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Theoretical Basis of Mathematical Apparatus for parallel computing Implementation in Computer-Aided Design Systems

PROGRAMMING AND COMPUTER SOFTWARE 2024年第5期50卷 335-342页

作者： Konopatskiy, E. Nizhny Novgorod State Univ Architecture & Civil En 65 Iljinskaya st Nizhnii Novgorod 603000 Russia

The purpose of this work is to develop a mathematical apparatus and computational algorithms for implementation of parallel computing in geometric modeling and computer-aided design (CAD) systems. The analysis of existing approaches to parallel computing implementation in CAD systems is carried out. As a result, it is found that most information modeling and CAD systems do not support parallel computing at the level of the geometric kernel. A concept for the development of a CAD geometric kernel based on the invariants of parallel projection of geometric objects onto the axes of the global coordinate system is proposed. It combines the potential of constructive methods for geometric modeling, capable of parallelizing geometric constructions by tasks (message passing), and the mathematical apparatus of point calculus, capable of parallelization by data through coordinate-by-coordinate calculation (data parallel). The use of the coordinate-by-coordinate calculation for point equations not only makes it possible to parallelize computations along coordinate axes, but also ensures the consistency of computational operations with respect to threads, which significantly reduces the idle time and optimizes the CPU operation to achieve the maximum effect from the use of parallel computing.

关键词： CAD mathematical apparatus parallel computing point calculus coordinate-by-coordinate calculation coordinate vector invariants of parallel projection and hidden parallelism

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Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

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JOURNAL OF HYDROLOGY 2024年 636卷

作者： Zhang, Rongqi Zhang, Shanghong Wen, Xiaoxiong Yue, Ziqi Zhou, Yang North China Elect Power Univ Sch Water Resources & Hydropower Engn Beijing Peoples R China

The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.

关键词： Reservoir Optimization Scheduling Three Gorges Reservoir Genetic Algorithm parallel computing OpenMP

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Train/track coupled dynamics model of long heavy haul train based on substructure and parallel computing

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT 2024年第5期238卷 463-478页

作者： Wang, Qi Xiao, Xin-Biao Wang, Jia-Nuo Wang, Wei Yang, Yi Jin, Xue-Song Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Jiulidi Campus Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu Peoples R China Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Peoples R China

To consider the coupled effect on the running safety between elastic track and longitudinal impulse of Long Heavy Haul Train(LHHT), a train/track coupled dynamics model is established by using connection substructure theory. The ballasted track is divided into several segments called sub-tracks: a sub-track includes rail, sleepers and ballast. In the sub-track model, the sleepers and ballast are modelled as lumped mass. The rail is divided into the contact and connection rail. The contact rail is modelled as an Euler beam to reflect the wheel/rail interaction and the flexible vibration of the rail. The connection rail is modelled as a super element to reflect the interaction between adjacent contact rail. To increase the simulation speed, a new parallel computing method is proposed: a train/track coupled dynamics model is divided into different submodule, a submodule includes a sub-track and a vehicle on the sub-track. A submodule is calculated by a single computer core. The submodule is connected by connection rail, couplers and ballast. The advantage of this parallel method is that the load of each computer core is almost uniform. The simulation speed depends on the number of parallel computing cores instead of one core with a particularly large load. Finally, taking the 10,000-ton train as an example, the distribution of coupler force, the derailment coefficient and wheel unloading rate are given during the train braking on a curve, which shows the application and necessity of the train/track coupled dynamics model based on substructure and parallel computing.

关键词： Long heavy haul train train/track coupled dynamics model substructure method parallel computing dynamic response

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A novel combining method of dynamic and static web crawler with parallel computing

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第21期83卷 60343-60364页

作者： Liu, Qingyang Yahyapour, Ramin Liu, Hongjiu Hu, Yanrong Georg August Univ Gottingen Inst Informat Wilhelmspl 1 Aula D-37077 Gottingen Germany Zhejiang A&F Univ Coll Math & Comp Sci Hangzhou 311300 Peoples R China

Recovering information from a targeted website that undergoes dynamic changes is a complicated undertaking. It necessitates the use of a highly efficient web crawler by search engines. In this study, we merged two web crawlers: Selenium with parallel computing capabilities and Scrapy, to gather electron molecular collision cross-section data from the National Fusion Research Institute (NFRI) database. The method effectively combines static and dynamic web crawling. The primary challenges lie in the time-consuming nature of dynamic web crawling using Selenium and that Scrapy's limited support for parallel computing within the "download middleware". Nevertheless, this combined approach proves exceptionally well-suited for the task of data extraction from an online database, which comprises multiple web pages with unchanging URLs when specific keywords are submitted. We applied natural language processing techniques to identify species and dissect reaction formulas into various states. Employing these methodologies, we extracted a total of 76,893 data points pertaining to 112 species. These data pieces offer intricate insights into the processes unfolding within the plasma, all collected within a span of ten minutes. When compared to traditional web crawling methods, our approach boasts a speed advantage of roughly 100 times faster than dynamic web crawlers and exhibits greater flexibility than static web crawlers. In this report, we present the retrieved results, encompassing reaction formulas, reference information, species metadata, and time comparison among various methods.

关键词： Web crawling Dynamic Static Natural language processing parallel computing

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Make optical transmission quality prediction more effective: A parallel computing view

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OPTICS COMMUNICATIONS 2024年 571卷

作者： Sun, Xiaochuan Yang, Shuohan Han, Jinpeng Li, Yingqi Meng, Qinghong North China Univ Sci & Technol Coll Artificial Intelligence 21 Bohai Rd Tangshan 063210 Hebei Peoples R China Hebei Key Lab Ind Intelligent Percept Tangshan 063210 Hebei Peoples R China

In optical networks, ensuring high quality of transmission (QoT) is essential to prevent degradation of optical signals, especially when the signal strength falls below a specified threshold. While machine learning (ML) is widely used for QoT prediction, predicting QoT accurately for large-scale optical links presents challenges. Traditional serial methods often result in high latency and decreased processing efficiency of optical channels. To solve this problem, this paper proposes a Dask-based P-FEDformer approach. Initially, a FEDformer-based predictor is constructed, and then QoT prediction for multiple channels is realized under the Dask parallel architecture. To enhance model prediction accuracy, wavelet decomposition technique is employed. Simulation results demonstrate the method's effectiveness in handling large amount of data with a 60% improvement in time efficiency compared to serial execution, while maintaining accurate QoT prediction.

关键词： Dask P-FEDformer parallel computing Quality of transmission prediction

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Enhancing virtual flow metering on offshore oil platforms through parallel computing and data reconciliation

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 235卷

作者： Rabello, Gildeir L. Andrade, Gabriel M. P. de Menezes, Diego Q. F. Soares, Rafael M. Lemos, Tiago S. M. Ribeiro, Leonardo D. Vieira, Bruno F. Pinto, Jose Carlos Univ Fed Rio de Janeiro Programa Engn Quim COPPE BR-21941972 Rio De Janeiro Brazil Univ Fed Rio de Janeiro Programa Posgrad Engn Proc Quim & Bioquim Escola Quim BR-21941909 Rio De Janeiro Brazil Ctr Pesquisas Leopoldo Americo Miguez Mello CENPE Petrobras Petr Brasileiro SA BR-21941915 Rio De Janeiro Brazil Gestao Parcerias & Proc Exploracao & Prod GPP E&P Petrobras Petr Brasileiro SA BR-20231030 Rio De Janeiro Brazil

Technological advances in recent years have facilitated data accessibility and increased computational resources;consequently, more remarkable numerical performances in data analysis and computational tests have become possible. In this context, parallel computing techniques are considered relevant tools in applications necessitating reduced computational time, such as online and real -time monitoring of industrial processes. For these reasons, the present work develops and implements online and real -time monitoring of offshore oil and gas production, using parallel computing to improve the performance of the data reconciliation methodology implemented, which will estimate the virtual flow metering with more reliability and in real time. To achieve this, three optimization methods (DA, L-BFGS-B, and SLSQP) and two approaches of parallelization schemes were considered: (1) parallelization of the simulations of each production well;(2) parallelization of the numerical gradient, performed by the deterministic optimization method. parallel implementations allowed the reduction of up to 80% (type 1) and up to 89% (type 2) of the simulation time compared to their serial counterparts. Hence, the parallel execution of type 2 exhibited more promising results, while type 1 demonstrated greater adaptability to optimization methods. Finally, the results indicate that parallel computing techniques facilitate data reconciliation in real -time, enabling online monitoring in a much shorter time and more accurately than achieved with similar sequential procedures. Moreover, the implemented methodology has proven effective for virtual flow metering of each well's flow rates of oil, water, and gas. In this context, parallelism techniques present the potential for developing the global monitoring of the virtual flow meters for real -time oil, water, and gas flow rates in each well.

关键词： Virtual flow metering Data reconciliation Real-time monitoring parallel computing Oil production

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Adaptive density-based robust topology optimization under uncertain loads using parallel computing

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ENGINEERING WITH COMPUTERS 2024年第1期40卷 21-43页

作者： Herrero-Perez, David Pico-Vicente, Sebastian Gines Martinez-Barbera, Humberto Tech Univ Cartagena Struct Construct & Graph Express Campus Muralla Mar Murcia 30202 Spain Univ Murcia Dept Informat & Commun Engn Campus Espinardo Murcia 30100 Murcia Spain

This work presents an efficient parallel implementation of density-based robust topology optimization (RTO) using adaptive mesh refinement (AMR) schemes permitting us to address the problem with modest computational resources. We use sparse grid stochastic collocation methods (SCMs) for transforming the RTO problem into a weighted multiple-loading deterministic problem at the collocation points. The calculation of these deterministic problems and the functional sensitivity is computationally expensive. We combine distributed-memory parallel computing and AMR techniques to address the problem efficiently. The former allows us to exploit the computational resources available, whereas the latter permits us to increase performance significantly. We propose the parallel incremental calculation of the deterministic problems and the contribution to the functional sensitivity maintaining a similar memory allocation to the one used in the deterministic counterpart. The cumulative computing uses buffers to adapt the evaluation at the collocation points to the parallel computing resources permitting the exploitation of the embarrassing parallelism of SCMs. We evaluate the deterministic problems in a coarse mesh generated for each topology optimization iteration to increase the performance. We perform the regularization and design variable update in a fine mesh to obtain an equivalent design to the one generated in such a mesh. We evaluate the proposal in two- and three-dimensional problems to test its feasibility and scalability. We also check the performance improvement using computational buffers in parallel computing nodes. Finally, we compare the proposal to the same approach using different preconditioners without AMR schemes showing significant performance improvements.

关键词： Robust topology optimization Sparse grid Adaptivity parallel computing Multigrid methods

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FEniTop: a simple FEniCSx implementation for 2D and 3D topology optimization supporting parallel computing

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2024年第8期67卷 140-140页

作者： Jia, Yingqi Wang, Chao Zhang, Xiaojia Shelly Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Natl Ctr Supercomp Applicat Urbana IL 61801 USA

Topology optimization has emerged as a versatile design tool embraced across diverse domains. This popularity has led to great efforts in the development of education-centric topology optimization codes with various focuses, such as targeting beginners seeking user-friendliness and catering to experienced users emphasizing computational efficiency. In this study, we introduce FEniTop, a novel 2D and 3D topology optimization software developed in Python and built upon the open-source FEniCSx library, designed to harmonize usability with computational efficiency and post-processing for fabrication. FEniTop employs a modular architecture, offering a unified input script for defining topology optimization problems and six replaceable modules to streamline subsequent optimization tasks. By enabling users to express problems in the weak form, FEniTop eliminates the need for matrix manipulations, thereby simplifying the modeling process. The software also integrates automatic differentiation to mitigate the intricacies associated with chain rules in finite element analysis and sensitivity analysis. Furthermore, FEniTop provides access to a comprehensive array of readily available solvers and preconditioners, bolstering flexibility in problem-solving. FEniTop is designed for scalability, furnishing robust support for parallel computing that seamlessly adapts to diverse computing platforms, spanning from laptops to distributed computing clusters. It also facilitates effortless transitions for various spatial dimensions, mesh geometries, element types, and orders, and quadrature degrees. Apart from the computational benefits, FEniTop facilitates the automated exportation of optimized designs, compatible with open-source ParaView software for post-processing. This functionality allows for visualizing optimized designs across diverse mesh geometries and element shapes, automatically smoothing 3D designs, and converting smoothed designs into STereoLithography (STL) files for 3D

关键词： Topology optimization parallel computing Automatic differentiation Weak form FEniCSx Python 3D printing

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A novel parallel computing framework for traffic assignment problem: Integrating alternating direction method of multipliers with Jacobi over relaxation method

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 189卷

作者： Liu, Zhiyuan Dong, Yu Zhang, Honggang Zheng, Nan Huang, Kai Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Sch Transportat Jiangsu Key Lab Urban ITS Nanjing Peoples R China Minist Transport Key Lab Transport Ind Comprehens Transportat Theor Nanjing Modern Multimodal Transportat Lab Beijing Peoples R China Monash Univ Dept Civil Engn Melbourne Vic 3800 Australia Southeast Univ Sch Instrument Sci & Engn Nanjing Peoples R China Southeast Univ Wuxi Campus Wuxi Peoples R China

Traffic assignment plays a crucial role in transport system analysis, and the user equilibrium model is a very essential tool. It is however highly challenging to efficiently solve the user equilibrium model, especially for large-scale networks. Taking the deterministic user equilibrium (DUE) model as a representative, this paper aims to harness parallel computing to tackle its high computing burden. A novel parallel computing framework is proposed, drawing from the frontier alternating direction method of multipliers (ADMM) method. This algorithmic framework takes a good balance between the convergence rate and the additional communication costs. Specifically, the paper incorporates insights from the Jacobi over-relaxation (JOR) iteration method into the framework of the ADMM method, improving the convergence rate by utilizing more information during the iteration process while striving to reduce the side effects it brings, thereby developing a novel parallel computing framework, named ADMM-JOR. Subsequently, convergence of the proposed algorithm is rigorously proven under an analytic framework of contractive-type methods. Furthermore, we develop an adaptive strategy for adjusting the relaxation factor in ADMM-JOR, guided by the principle of objective function value decline, which aims to further improve the convergence performance of ADMM-JOR, with minimal additional computational cost in a fully parallel setting. Numerical experiments indicate that the proposed ADMM-JOR significantly reduces computation time while retaining the excellent parallel performance of the original ADMM method and significantly improving its convergence rate.

关键词： Traffic assignment Deterministic user equilibrium parallel computing Alternating direction method of multipliers Jacobi over relaxation iteration method

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Solution of nonlinear fractional-order models of nuclear reactor with parallel computing: Implementation on GPU platform

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ANNALS OF NUCLEAR ENERGY 2024年 195卷

作者： Keluskar, Yugesh C. Singhaniya, Navin G. Vyawahare, Vishwesh A. Jage, Chaitanya S. Patil, Parag Espinosa-Paredes, Gilberto DY Patil Deemed Univ Nerul Ramrao Adik Inst Technol Dept Elect Engn Navi Mumbai 400706 Maharashtra India Univ Autonoma Metropolitana Iztapalapa Area Ingn Recursos Energet Av San Rafael Atlixco 186 Mexico City 09340 Mexico

This paper present novel parallel computing algorithms for numerical solution of nonlinear fractional-order (FO) models of nuclear reactor. These FO models arise by virtue of considering neutron transport in nuclear reactor as subdiffusion. The numerical computation of these FO models is carried out by parallelizing the sequential steps in Adams-Bashforth-Moulton method. A comparison of parallel execution on MATLAB and CUDA platforms is also given. The parallel computation is executed by harnessing multicore architecture of the general purpose graphics processing unit (GPGPU). A detailed analysis of the proposed procedure is presented considering different values of fractional derivative order in the fractional differential equations (FDEs). It is shown with extensive computational exercise that the proposed methodology achieves substantial speed up for all the three FDEs models used in nuclear reactor.

关键词： Fractional differential equations Adams-Bashforth-Moulton method Fractional neutron point kinetics GPU CUDA parallel computing

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