Journal articles: 'Parallel numerical computing'

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Migdalas, A., G. Toraldo, and V. Kumar. "Parallel computing in numerical optimization." Parallel Computing 29, no. 4 (2003): 373. http://dx.doi.org/10.1016/s0167-8191(03)00012-7.

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Karelkhan, N., P. S. Gapbarova, and O. A. Alshynbayev. "Theoretical and practical foundations of the use of parallel computing in solving problems of numerical methods in the educational process." BULLETIN of the L.N. Gumilyov Eurasian National University. PEDAGOGY. PSYCHOLOGY. SOCIOLOGY Series 143, no. 2 (2023): 158–68. http://dx.doi.org/10.32523/2616-6895-2023-143-2-158-168.

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Currently, the development of high-performance parallel computing is one of the modern requirements. There are many unsolved problems in the world. Many of these tasks require highperformance calculations. Therefore, in the field of education, there is a need to use parallel computing in solving problems of numerical methods. This requires professionals who can use parallel computing. This article makes a theoretical analysis of the learning conditions using parallel computing in solving problems of numerical methods in universities around the world and the Republic. The necessity of using parallel calculations in solving problems of numerical methods in higher educational institutions of the Republic of Kazakhstan is substantiated.

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SCHEININE, ALAN LOUIS. "PARALLEL COMPUTING AT CRS4." International Journal of Modern Physics C 04, no. 06 (1993): 1315–21. http://dx.doi.org/10.1142/s0129183193001038.

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An overview is given of parallel computing work being done at CRS4 (Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna). Parallel computation projects include: parallelization of a simulation of the interaction of high energy particles with matter (GEANT), domain decomposition for numerical solution of partial differential equations, seismic migration for oil prospecting, finite-element structural analysis, parallel molecular dynamics, a C++ library for distributed processing of specific functions, and real-time visualization of a computer simulation that runs as distributed processes.

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Wu, Qing, Maksym Spiryagin, Ingemar Persson, Chris Bosomworth, and Colin Cole. "Parallel computing of wheel-rail contact." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 10 (2019): 1109–16. http://dx.doi.org/10.1177/0954409719880737.

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Railway wheel–rail contact simulations are the most important and time-consuming tasks when simulating the system dynamics of vehicles. Parallel computing is a good approach for improving the numerical computing speed. This paper reports the advances in parallel computing of the wheel–rail contact simulations. The proposed method uses OpenMP to parallelise the multiple contact points of all the wheel–rail interfaces of a locomotive model. The method has been implemented in the vehicle system dynamics simulation package GENSYS. Simulations were conducted using two numerical solvers (4th Runge-Kutta and HeunC) and a maximum of four computer cores. Simulation cases have shown exactly the same numerical results using serial computing and parallel computing, which prove the effectiveness of the parallel computing method. The HeunC solver achieved the same simulation results and is 3.5 times faster than the 4th Runge-Kutta method. Simulation results obtained from both numerical solvers show that parallel computing using 2, 3 and 4 computer cores can improve the simulation speeds by roughly 29, 39 and 41%, respectively. There is an apparent diminishing of the rate of improvement due to the increase of the communication resource overhead when more computer cores are used. Using up to four computer cores does not require revision of the GENSYS code, and simulations can be executed using personal computers.

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Wachmann, B., and S. Schwarzer. "Three-Dimensional Massively Parallel Computing of Suspensions." International Journal of Modern Physics C 09, no. 05 (1998): 759–75. http://dx.doi.org/10.1142/s0129183198000662.

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Numerical simulations of suspensions often suffer from the fact that the simulated systems are rather small compared to experimental setups. We present a numerical scheme for non-Brownian particle-liquid mixtures in three dimensions at particle Reynolds numbers between 0.01 and 20 and describe its parallel implementation. The fluid equations are solved by a time-explicit pressure-implicit Navier–Stokes algorithm and the particle motion is tracked by molecular-dynamics methods. The two are coupled by imposing no-slip boundary conditions between the particles and the fluid. We integrate the stress distribution on the particle surface numerically to obtain forces and torques. The building blocks of the algorithm are local and scalable and we have reached particle numbers up to 106 (1.41*108 fluid nodes) on a 512 node CRAY-T3E. We compare our simulation results to theoretical predictions and experimental data and find good agreement for particle volume fractions up to 0.30.

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Quintana-Ortí, Gregorio, and Enrique S. Quintana-Ortí. "Parallel codes for computing the numerical rank." Linear Algebra and its Applications 275-276 (May 1998): 451–70. http://dx.doi.org/10.1016/s0024-3795(97)10032-5.

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Sharapov, Dmitry. "Building accurate numerical models." E3S Web of Conferences 583 (2024): 07012. http://dx.doi.org/10.1051/e3sconf/202458307012.

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Numerical modeling has emerged as a crucial tool across various scientific and engineering disciplines, enabling the simulation and prediction of complex systems. This paper explores the comprehensive process of numerical model development, encompassing problem definition, mathematical formulation, discretization, implementation, and validation. High-performance computing (HPC) technologies, including supercomputers and parallel processing, play a pivotal role in managing large-scale simulations and enhancing computational efficiency. Key strategies such as algorithm optimization, parallel computing, and efficient data management are essential for maximizing computational resources. The integration of emerging technologies like machine learning, artificial intelligence, and quantum computing holds significant promise for advancing numerical modeling capabilities. Additionally, cloud computing offers scalable and flexible resources, making high-performance computing more accessible. The paper underscores the importance of continual refinement and validation of numerical models to maintain their accuracy and reliability, ultimately highlighting the dynamic and evolving nature of this critical scientific methodology.

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Lai, Jianqi, Fengshun Lu, Xingzhi Hu, Lin Hou, Zhiren Wang, and Xiong Jiang. "Numerical simulation of supersonic combustion chemical reactions based on multi-GPU_s." Journal of Physics: Conference Series 2764, no. 1 (2024): 012044. http://dx.doi.org/10.1088/1742-6596/2764/1/012044.

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Abstract To achieve a large-scale and efficient numerical solution to supersonic combustion chemical reaction problems, a numerical simulation algorithm for combustion chemical reactions was established on a graphics processing unit (GPU). Numerical simulation of the combustion chemical reaction of a hydrocarbon fuel/air mixture in a supersonic combustor was conducted to verify the accuracy of GPU parallel calculation results and analyze its parallel performance. Numerical results show that GPU parallel computing can accurately simulate the complex flowfield in the combustor. The distribution law of wall pressure is basically consistent with the experimental values, and the numerical agreement is good, indicating that the numerical simulation algorithm for the multi-GPU supersonic combustion chemical reaction established in this paper is correct and reliable. On the Tesla V100 GPU parallel computing platform, the speedup of GPU parallel computing can reach over 100 times, greatly improving computational efficiency. When the grid size is 18.64 million, the parallel efficiency of single and double precision floating-point operations on four GPUs is 66.2% and 69.1%, respectively, which demonstrates that the multi-GPU parallel algorithm has good scalability.

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Makhmut, E., T. S. Imankulov, and B. Matkerim. "Design and development of a hybrid (mpi+cuda) parallel program for solving the oil displacement problem." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 91, no. 1 (2024): 72–82. http://dx.doi.org/10.47533/2024.1606-146x.08.

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In this work, a numerical model of oil displacement was developed using parallel computing technology through the Buckley-Leverett method. Used the hybrid (MPI + CUDA (2 GPU)) high-performance parallel computing technologies. The main goal of this work is by using these two GPU to implement computing processes of distributed data through the MPI, as well as to make a comparative analysis of the computing time and acceleration of parallel algorithms. The MPI, CUDA, hybrid (MPI + CUDA) parallel computing algorithm and the parallel program were realized, and the results were analyzed.

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KISHIMOTO, Satoshi, Masaru IWATA, Nobuatsu TANAKA, and Hiroyuki OHSHIMA. "Parallel Computing of Numerical Analysis Using CIVA Method." Proceedings of Ibaraki District Conference 2004 (2004): 1–2. http://dx.doi.org/10.1299/jsmeibaraki.2004.1.

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Gapbarova, Perizat. "Theoretical and practical bases of application of parallel computing when solving problems of the numerical method in the educational process." Scientific Herald of Uzhhorod University Series Physics, no. 56 (May 22, 2024): 1396–404. http://dx.doi.org/10.54919/physics/56.2024.139et6.

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Relevance. The rapid development of the information society highlights the need for high-quality training of computer specialists in Kazakhstan's higher education institutions. Establishing a modern system for teaching numerical methods and parallel computing is crucial for preparing future specialists to be competitive in the evolving labor market. Purpose. The research aims to justify the need for a methodological system to develop the readiness of future computer specialists in Kazakhstan's higher education institutions. This system is essential for ensuring their ability to use parallel computing to solve numerical methods problems. Methodology. The study involved analysing, comparing, systematising, classifying, and generalising theoretical data, as well as using questionnaires to evaluate opinions on the research level and the significance of the problems studied. The practical stage included preparing and implementing experiment materials, and evaluating results using mathematical statistics to verify the relevance of the author's methodology. Results. During the experiment the content of the concepts �willingness of the future specialist to use parallel computing in solving problems of numerical methods�, �hardware technologies of software computing�, and �software technologies of parallel computing� is defined. The signs and features of developing future specialists' willingness to use parallel computing in solving numerical methods problems are revealed. A methodology for this development was created and implemented. During experimental research at Nazarbayev University (Astana), a methodological toolkit was developed to enhance specialists' readiness for parallel computing. This toolkit includes the implementation of components, criteria, and indicators of readiness, along with selected methods for their development. Conclusions. The research results identify key areas for improving the methodology for developing future specialists' readiness to apply parallel computing in solving numerical methods problems. This involves enhancing the components of students' willingness to engage in such activities. The practical value of the work lies in the creation of a methodological system to improve the training of students and future computer technology specialists in Kazakhstan's higher education institutions. Keywords: informatisation of society; computer technologies; engineering education; professional training; methodological tools

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Li, De Bo, Qi Sheng Xu, Yue Liang Shen, Zhi Yong Wen, and Ya Ming Liu. "Parallel Algorithms for Compressible Turbulent Flow Simulation Using Direct Numerical Method." Advanced Materials Research 516-517 (May 2012): 980–91. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.980.

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In this study, SPMD parallel computation of compressible turbulent jet flow with an explicit finite difference method by direct numerical method is performed on the IBM Linux Cluster. The conservation equations, boundary conditions including NSCBC (charactering boundary conditions), grid generation method, and the solving processing are carefully presented in order to give other researchers a clear understanding of the large scale parallel computing of compressible turbulent flows using explicit finite difference method, which is scarce in the literatures. The speedup factor and parallel computational efficiency are presented with different domain decomposition methods. In order to use our explicit finite method for large scale parallel computing, the grid size imposed on each processor, the speedup factor, and the efficiency factor should be carefully chosen in order to design an efficient parallel code. Our newly developed parallel code is quite efficient from that of implicit finite difference method or spectral method on parallel computational efficiency. This is quite useful for future research for gas and particle two-phase flow, which is still a problem for highly efficient code for two-phase parallel computing.

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Altybay, Arshyn, Dauren Darkenbayev, and Nurbapa Mekebayev. "Parallel numerical simulation of the 2D acoustic wave equation." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024): 6519. http://dx.doi.org/10.11591/ijece.v14i6.pp6519-6525.

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Mathematical simulation has significantly broadened with the advancement of parallel computing, particularly in its capacity to comprehend physical phenomena across extensive temporal and spatial dimensions. High-performance parallel computing finds extensive application across diverse domains of technology and science, including the realm of acoustics. This research investigates the numerical modeling and parallel processing of the two-dimensional acoustic wave equation in both uniform and non-uniform media. Our approach employs implicit difference schemes, with the cyclic reduction algorithm used to obtain an approximate solution. We then adapt the sequential algorithm for parallel execution on a graphics processing unit (GPU). Ultimately, our findings demonstrate the effectiveness of the parallel approach in yielding favorable results.

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Sahu, D. R., Shin Min Kang, and Ajeet Kumar. "Convergence Analysis of ParallelS-Iteration Process for System of Generalized Variational Inequalities." Journal of Function Spaces 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/5847096.

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We consider a new system of generalized variational inequalities (SGVI) defined on two closed convex subsets of a real Hilbert space. To find the solution of considered SGVI, a parallel Mann iteration process and a parallelS-iteration process have been proposed and the strong convergence of the sequences generated by these parallel iteration processes is discussed. Numerical example illustrates that the proposed parallelS-iteration process has an advantage over parallel Mann iteration process in computing altering points of some mappings.

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Konopka, K., K. Miłkowska-Piszczek, L. Trębacz, and J. Falkus. "Improving Efficiency of CCS Numerical Simulations Through Use of Parallel Processing." Archives of Metallurgy and Materials 60, no. 1 (2015): 235–38. http://dx.doi.org/10.1515/amm-2015-0037.

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Abstract The study presents the findings of research concerning the possibilities for application of parallel processing in order to reduce the computing time of numerical simulations of the steel continuous casting process. The computing efficiency for a CCS model covering the mould and a strand fragment was analysed. The calculations were performed with the ProCAST software package using the finite element method. Two computing environments were used: the PL-Grid infrastructure and cloud computing platform.

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Liddell, Heather M., D. Parkinson, G. S. Hodgson, and P. Dzwig. "Parallel Computing Applications and Financial Modelling." Scientific Programming 12, no. 2 (2004): 81–90. http://dx.doi.org/10.1155/2004/404575.

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At Queen Mary, University of London, we have over twenty years of experience in Parallel Computing Applications, mostly on "massively parallel systems", such as the Distributed Array Processors (DAPs). The applications in which we were involved included design of numerical subroutine libraries, Finite Element software, graphics tools, the physics of organic materials, medical imaging, computer vision and more recently, Financial modelling. Two of the projects related to the latter are described in this paper, namely Portfolio Optimisation and Financial Risk Assessment.

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Su, Yanhui. "A Parallel Spectral Element Method for Fractional Lorenz System." Discrete Dynamics in Nature and Society 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/682140.

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We provide a parallel spectral element method for the fractional Lorenz system numerically. The detailed construction and implementation of the method are presented. Thanks to the spectral accuracy of the presented method, the storage requirement due to the “global time dependence” can be considerably relaxed. Also, the parareal method combining spectral element method reduces the computing time greatly. Finally, we have tested the chaotic behaviors of fractional Lorenz system. Our numerical results are in excellent agreement with the results from other numerical methods.

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Fei, Guang Lei, Jian Guo Ning, and Tian Bao Ma. "Study on the Numerical Simulation of Explosion and Impact Processes Using PC Cluster System." Advanced Materials Research 433-440 (January 2012): 2892–98. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2892.

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Parallel computing has been applied in many fields, and the parallel computing platform system, PC cluster based on MPI (Message Passing Interface) library under Linux operating system is a cost-effectiveness approach to parallel compute. In this paper, the key algorithm of parallel program of explosion and impact is presented. The techniques of solving data dependence and realizing communication between subdomain are proposed. From the test of program, the portability of MMIC-3D parallel program is satisfied, and compared with the single computer, PC cluster can improve the calculation speed and enlarge the scale greatly.

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Dongarra, Jack, Shirley Moore, and Anne Trefethen. "Numerical Libraries and Tools for Scalable Parallel Cluster Computing." International Journal of High Performance Computing Applications 15, no. 2 (2001): 175–80. http://dx.doi.org/10.1177/109434200101500210.

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Sahimi, Muhammad, and S. Mehdi Vaez Allaei. "Numerical Simulation of Wave Propagation, Part II: Parallel Computing." Computing in Science & Engineering 10, no. 4 (2008): 76–83. http://dx.doi.org/10.1109/mcse.2008.101.

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Gunawan, Dr Putu Harry. "OpenMP Performance in Numerical Simulation of Dambreak Problem Using Shallow Water Equations." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 11, no. 1 (2020): 1. http://dx.doi.org/10.24843/lkjiti.2020.v11.i01.p01.

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Numerical simulation of water surface waves is widely used to describe water flow and its impact for human life. For instance, numerical simulation of waves is elaborated to simulate Tsunami as an early warning system. Using numerical approach, the study of water flow will reduce cost and save time compared with the conventional approach (in laboratory). Shallow water equations (SWE) is one of the mathematical models which can be used to describe water flow. In numerical simulation of SWE, finite volume method is a robust method to approximate SWE. The result using numerical approach depends on the number of grids. High number of grids then smooth solution can be obtained. However, increasing number of grids leads to the increasing of computational cost. In this paper, parallel computing using OpenMP platform is given in order to reduce computational cost of numerical simulation. In parallel computing performances, Speedup andEfficiency of numerical simulation using 6400 grids points are obtained 4 times and 51% respectively.Moreover, by several numbers of cores from 2 to 8, CPU time of parallel computing is shown decreasing along the increasing number of computer cores.

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Zou, Yuan Jun. "Cloud Data Management Based on Cloud Computing Technology." Applied Mechanics and Materials 543-547 (March 2014): 3573–76. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3573.

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Cloud computing, networking and other high-end computer data processing technology are the important contents of eleven-five development planning in China. They have developed rapidly in recent years in the field of engineering. In this paper, we combine parallel computing with the collaborative simulation principle, design a cloud computing platform, establish the mathematical model of cloud data processing and parallel computing algorithm, and verify the applicability of algorithm through the numerical simulation. Through numerical calculation, cloud computing platform can be divided into complex grids, and the transmission speed is fast, which is eight times than the finite difference method. The mesh is meticulous, which reaches millions. Convergence error is minimum, only 0.001. The calculation accuracy is up to 98.36%.

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Zeng, Yao Yuan, Wen Tao Zhao, and Zheng Hua Wang. "Parallel Computing of Laser Propulsion with Hypergraph Partitioning." Advanced Materials Research 760-762 (September 2013): 311–14. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.311.

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As one of the most important methods to study laser propelled rocket, the numerical simulation of laser propulsion has drawn an ever increasing attention at present. Nevertheless, the traditional serial computing cannot satisfy the practical requirements because of high calculation precision, insatiable memory overhead and considerable computation time. In this paper, we study on a general algorithm for laser propulsion, and divide the computing domain by using a multilevel hypergraph partitioning algorithm. Furthermore, MPI allreduce, overlapping communication with computation and non blocking communication are adopted to decrease the communication time when dealing with global communication. Finally, parallel performance about two typical configurations on a China-made supercomputer shows the smallest value of speedup ratio is more than 123 when the number of processors is 256. In conclusion, our parallel method is effective and practical in numerical simulation of laser propulsion.

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Zeng, Peng, Zhong Shan Deng, and Jing Liu. "Parallel Algorithm for Solving 3-D Freezing Problems in Biological Tissues during Cryosurgery." Applied Mechanics and Materials 195-196 (August 2012): 1131–36. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1131.

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Treatment planning based on numerical prediction before or during cryosurgery is an indispensable way to achieve exactly killing of tumor. However, conventional sequential computation is difficult to meet the challenge of real-time assistance with complex treatment plans. In this study, two parallel numerical algorithms, i.e. explicit finite difference scheme and alternating direction implicit scheme, based on an effective heat capacity method are established to solve three-dimensional phase change problems in biological tissues subjected to freezing of multiple cryoprobes. The results as well as speedup of parallel computing were compared. It was shown that the parallel algorithms developed in this study can be used to perform rapid prediction of temperature distribution for cryosurgery, and that parallel computing is hopeful to assist cryosurgeons with prospective parallel treatment planning in the near future.

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Akimova, E. N., D. V. Belousov, and V. E. Misilov. "Algorithms for solving inverse geophysical problems on parallel computing systems." Numerical Analysis and Applications 6, no. 2 (2013): 98–110. http://dx.doi.org/10.1134/s199542391302002x.

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Gustafson, K. "Parallel computing forty years ago." Mathematics and Computers in Simulation 51, no. 1-2 (1999): 47–62. http://dx.doi.org/10.1016/s0378-4754(99)00108-1.

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Zosimov, Vjacheslav V., and Oleksandra S. Bulgakova. "Optimizing Computational Performance with OpenMP Parallel Programming Techniques." Control Systems and Computers, no. 3 (303) (2023): 61–68. http://dx.doi.org/10.15407/csc.2023.03.061.

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The article presents a study of parallel computing, specifically comparing the performance of OpenMP in C++ and Python. Furthermore, the technologies of OpenMP and TPL (C++, C#) are contrasted. Performance indicators were established that showcase the advantages and disadvantages of each methodology. In addition to the numerical data, the research provides insights into the peculiarities of each parallel computing model, which can assist developers in choosing the right tool.

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Li, Junjie, Yunfeng Lou, Gaoyuan Yu, and Xianlong Jin. "Parallel Computing for Numerical Analysis of a Fan Assembly Subjected to a SPH Bird." E3S Web of Conferences 233 (2021): 04045. http://dx.doi.org/10.1051/e3sconf/202123304045.

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Smoothed Particle Hydrodynamics (SPH) is widely adopted to predict bird strike events. To improve the parallel computing efficiency of the SPH approach, parallel computing was performed on the process of a bird striking the fan assembly. Since the cube-shaped domains aligned along the coordinate axes that are inherent in the decomposition algorithm may result in low computational efficiency, the effect of customized data partitioning on the efficiency is investigated. The results show that customized decompositions can minimize communication between processors and ensure the load balance during the simulation process. Besides, distributed computing with domain decompositions can present reasonable predictions at soft-impact damage, achieving consistent results within a range of less than 7% of the reference data derived from shared memory computing.

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Sbalzarini, Ivo F. "Abstractions and Middleware for Petascale Computing and Beyond." International Journal of Distributed Systems and Technologies 1, no. 2 (2010): 40–56. http://dx.doi.org/10.4018/jdst.2010040103.

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As high-performance computing moves to the petascale and beyond, a number of algorithmic and software challenges need to be addressed. This paper reviews the main performance-limiting factors in today’s high-performance computing software and outlines a possible new programming paradigm to address them. The proposed paradigm is based on abstract parallel data structures and operations that encapsulate much of the complexity of an application, but still make communication overhead explicit. The authors argue that all numerical simulations can be formulated in terms of the presented abstractions, which thus define an abstract semantic specification language for parallel numerical simulations. Simulations defined in this language can automatically be translated to source code containing the appropriate calls to a middleware that implements the underlying abstractions. Finally, the structure and functionality of such a middleware are outlined while demonstrating its feasibility on the example of the parallel particle-mesh library (PPM).

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MAZZONE, A. M. "PARALLEL PREDICTOR-CORRECTOR METHODS FOR THE SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS II." International Journal of Modern Physics C 10, no. 01 (1999): 135–45. http://dx.doi.org/10.1142/s0129183199000097.

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This work presents parallel multistep methods for the solution of ordinary differential equations. The characteristic of parallel computing is that there is a "front" and the computation at points ahead of the front depends only on information behind it. This requires a resetting of serial algorithms and may also lead to numerical errors and instabilities. The analysis of positive and negative aspects of parallel computing is the subject of this paper. Some of the methods presented below are uncommon in the literature on mathematical computing. Others have been elaborated for this study on the basis of the traditional Adams-Bashforth multistep methods. A performance comparison of the methods is made by numerical testing in molecular dynamics calculations. The increase of the number of processors m appears to seriously deteriorate the stability of the calculations and the use of m larger than 2 seems impractical.

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Padalko, Mikhail Alexandrovich, Yuriy Andreevich Shevchenko, Vitalii Yurievich Kapitan, and Konstantin Valentinovich Nefedev. "Parallel Computing of Edwards—Anderson Model." Algorithms 15, no. 1 (2021): 13. http://dx.doi.org/10.3390/a15010013.

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A scheme for parallel computation of the two-dimensional Edwards—Anderson model based on the transfer matrix approach is proposed. Free boundary conditions are considered. The method may find application in calculations related to spin glasses and in quantum simulators. Performance data are given. The scheme of parallelisation for various numbers of threads is tested. Application to a quantum computer simulator is considered in detail. In particular, a parallelisation scheme of work of quantum computer simulator.

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Aoufi, Asdin, Frank Montheillet, and Christophe Desrayaud. "Parallel Computing of Thermo-Fluid Modeling Related to the FSW Process." Key Engineering Materials 966 (November 29, 2023): 109–14. http://dx.doi.org/10.4028/p-u8ijun.

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This paper is devoted to the numerical computation of a steady-state thermo-fluid modeling related to the Friction Stir Welding Process in a two-dimensional cylindrical geometry. It analyzes the efficiency of the implementation on parallel architectures of two finite-difference schemes on a structured grid. The first one applies the Newton-Raphson method to compute a numerical solution of this non-linear elliptic type equation, and uses an iterative sparse solver. The second one is based on a time-marching approach converging to the steady state solution thanks to a time-explicit computation. Their respective performance is presented and discussed. Some numerical simulation results are presented to validate the proposed approach.

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Mikhaylenko, С. I., and S. F. Khizbullina. "About one efficient pipeline parallel algorithm for solving problems of continuum mechanics." Proceedings of the Mavlyutov Institute of Mechanics 4 (2006): 90–102. http://dx.doi.org/10.21662/uim2006.1.009.

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The paper proposes a technique for pipelining a computational process in the spatial decomposition of the computational domain for constructing efficient parallel algorithms for numerical solution of hydrodynamic problems oriented to cluster computing systems. Methods for achieving high efficiency of a parallel application based on a finite-difference explicit or semi-explicit numerical scheme are shown. An expression is written to determine the minimum size of the calculated area, at which the efficiency of the parallel program approaches unity.

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Zhu, Dingju. "Adjacent Zero Communication Parallel Cloud Computing Method and Its System forN-Body Problem with Short-Range Interaction Domain Decomposition." Advances in Astronomy 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/9049828.

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Although parallel computing is used in the existing numerical solutions ofN-body problem, tons of communications betweenNparticles render the parallel efficiency extremely low. Despite the fact that domain decomposition based on short-range interaction is used, whenNis exceedingly large and lots of communications exist between particles in adjacent areas, the parallel efficiency remains terribly low. This paper puts forward adjacent zero communication parallel cloud computing method forN-body problem with short-range interaction domain decomposition. According to this method, the adjacent subblock data are exchanged and redundantly stored without acquiring data from other subblocks in the parallel processing, so the waiting time for data transmission can be saved and hence the parallel processing efficiency can be enhanced substantially.

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Bahi, Jacques M. "Solve Numerical Problems in Parallel Computing: Parallel Iterative Algorithms20085Solve Numerical Problems in Parallel Computing: Parallel Iterative Algorithms. Chapman and Hall/CRC, 2007 (November). 240 pp. $89.95/£48.99, ISBN: 978‐1‐58488‐808‐6." Kybernetes 37, no. 9/10 (2008): 1585–86. http://dx.doi.org/10.1108/k.2008.37.9_10.1585.5.

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Anderson, F. C., J. M. Ziegler, M. G. Pandy, and R. T. Whalen. "Application of High-Performance Computing to Numerical Simulation of Human Movement." Journal of Biomechanical Engineering 117, no. 1 (1995): 155–57. http://dx.doi.org/10.1115/1.2792264.

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We have examined the feasibility of using massively-parallel and vector-processing supercomputers to solve large-scale optimization problems for human movement. Specifically, we compared the computational expense of determining the optimal controls for the single support phase of gait using a conventional serial machine (SGI Iris 4D25), a MIMD parallel machine (Intel iPSC/860), and a parallel-vector-processing machine (Cray Y-MP 8/864). With the human body modeled as a 14 degree-of-freedom linkage actuated by 46 musculotendinous units, computation of the optimal controls for gait could take up to 3 months of CPU time on the Iris. Both the Cray and the Intel are able to reduce this time to practical levels. The optimal solution for gait can be found with about 77 hours of CPU on the Cray and with about 88 hours of CPU on the Intel. Although the overall speeds of the Cray and the Intel were found to be similar, the unique capabilities of each machine are better suited to different portions of the computational algorithm used. The Intel was best suited to computing the derivatives of the performance criterion and the constraints whereas the Cray was best suited to parameter optimization of the controls. These results suggest that the ideal computer architecture for solving very large-scale optimal control problems is a hybrid system in which a vector-processing machine is integrated into the communication network of a MIMD parallel machine.

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Hafizah, Farhah Saipan Saipol, and Alias Norma. "Numerical simulation of DIC drying process on matlab distributed computing server." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 338–46. https://doi.org/10.11591/ijeecs.v20.i1.pp338-346.

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Instant controlled pressure drop, also known as DIC, is one of the drying techniques that has been used for texturing, extracting and drying various food products. Mathematical model has been used to explain the drying process, although most of the studies focused on the statistical regression model approach. Due to the limitations of regression model, which neglects the fundamental of dehydration process, this paper presents the development of mathematical models to detect, solve and visualize the three-dimensional (3D) heat and mass transfer in DIC drying process. Finite Difference Method (FDM) with central difference formula is used to discretize the mathematical models. A large sparse of system of linear equation (SLE), which represents the actual drying process, is solved by using some numerical methods, such as Jacobi (JB), Red Black Gauss Seidel (RBGS), Alternating Group Explicit with Douglas (AGED) and Brian (AGEB) methods. Based on the numerical results, high execution time and high computational complexity have been shown. In order to reduce the execution time and computational complexity, the parallel algorithm based on domain decomposition technique has been implemented on the MATLAB Distributed Computing Server (MDCS). The parallel algorithm of the numerical methods was evaluated and compared based on the parallel performance metrics: execution time, speed up, efficiency, effectiveness, temporal performance and granularity. From the parallel performance metrics, it was found that the PAGEB approach had better performance, followed by PAGED, PRBGS and PJB methods.

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Gavrilova, Natalia M., Yuri A. Plotonenko, and Andrey A. STUPNIKOV. "DEVELOPING INTELLIGENT SOFTWARE FOR COMPUTING PARALLELIZATION RESEARCH." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 3 (2021): 152–69. http://dx.doi.org/10.21684/2411-7978-2021-7-3-152-169.

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One of the most important ways of improving the speed of complex task solving is employing a multiprocessor computational system. This paper describes the experience of software development for research management and solving educational problems using parallel computing technologies. The authors describe approaches to computation parallelization using a multiprocessor system with shared memory within a task of finding a numerical root of a system of linear equations with a tridiagonal coefficient matrix that appears when solving a boundary problem for a partial differential equation of parabolic type, the heat equation. Additionally, the approaches to parallelization implementation of the tridiagonal matrix method for the heat equation in the two-dimensional case within a numerical root-finding algorithm using the alternating-direction implicit method for a multiprocessor system with shared memory are described. A finite-difference method of variable directions is used to find a numerical root of a heat equation in the two-dimensional case. Sequential and parallel algorithms (two-sided Thomas algorithm and multithread horizontal block Thomas algorithm) that fit an execution on computational systems with shared memory have been used to implement a tridiagonal matrix method. Two parallel computation organization technologies for computational systems with shared memory have been used for computation parallelization: one based on OpenMP technology and one using .NET framework facilities. The parallelization process and load balance serving have been performed by means of the environment in the first case as manual operation of threads parallelization process is allowed in the latter one. As an assessment of the described approach performance, the calculation time for sequential and parallel algorithms is given depending on the task’s size and the number of threads used. Comparison of the considered parallelization algorithms and implementation technologies is performed based on the analysis of the resulting acceleration. This paper shows that total computation time is several times smaller and calculation acceleration is several times bigger when using Thread instead of OpenMP. An application has been developed that allows obtaining a visual result of modelling of process of temperature propagation in the study area using parallel calculation technologies in real time.

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Alaeddini, Atiye, and Daniel J. Klein. "Parallel Simultaneous Perturbation Optimization." Asia-Pacific Journal of Operational Research 36, no. 03 (2019): 1950009. http://dx.doi.org/10.1142/s021759591950009x.

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Stochastic computer simulations enable users to gain new insights into complex physical systems. Optimization is a common problem in this context: users seek to find model inputs that maximize the expected value of an objective function. The objective function, however, is time-intensive to evaluate, and cannot be directly measured. Instead, the stochastic nature of the model means that individual realizations are corrupted by noise. More formally, we consider the problem of optimizing the expected value of an expensive black-box function with continuously-differentiable mean, from which observations are corrupted by Gaussian noise. We present parallel simultaneous perturbation optimization (PSPO), which extends a well-known stochastic optimization algorithm, simultaneous perturbation stochastic approximation, in several important ways. Our modifications allow the algorithm to fully take advantage of parallel computing resources, like high-performance cloud computing. The resulting PSPO algorithm takes fewer time-consuming iterations to converge, automatically chooses the step size, and can vary the error tolerance by step. Theoretical results are supported by a numerical example.

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40

Saipan Saipol, Hafizah Farhah, and Norma Alias. "Numerical simulation of DIC drying process on matlab distributed computing server." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 338. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp338-346.

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Abstract:

Instant controlled pressure drop, also known as DIC, is one of the drying techniques that has been used for texturing, extracting and drying various food products. Mathematical model has been used to explain the drying process, although most of the studies focused on the statistical regression model approach. Due to the limitations of regression model, which neglects the fundamental of dehydration process, this paper presents the development of mathematical models to detect, solve and visualize the three-dimensional (3D) heat and mass transfer in DIC drying process. Finite Difference Method (FDM) with central difference formula is used to discretize the mathematical models. A large sparse of system of linear equation (SLE), which represents the actual drying process, is solved by using some numerical methods, such as Jacobi (JB), Red Black Gauss Seidel (RBGS), Alternating Group Explicit with Douglas (AGED) and Brian (AGEB) methods. Based on the numerical results, high execution time and high computational complexity have been shown. In order to reduce the execution time and computational complexity, the parallel algorithm based on domain decomposition technique has been implemented on the MATLAB Distributed Computing Server (MDCS). The parallel algorithm of the numerical methods was evaluated and compared based on the parallel performance metrics: execution time, speed up, efficiency, effectiveness, temporal performance and granularity. From the parallel performance metrics, it was found that the PAGEB approach had better performance, followed by PAGED, PRBGS and PJB methods.

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41

Shvachych, Gennady, Volodymyr Konovalenkov, Olena Ivaschenko, and Larysa Sushko. "Development of parallel structures of differential tasks of mathematical physics." System technologies 3, no. 128 (2020): 36–45. http://dx.doi.org/10.34185/1562-9945-3-128-2020-04.

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The paper is devoted to the construction of parallel forms of mathematical models of a tridiagonal structure. Two methods of discretization of differential problems are considered by the example of solving the mathematical physics equation. Moreover, the application of the numerical-analytical straight line method and sweep methods to parallelization of mathematical models with a tridiagonal structure allows constructing its exact node-by-node solutions with the maximum parallel form and the least implementation time on parallel computing devices. This paper proposes to apply finite-difference and numerical-analytical methods in combination with the splitting method as a methodological basis for constructing numerical methods for solving such problems. The splitting method provides an economical and sustainable implementation of numerical models by the scalar sweep method. For such systems, acceptable acceleration in most cases is achieved by parallelizing operations in the corresponding sequential method, forming linear sections.It is convenient to implement the parallelization algorithm and its mapping to parallel computing systems on the two schemes proposed in this paper: finite-difference and numerical-analytical. This approach allows arranging separate determination of the thermophysical characteristics of the structures’ material, i.e. allows obtaining solutions of coefficient and other inverse problems of thermal conductivity.The proposed approach to the development of methods, algorithms and software can be applied in various branches of metallurgical thermal physics, economics, as well as for environmental problems of the metallurgical industry.

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Briš, Radim, and Simona Domesová. "New Computing Technology in Reliability Engineering." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/187362.

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Reliability engineering is relatively new scientific discipline which develops in close connection with computers. Rapid development of computer technology recently requires adequate novelties of source codes and appropriate software. New parallel computing technology based on HPC (high performance computing) for availability calculation will be demonstrated in this paper. The technology is particularly effective in context with simulation methods; nevertheless, analytical methods are taken into account as well. In general, basic algorithms for reliability calculations must be appropriately modified and improved to achieve better computation efficiency. Parallel processing is executed by two ways, firstly by the use of the MATLAB function parfor and secondly by the use of the CUDA technology. The computation efficiency was significantly improved which is clearly demonstrated in numerical experiments performed on selected testing examples as well as on industrial example. Scalability graphs are used to demonstrate reduction of computation time caused by parallel computing.

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Alawneh, Shadi G., Lei Zeng, and Seyed Ali Arefifar. "A Review of High-Performance Computing Methods for Power Flow Analysis." Mathematics 11, no. 11 (2023): 2461. http://dx.doi.org/10.3390/math11112461.

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Power flow analysis is critical for power systems due to the development of multiple energy supplies. For safety, stability, and real-time response in grid operation, grid planning, and analysis of power systems, it requires designing high-performance computing methods, accelerating power flow calculation, obtaining the voltage magnitude and phase angle of buses inside the power system, and coping with the increasingly complex large-scale power system. This paper provides an overview of the available parallel methods to fix the issues. Specifically, these methods can be classified into three categories from a hardware perspective: multi-cores, hybrid CPU-GPU architecture, and FPGA. In addition, from the perspective of numerical computation, the power flow algorithm is generally classified into iterative and direct methods. This review paper introduces models of power flow and hardware computing architectures and then compares their performance in parallel power flow calculations depending on parallel numerical methods on different computing platforms. Furthermore, this paper analyzes the challenges and pros and cons of these methods and provides guidance on how to exploit the parallelism of future power flow applications.

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Bao, Y., J. Luo, and M. Ye. "Parallel Direct Method of DNS for Two-Dimensional Turbulent Rayleigh-Bénard Convection." Journal of Mechanics 34, no. 2 (2017): 159–66. http://dx.doi.org/10.1017/jmech.2017.54.

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AbstractA highly efficient parallelization scheme of direct numerical simulation (DNS) for two-dimensional Rayleigh-Bénard convection is presented. By introducing the parallel diagonal dominant (PDD) algorithm to solve the pressure Poisson equation and adjusting the domain decomposition accordingly, all-to-all communication as the usual obstacle to parallel computing can be eliminated. Excellent strong scaling and weak scaling for the parallel efficiency are achieved. Numerical results show that very complex structures in flow exist at very high Ra numbers. The required high resolution both in space and in time can be obtained by the present method at low parallel overhead.

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Yu, Kun Ming, and Ming Gong Lee. "A Parallel Block Predictor-Corrector Method by Python-Based Distributed Computing." Applied Mechanics and Materials 263-266 (December 2012): 1315–18. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1315.

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This paper is to discuss how Python can be used in designing a cluster parallel computation environment in numerical solution of some block predictor-corrector method for ordinary differential equations. In the parallel process, MPI-2(message passing interface) is used as a standard of MPICH2 to communicate between CPUs. The operation of data receiving and sending are operated and controlled by mpi4py which is based on Python. Implementation of a block predictor-corrector numerical method with one and two CPUs respectively is used to test the performance of some initial value problem. Minor speed up is obtained due to small size problems and few CPUs used in the scheme, though the establishment of this scheme by Python is valuable due to very few research has been carried in this kind of parallel structure under Python.

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WEI, Haoyang, Guosun ZENG, and Chunling DING. "Parallel computing and numerical analysis of laminar diffusion combustion on GPU." Journal of Computer Applications 33, no. 9 (2013): 2428–31. http://dx.doi.org/10.3724/sp.j.1087.2013.02428.

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De, Somen. "Faster numerical weather forecasting using parallel computing with multi-mesh topology." ENVIRONMENTAL AND EARTH SCIENCES RESEARCH JOURNAL 4, no. 2 (2017): 29–32. http://dx.doi.org/10.18280/eesrj.040201.

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Edelman, Alan. "Large Dense Numerical Linear Algebra in 1993: the Parallel Computing Influence." International Journal of Supercomputing Applications 7, no. 2 (1993): 113–28. http://dx.doi.org/10.1177/109434209300700203.

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Levin, M. P. "Numerical Recipes In Fortran 90: The Art Of Parallel Scientific Computing." IEEE Concurrency 6, no. 4 (1998): 79. http://dx.doi.org/10.1109/mcc.1998.736436.

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Tinetti, Fernando G., Maximiliano J. Perez, Ariel Fraidenraich, and Adolfo E. Altenberg. "Legacy code and parallel computing: updating and parallelizing a numerical model." Journal of Supercomputing 76, no. 7 (2020): 5636–54. http://dx.doi.org/10.1007/s11227-020-03172-7.

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Journal articles on the topic 'Parallel numerical computing'

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