis (PCA), principal component regression (PCR), partial least squares (PLS), may not work well. In this paper, various nonlinear latent variable models based on autoencoder (AE) are developed. In order to extract deeper nonlinear features from process data, the basic shallow AE models are extended to the deep latent variable models, which provides a deep generative structure for nonlinear process monitoring and quality prediction. Meanwhile, with the ever increasing scale of industrial data, the computational burden for process modeling and analytics has becoming more and more tremendous, particularly for large-scale processes. To handle the big data problem, the parallel computing strategy is further applied to the above model, which partitions the whole computational task into a few sub-tasks and assigns them to parallel computing nodes. Then the parallel models are utilized for process monitoring and quality prediction applications. The effectiveness of the developed methods are evaluated through the Tennessee Eastman (TE) benchmark process and a real-life industrial process in an ammonia synthesis plant (ASP). (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Big process data Process monitoring Quality prediction Deep latent variable models parallel computing

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A global optimization strategy based on the Kriging surrogate model and parallel computing

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2020年第1期62卷 405-417页

作者： Xing, Jian Luo, Yangjun Gao, Zhonghao Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Sch Aeronaut & Astronaut Dalian 116024 Peoples R China

The computational efficiency and accuracy of the global solution are the main performance indicators of an optimization algorithm to solve the structural and multidisciplinary optimization problems. The Kriging-based optimization algorithm can satisfy certain engineering requirements by applying the single-point sequence sampling method. However, this conventional algorithm does not efficiently apply the parallel performance of high-performance multi-core computers. This study aims to propose a global optimization strategy based on the Kriging surrogate model and parallel computing depending on the multi-peak characteristics of the expect improvement (EI) function. The proposed method searches out the locations of multiple peaks of the EI function by introducing a so-calledP-EI function;it then simultaneously includes multiple sampling points located nearby these peaks. Furthermore, an efficient design domain reduction technique is applied to improve the accuracy of the global solution. When compared with the traditional Kriging-based methods, the proposed method can effectively obtain multiple peaks of the EI function without solving complex expressions of the high-dimensional EI function exhibiting a joint probability density distribution. The parallel computation ability, global performance, and solution accuracy of our method are validated via typical test functions and structural optimization problems.

关键词： Global optimization parallel computing Multi-point sampling Surrogate model

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An explicit asynchronous step parallel computing method for finite element analysis on multi-core clusters

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ENGINEERING WITH COMPUTERS 2020年第2期36卷 443-453页

作者： Ma, Zhiqiang Lou, Yunfeng Li, Junjie Jin, Xianlong Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

The finite element analysis of complex structure often requires a refine mesh in some local domain. To reduce the computation time, an explicit asynchronous step parallel computing method is proposed. The domain decomposition with overlapping node method is used to divide the model into different subdomains. The multiple overlapping nodes between different subdomains constitute the coupling region. Each subdomain selects the time step based on the mesh characteristics. The subcycling method is adopted to tackle the matching of asynchronous step boundary. The subdomain model, boundary information and calculation results are stored in parallel files which mean the overall finite element analysis process is implemented in parallel. The validity and efficiency of the proposed method are verified through three simulation cases which conducted on Tianhe 2 multi-core supercomputers. The results of simulation cases show that the proposed method has a higher accuracy than classic subcycling method under the same time step. The total speedup of the algorithm relates to the step ratios between subdomains, the number of subdomain and the load balance. This approach offers an efficient way to solve large-scale and super-scale structural dynamics analysis with local refine mesh.

关键词： Explicit integration Asynchronous step parallel computing Multiple overlapping node Multiple files

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Method for real-time simulation of haptic interaction with deformable objects using GPU-based parallel computing and homogeneous hexahedral elements

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COMPUTATIONAL MECHANICS 2020年第5期65卷 1205-1218页

作者： Byeon, Seong Pil Lee, Doo Yong Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon South Korea

This paper proposes a method for simulating real-time haptic interaction with deformable objects. The deformable model consists of regular hexahedrons of a single type. This homogeneity is exploited to improve the efficiency in deformation computations. Model boundaries are approximated using a moving-least-squares function reflecting the deformation results of the hexahedrons. A method for adaptively approximating the model boundaries is presented for efficient collision handling in the haptic loop. The proposed method can simulate a model of 16,481 nodes in less than 1 ms, which is a significant improvement over the previous methods in the literature. Small gap between the model boundary and the hexahedrons can cause errors in the proposed method. Numerical examples considering the characteristics of human tissues show that the errors are less than just-noticeable difference of human.

关键词： Haptic simulation Interactive simulation parallel computing Finite-element method Deformable object Physics-based model

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