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Artykuły w czasopismach na temat „Graphics Processing Unit (GPU) Programming”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Huang, Yunlong, Keck Voon Ling, and Simon See. "SOLVING QUADRATIC PROGRAMMING PROBLEMS ON GRAPHICS PROCESSING UNIT." ASEAN Engineering Journal 1, no. 2 (2010): 76–86. http://dx.doi.org/10.11113/aej.v1.15293.

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Quadratic Programming (QP) problems frequently appear as core component when solving constrained optimal control or estimation problems. The focus of this paper is on accelerating an existing Interior Point Method (IPM) for solving QP problems by exploiting the parallel computing characteristics of GPU. We compare the so-called data-parallel and the problem-parallel approaches to achieve speed up for solving QP problems. The data-parallel approach achieves speed up by parallelizing the vector and matrix computations such as the dot-product, while the problem-parallel approach solves multiple Q

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Brodtkorb, André R., Trond R. Hagen, and Martin L. Sætra. "Graphics processing unit (GPU) programming strategies and trends in GPU computing." Journal of Parallel and Distributed Computing 73, no. 1 (2013): 4–13. http://dx.doi.org/10.1016/j.jpdc.2012.04.003.

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Kurniawan, Kwek Benny, and YB Dwi Setianto. "CPU AND GPU PERFORMANCE ANALYSIS ON 2D MATRIX OPERATION." Proxies : Jurnal Informatika 2, no. 1 (2021): 1. http://dx.doi.org/10.24167/proxies.v2i1.3194.

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GPU or Graphic Processing Unit can be used on many platforms in general GPUs are used for rendering graphics but now GPUs are general purpose parallel processors with support for easily accessible programming interfaces and industry standard languages such as C, Python and Fortran. In this study, the authors will compare CPU and GPU for completing some matrix calculation. To compare between CPU and GPU, the authors have done some testing to observe the use of Processing Unit, memory and computing time to complete matrix calculations by changing matrix sizes and dimensions. The results of tests

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Kravchuk, Denis. "Construction of an optoacoustic image of biological tissues based on an algorithm for a graphics processor." Applied Physics, no. 5 (November 19, 2021): 106–9. http://dx.doi.org/10.51368/1996-0948-2021-5-106-109.

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The use of optical contrast between different blood particles allows the use of optoacoustic imaging to visualize the distribution of blood particles (erythrocytes, taking into account oxygen saturation), the delivery of drugs to organs through blood vessels. An algorithm for calculating the ultrasonic field obtained as a result of optoacoustic interaction has been developed to speed up calculations on the GPU board. An architecture for fast restoration of an optoacoustic signal based on graphics processing unit (GPU) programming is proposed. The algorithm used in combination with the pre-migr

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Nozdrzykowski, Łukasz, and Magdalena Nozdrzykowska. "Models for estimating the time of program loop execution in parallel on a CPU and with the use of OpenCL computation on a GPU." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 12 (2018): 802–7. http://dx.doi.org/10.24136/atest.2018.501.

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The authors present models for estimating the time of execution of program loops compliant with the FAN model with no data dependencies or with data dependencies only within the body programming loop, which can be executed either by CPUs or by stream multiprocessors referred to as GPU cores. The models presented will make it possible to determine whether it would be more efficient to execute computation in the existing environment using the CPU (Central Pro-cessing Unit) or a state-of-the-art graphics card with a high-performance GPU (Graphics Processing Unit) and super-fast memory, of-ten imp

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Rtal, Youness, and Abdelkader Hadjoudja. "Comparative study of the implementation of the Lagrange interpolation algorithm on GPU and CPU using CUDA to compute the density of a material at different temperatures." SHS Web of Conferences 119 (2021): 07002. http://dx.doi.org/10.1051/shsconf/202111907002.

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Graphics Processing Units (GPUs) are microprocessors attached to graphics cards, which are dedicated to the operation of displaying and manipulating graphics data. Currently, such graphics cards (GPUs) occupy all modern graphics cards. In a few years, these microprocessors have become potent tools for massively parallel computing. Such processors are practical instruments that serve in developing several fields like image processing, video and audio encoding and decoding, the resolution of a physical system with one or more unknowns. Their advantages: faster processing and consumption of less

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Crous, P. A., J. E. van Zyl, and Y. Roodt. "The potential of graphical processing units to solve hydraulic network equations." Journal of Hydroinformatics 14, no. 3 (2011): 603–12. http://dx.doi.org/10.2166/hydro.2011.023.

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The Engineering discipline has relied on computers to perform numerical calculations in many of its sub-disciplines over the last decades. The advent of graphical processing units (GPUs), parallel stream processors, has the potential to speed up generic simulations that facilitate engineering applications aside from traditional computer graphics applications, using GPGPU (general purpose programming on the GPU). The potential benefits of exploiting the GPU for general purpose computation require the program to be highly arithmetic intensive and also data independent. This paper looks at the sp

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Narang, Hira, Fan Wu, and Abdul Rafae Mohammed. "An Efficient Acceleration of Solving Heat and Mass Transfer Equations with the Second Kind Boundary Conditions in Capillary Porous Radially Composite Cylinder Using Programmable Graphics Hardware." Computer and Information Science 13, no. 2 (2020): 75. http://dx.doi.org/10.5539/cis.v13n2p75.

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With the recent developments in computing technology, increased efforts have gone into the simulation of various scientific methods and phenomenon in engineering fields. One such case is the simulation of heat and mass transfer equations which is becoming more and more important in analyzing various scenarios in engineering applications. Analysing the heat and mass transfer phenomenon under various environmental conditions require us to simulate it. However, this process of numerical solution of heat and mass transfer equations is very time consuming. Therefore, this paper aims at utilizing on

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Syrocki, Łukasz, and Grzegorz Pestka. "Implementation of algebraic procedures on the GPU using CUDA architecture on the example of generalized eigenvalue problem." Open Computer Science 6, no. 1 (2016): 79–90. http://dx.doi.org/10.1515/comp-2016-0006.

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AbstractThe ready to use set of functions to facilitate solving a generalized eigenvalue problem for symmetric matrices in order to efficiently calculate eigenvalues and eigenvectors, using Compute Unified Device Architecture (CUDA) technology from NVIDIA, is provided. An integral part of the CUDA is the high level programming environment enabling tracking both code executed on Central Processing Unit and on Graphics Processing Unit. The presented matrix structures allow for the analysis of the advantages of using graphics processors in such calculations.

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Cao, Wei, Zheng Hua Wang, and Chuan Fu Xu. "An Out-of-Core Method for CFD Simulation in Heterogeneous Environment." Advanced Materials Research 753-755 (August 2013): 2912–15. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2912.

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In recent years, the highly parallel graphics processing unit (GPU) is rapidly gaining maturity as a powerful engine for high performance computer. However, in most computational fluid dynamics (CFD) simulations, the computational capacity of CPU was ignored. In this paper, we propose a hybrid parallel programming model to utilize the computational capacity of both CPU and GPU. Considering the memory amount of CPU and GPU, we also propose an out-of-core method to increase the simulation scale on single node. The experiment results show that the programming model can utilize the computational c

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Jo, Hee Seung, Myung Ho Lee, and Dong Hoon Choi. "GPU Virtualization using PCI Direct Pass-Through." Applied Mechanics and Materials 311 (February 2013): 15–19. http://dx.doi.org/10.4028/www.scientific.net/amm.311.15.

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Machine virtualization and cloud computing environment have highlighted for last several years. This trend is based on the endeavor to enhance the utilization and reduce the ownership cost of machines. On the other hand, in aspect of high performance computing, graphics processing unit (GPU) has proved its capability for general purpose computing in many research areas. Evolving from traditional APIs such as the OpenGL and the Direct3D to program GPU as a graphics device, the CUDA of NIVDIA and the OpenCL provide more general programming environment for users. By supporting memory access model

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Wei, Cao, Zheng Hua Wang, and Chuan Fu Xu. "A GPU/CPU Programming Model for CFD Simulation." Advanced Materials Research 712-715 (June 2013): 2538–41. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2538.

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In recent years, the highly parallel graphics processing unit (GPU) is rapidly gaining maturity as a powerful engine for high performance computer. More and more researchers try to port the computational fluid dynamics (CFD) simulations into heterogeneous computers. However, most researchers focus on exploring the computational capability of GPU, while ignore the computational capability of CPU. In order to utilize the computational capability of CPU and GPU, we propose a hybrid CUDA/OpenMP parallel programming model. And we proposed an adaptive load balancing scheme to distribute the workload

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Vuyets, T. R., and Vladislav A. Ovchinnikov. "Development of a Parallel Fast Fourier Transform-Based Algorithm for Digital Hologram Reconstruction." Advanced Materials Research 1040 (September 2014): 949–53. http://dx.doi.org/10.4028/www.scientific.net/amr.1040.949.

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Digital holography is a comparatively new observation method for micron-sized particles. It is based on numerical reconstruction of recorded interference fringe. Calculation processes for reconstruction are both time- and memory-intensive. The aim of this study was to develop a faster, more efficient algorithm for digital hologram reconstruction. To this purpose Central Processing Unit (CPU) and Graphics Processing Unit (GPU) programming were implemented. For the problem solving the algorithms’ run-time for both configurations was measured. The results showed that the algorithm using a GPU boa

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Cabodi, G., A. Garbo, C. Loiacono, S. Quer, and G. Francini. "Efficient Complex High-Precision Computations on GPUs without Precision Loss." Journal of Circuits, Systems and Computers 26, no. 12 (2017): 1750187. http://dx.doi.org/10.1142/s0218126617501870.

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General-purpose computing on graphics processing units is the utilization of a graphics processing unit (GPU) to perform computation in applications traditionally handled by the central processing unit. Many attempts have been made to implement well-known algorithms on embedded and mobile GPUs. Unfortunately, these applications are computationally complex and often require high precision arithmetic, whereas embedded and mobile GPUs are designed specifically for graphics, and thus are very restrictive in terms of input/output, precision, programming style and primitives available. This paper st

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Balagafshe, Rahman Ghasempour, Alireza Akoushideh, and Asadollah Shahbahrami. "Matrix-matrix multiplication on graphics processing unit platform using tiling technique." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 1012. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp1012-1019.

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Today’s hardware platforms have parallel processing capabilities and many parallel programming models have been developed. It is necessary to research an efficient implementation of compute-intensive applications using available platforms. Dense matrix-matrix multiplication is an important kernel that is used in many applications, while it is computationally intensive, especially for large matrix sizes. To improve the performance of this kernel, we implement it on the graphics processing unit (GPU) platform using the tiling technique with different tile sizes. Our experimental results show the

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Balagafshe, Rahman Ghasempour, Alireza Akoushideh, and Asadollah Shahbahrami. "Matrix-matrix multiplication on graphics processing unit platform using tiling technique." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 1012–19. https://doi.org/10.11591/ijeecs.v28.i2.pp1012-1019.

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Today’s hardware platforms have parallel processing capabilities and many parallel programming models have been developed. It is necessary to research an efficient implementation of compute-intensive applications using available platforms. Dense matrix-matrix multiplication is an important kernel that is used in many applications, while it is computationally intensive, especially for large matrix sizes. To improve the performance of this kernel, we implement it on the graphics processing unit (GPU) platform using the tiling technique with different tile sizes. Our experimental results sh

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Das, Rajat Suvra, and Vikas Gupta. "A Systematic Literature Review on Graphics Processing Unit Accelerated Realm of High-Performance Computing." International Journal of Computing and Engineering 5, no. 3 (2024): 10–21. http://dx.doi.org/10.47941/ijce.1813.

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GPUs (Graphics Processing Units) are widely used due to their impressive computational power and parallel computing ability.It have shown significant potential in improving the performance of HPC applications. This is due to their highly parallel architecture, which allows for the execution of multiple tasks simultaneously. However, GPU computing is synonymous with CUDA in providing applications for GPU devices. This offers enhanced development tools and comprehensive documentation to increase performance, while AMD’s ROCm platform features an application programming interface compatible with

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Roberge, Vincent, and Mohammed Tarbouchi. "Parallel Hybrid 2-Opt Flower Pollination Algorithm for Real-Time UAV Trajectory Planning on GPU." ITM Web of Conferences 48 (2022): 03007. http://dx.doi.org/10.1051/itmconf/20224803007.

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Abstract. The development of autonomous Unmanned Aerial Vehicles (UAVs) is a priority to many civilian and military organizations. An essential aspect of UAV autonomy is the ability for automatic trajectory planning. In this paper, we use a parallel Flower Pollination Algorithm (FPA) to deal with the problem's complexity and compute feasible and quasi-optimal trajectories for fixed-wing UAVs in complex 3D environments, taking into account the vehicle's flight properties. The global optimization algorithm is improved with the addition of 2-opt local search providing a significant improvement. T

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Silva, Bruno, Luiz Guerreiro Lopes, and Fábio Mendonça. "Multithreaded and GPU-Based Implementations of a Modified Particle Swarm Optimization Algorithm with Application to Solving Large-Scale Systems of Nonlinear Equations." Electronics 14, no. 3 (2025): 584. https://doi.org/10.3390/electronics14030584.

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This paper presents a novel Graphics Processing Unit (GPU) accelerated implementation of a modified Particle Swarm Optimization (PSO) algorithm specifically designed to solve large-scale Systems of Nonlinear Equations (SNEs). The proposed GPU-based parallel version of the PSO algorithm uses the inherent parallelism of modern hardware architectures. Its performance is compared against both sequential and multithreaded Central Processing Unit (CPU) implementations. The primary objective is to evaluate the efficiency and scalability of PSO across different hardware platforms with a focus on solvi

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Ahmad, Hasif Azman, Abdul Mutalib Al Junid Syed, Hadi Abdul Razak Abdul, Faizul Md Idros Mohd, Karimi Halim Abdul, and Nazmie Osman Fairul. "Performance Evaluation of SW Algorithm on NVIDIA GeForce GTX TITAN X Graphic Processing Unit (GPU)." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 670–76. https://doi.org/10.11591/ijeecs.v12.i2.pp670-676.

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Nowadays, the requirement for high performance and sensitive alignment tools have increased after the advantage of the Deoxyribonucleic Acid (DNA) and molecular biology has been figured out through Bioinformatics study. Therefore, this paper reports the performance evaluation of parallel Smith-Waterman Algorithm implementation on the new NVIDIA GeForce GTX Titan X Graphic Processing Unit (GPU) compared to the Central Processing Unit (CPU) running on Intel® CoreTM i5-4440S CPU 2.80GHz. Both of the design were developed using C-programming language and targeted to the respective platform. Th

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da Silva Reis, César Augusto Borges, Daniel Botezelli, Arthur Mendonça de Azevedo, Elisan dos Santos Magalhães, and Aristeu da Silveira Neto. "Accelerating Conjugate Heat Transfer Simulations in Squared Heated Cavities through Graphics Processing Unit (GPU) Computing." Computation 12, no. 5 (2024): 106. http://dx.doi.org/10.3390/computation12050106.

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This research develops an innovative framework for accelerating Conjugate Heat Transfer (CHT) simulations within squared heated cavities through the application of Graphics Processing Units (GPUs). Although leveraging GPUs for computational speed improvements is well recognized, this study distinguishes itself by formulating a tailored optimization strategy utilizing the CUDA-C programming language. This approach is specifically designed to tackle the inherent challenges of modeling squared cavity configurations in thermal simulations. Comparative performance evaluations reveal that our GPU-ac

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Shyam, Mohan K. T. Jayaraj P. B. "Cell Charge Approximation for Accelerating Molecular Simulation on CUDA-Enabled GPU." International Journal of Computer-Aided Technologies (IJCAx) 01, apr (2014): 01–12. https://doi.org/10.5281/zenodo.1436258.

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Methods for Molecular Dynamics(MD) simulations are investigated. MD simulation is the widely used computer simulation approach to study the properties of molecular system. Force calculation in MD is computationally intensive. Paral-lel programming techniques can be applied to improve those calculations. The major aim of this paper is to speed up the MD simulation calculations by/using General Purpose Graphics Processing Unit(GPU) computing paradigm, an efficient and economical way for parallel computing. For that we are proposing a method called cell charge approximation which treats the elect

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Sechenov, Pavel A. "Solution to the problem of thermal conductivity in a two-dimensional formulation on a graphics processing unit using parallel computing." Yugra State University Bulletin 18, no. 2 (2022): 104–12. http://dx.doi.org/10.18822/byusu202202104-112.

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Subject: technology and algorithms of parallel programming. Objective: to compare the execution speed of a sequential algorithm on a central processor with a parallel algorithm on a graphics processor when solving a two-dimensional thermal conductivity problem. Methods: the Crank Nicholson method modified by the author for solving the problem of two-dimensional thermal conductivity on a graphics processor. Research results: 1. The solution of the two-dimensional thermal conductivity problem according to the Crank Nicholson scheme is not absolutely parallel and the maximum pos

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Sechenov, Pavel A. "Solution to the problem of thermal conductivity in a two-dimensional formulation on a graphics processing unit using parallel computing." Yugra State University Bulletin 18, no. 2 (2022): 104–12. http://dx.doi.org/10.18822/byusu202202104-112.

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Subject: technology and algorithms of parallel programming. Objective: to compare the execution speed of a sequential algorithm on a central processor with a parallel algorithm on a graphics processor when solving a two-dimensional thermal conductivity problem. Methods: the Crank Nicholson method modified by the author for solving the problem of two-dimensional thermal conductivity on a graphics processor. Research results: 1. The solution of the two-dimensional thermal conductivity problem according to the Crank Nicholson scheme is not absolutely parallel and the maximum pos

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Hasif Azman, Ahmad, Syed Abdul Mutalib Al Junid, Abdul Hadi Abdul Razak, Mohd Faizul Md Idros, Abdul Karimi Halim, and Fairul Nazmie Osman. "Performance Evaluation of SW Algorithm on NVIDIA GeForce GTX TITAN X Graphic Processing Unit (GPU)." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 670. http://dx.doi.org/10.11591/ijeecs.v12.i2.pp670-676.

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Nowadays, the requirement for high performance and sensitive alignment tools have increased after the advantage of the Deoxyribonucleic Acid (DNA) and molecular biology has been figured out through Bioinformatics study. Therefore, this paper reports the performance evaluation of parallel Smith-Waterman Algorithm implementation on the new NVIDIA GeForce GTX Titan X Graphic Processing Unit (GPU) compared to the Central Processing Unit (CPU) running on Intel® CoreTM i5-4440S CPU 2.80GHz. Both of the design were developed using C-programming language and targeted to the respective platform. The co

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Sun, Yimin, Ilya Silvestrov, and Andrey Bakulin. "Accelerating the 2+2+1 method for estimating local traveltime operators in nonlinear beamforming using GPU graphics cards." Journal of Geophysics and Engineering 19, no. 3 (2022): 389–402. http://dx.doi.org/10.1093/jge/gxac028.

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Abstract Local traveltime operators are an effective way to describe local kinematic wavefronts. They are useful for many applications. One of them is nonlinear beamforming for enhancing the signal-to-noise ratio of challenging seismic data. The so-called 2+2+1 method is a pragmatic approach to estimate unknown local traveltime operators from input data. However, its efficiency still has much room for improvement when the solution space is big. We accelerate the 2+2+1 method using graphics processing unit (GPU) computing with the Compute Unified Device Architecture (CUDA) programming language.

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Zhang, Wenqiang, Zhenguo Zhang, Mengyang Li, and Xiaofei Chen. "GPU implementation of curved-grid finite-difference modelling for non-planar rupture dynamics." Geophysical Journal International 222, no. 3 (2020): 2121–35. http://dx.doi.org/10.1093/gji/ggaa290.

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SUMMARY A deep understanding of earthquake physics requires a large amount of numerical simulations on seismic wave propagation and dynamic rupture. However, the corresponding intensive computational expense of simulations at traditional CPU (central processing unit) platforms make related researches time-consuming. There are many mature graphics processing unit (GPU) programs that can dramatically accelerate the calculation of seismic wave propagation. Unfortunately, there are few discussions about GPU implementations for rupture dynamics. In this work, we extend our 3-D curved-grid finite-di

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Rehan, Muzammil. "Testing of High Speed Data Transmission over a Fading Channel on a Graphics Processing Unit for Software Defined Radio." European Journal of Advances in Engineering and Technology 5, no. 11 (2018): 879–87. https://doi.org/10.5281/zenodo.10727318.

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<strong>ABSTRACT</strong> 5G mobile communication networks are emerging in order to cover the extreme needs for high data rates for delivering multimedia data to mobile communication users. High transmission bit rate in wireless channels gives rise to severe inter-symbol interference (ISI) and this makes the detection task very challenging. In such cases, Near-Maximum-Likelihood (NML) detection gives good performance. This paper describes the Testing of a High Speed Multi-Level data transmission system using NML detection on a Graphics Processing Unit (GPU) using CUDA-C++ programming language.

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Feng, Yu Fen. "Research on Key Factors in Shader Programming of Dynamic Water Surface." Applied Mechanics and Materials 50-51 (February 2011): 775–79. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.775.

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Research on real time dynamic water surface is a hotspot in the study of virtual reality with very broad application prospect. With the rapid development of calculating ability of GPU (Graphics Processing Unit) and the development of HLSL (High-Level Shader Language), the tasks such as vertex shader ,pixel shader, calculation of normal vector, dynamic texture mapping and calculation of water reflections and refractions can be done by GPU. In our research we studied the key factors affecting the effect of dynamic water surface and indicated that the writer presents some opinions which can gener

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Handayanto, Rahmadya Trias, and Herlawati Herlawati. "Prediksi Kelas Jamak dengan Deep Learning Berbasis Graphics Processing Units." Jurnal Kajian Ilmiah 20, no. 1 (2020): 67–76. http://dx.doi.org/10.31599/jki.v20i1.71.

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For the first time, machine learning did the classical classification process using two classes (bi-class) such as class -1 and class +1, 0 and 1, or the form of categories such as true and false. Famous methods used are Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The current development was a problem with more than two classes, known as multi-class classes. For SVM sometimes the plural classes are overcome by doing a gradual process like a decision tree (DT) method. Meanwhile, ANN has experienced rapid development and is currently being developed with a large number of

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Jensen, Oyvind, Henrik Lam, and Hallvard Fjar. "GPU implementation of Kampmann-Wagner numerical precipitation models." Computer Methods in Material Science 16, no. 3 (2016): 127–38. http://dx.doi.org/10.7494/cmms.2016.3.0581.

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A Kampmann-Wagner type numerical precipitation model (KWN) has been implemented using NVIDIA's CUDA framework for numerical programming of the graphics processing unit (GPU). Different implementation strategies are discussed and subjected to performance When the KWN model is used in combination with other calculations that are processed by the CPU, the performance improvements can be such that the KWN model incurs only emph{negligible} additional execution time. Also if the KWN model is used standalone for a large case, the GPU implementation achieves good scalability and performance.measureme

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Sonam, Mahajan, and Singh Maninder. "ANALYSIS OF RSA ALGORITHM USING GPU PROGRAMMING." International Journal of Network Security & Its Applications (IJNSA) 6, no. 4 (2014): 13–28. https://doi.org/10.5281/zenodo.7055921.

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Modern-day computer security relies heavily on cryptography as a means to protect the data that we have become increasingly reliant on. The main research in computer security domain is how to enhance the speed of RSA algorithm. The computing capability of Graphic Processing Unit as a co-processor of the CPU can leverage massive-parallelism. This paper presents a novel algorithm for calculating modulo value that can process large power of numbers which otherwise are not supported by built-in data types. First the traditional algorithm is studied. Secondly, the parallelized RSA algorithm is desi

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DU, LIU-GE, KANG LI, FAN-MIN KONG, and YUAN HU. "PARALLEL 3D FINITE-DIFFERENCE TIME-DOMAIN METHOD ON MULTI-GPU SYSTEMS." International Journal of Modern Physics C 22, no. 02 (2011): 107–21. http://dx.doi.org/10.1142/s012918311101618x.

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Finite-difference time-domain (FDTD) is a popular but computational intensive method to solve Maxwell's equations for electrical and optical devices simulation. This paper presents implementations of three-dimensional FDTD with convolutional perfect match layer (CPML) absorbing boundary conditions on graphics processing unit (GPU). Electromagnetic fields in Yee cells are calculated in parallel millions of threads arranged as a grid of blocks with compute unified device architecture (CUDA) programming model and considerable speedup factors are obtained versus sequential CPU code. We extend the

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Zhu, Huming, Pei Li, Peng Zhang, and Zheng Luo. "A High Performance Parallel Ranking SVM with OpenCL on Multi-core and Many-core Platforms." International Journal of Grid and High Performance Computing 11, no. 1 (2019): 17–28. http://dx.doi.org/10.4018/ijghpc.2019010102.

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A ranking support vector machine (RSVM) is a typical pairwise method of learning to rank, which is effective in ranking problems. However, the training speed of RSVMs are not satisfactory, especially when solving large-scale data ranking problems. Recent years, many-core processing units (graphics processing unit (GPU), Many Integrated Core (MIC)) and multi-core processing units have exhibited huge superiority in the parallel computing domain. With the support of hardware, parallel programming develops rapidly. Open Computing Language (OpenCL) and Open Multi-Processing (OpenMP) are two of popu

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Altybay, Arshyn, Michael Ruzhansky, and Niyaz Tokmagambetov. "A parallel hybrid implementation of the 2D acoustic wave equation." International Journal of Nonlinear Sciences and Numerical Simulation 21, no. 7-8 (2020): 821–27. http://dx.doi.org/10.1515/ijnsns-2019-0227.

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AbstractIn this paper, we propose a hybrid parallel programming approach for a numerical solution of a two-dimensional acoustic wave equation using an implicit difference scheme for a single computer. The calculations are carried out in an implicit finite difference scheme. First, we transform the differential equation into an implicit finite-difference equation and then using the alternating direction implicit (ADI) method, we split the equation into two sub-equations. Using the cyclic reduction algorithm, we calculate an approximate solution. Finally, we change this algorithm to parallelize

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Boli Suban, Ignasius, Suyoto Suyoto, and Pranowo Pranowo. "Medical Image Segmentation Using a Combination of Lattice Boltzmann Method and Fuzzy Clustering Based on GPU CUDA Parallel Processing." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 11 (2021): 76. http://dx.doi.org/10.3991/ijoe.v17i11.24459.

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The rapid development of computer technology has had a significant influence on advances in medical science. This development concerns segmenting medical images that can be used to help doctors diagnose patient diseases. The boundary between objects contained in an image is captured using the level set function. The equation of the level set function is solved numerically by combining the Lattice Boltzmann (LBM) method and fuzzy clustering. Parallel processing using a graphical processing unit (GPU) accelerates the execution of the segmentation process. The results showed that image segmentati

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Bloch, Aurelien, Simone Casale-Brunet, and Marco Mattavelli. "Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis." Journal of Low Power Electronics and Applications 12, no. 3 (2022): 40. http://dx.doi.org/10.3390/jlpea12030040.

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Developing and fine-tuning software programs for heterogeneous hardware such as CPU/GPU processing platforms comprise a highly complex endeavor that demands considerable time and effort of software engineers and requires evaluating various fundamental components and features of both the design and of the platform to maximize the overall performance. The dataflow programming approach has proven to be an appropriate methodology for reaching such a difficult and complex goal for the intrinsic portability and the possibility of easily decomposing a network of actors on different processing units o

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Song, Zhe, Xing Mu, and Hou-Xing Zhou. "High Performance Computing of Complex Electromagnetic Algorithms Based on GPU/CPU Heterogeneous Platform and Its Applications to EM Scattering and Multilayered Medium Structure." International Journal of Antennas and Propagation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/9173062.

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The fast and accurate numerical analysis for large-scale objects and complex structures is essential to electromagnetic simulation and design. Comparing to the exploration in EM algorithms from mathematical point of view, the computer programming realization is coordinately significant while keeping up with the development of hardware architectures. Unlike the previous parallel algorithms or those implemented by means of parallel programming on multicore CPU with OpenMP or on a cluster of computers with MPI, the new type of large-scale parallel processor based on graphics processing unit (GPU)

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Gramacki, A., M. Sawerwain, and J. Gramacki. "FPGA-based bandwidth selection for kernel density estimation using high level synthesis approach." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 4 (2016): 821–29. http://dx.doi.org/10.1515/bpasts-2016-0091.

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Abstract Field-programmable gate arrays (FPGA) technology can offer significantly higher performance at much lower power consumption than is available from single and multicore CPUs and GPUs (graphics processing unit) in many computational problems. Unfortunately, the pure programming for FPGA using hardware description languages (HDL), like VHDL or Verilog, is a difficult and not-trivial task and is not intuitive for C/C++/Java programmers. To bring the gap between programming effectiveness and difficulty, the high level synthesis (HLS) approach is promoted by main FPGA vendors. Nowadays, tim

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Lai, Jianqi, Hua Li, Zhengyu Tian, and Ye Zhang. "A Multi-GPU Parallel Algorithm in Hypersonic Flow Computations." Mathematical Problems in Engineering 2019 (March 17, 2019): 1–15. http://dx.doi.org/10.1155/2019/2053156.

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Computational fluid dynamics (CFD) plays an important role in the optimal design of aircraft and the analysis of complex flow mechanisms in the aerospace domain. The graphics processing unit (GPU) has a strong floating-point operation capability and a high memory bandwidth in data parallelism, which brings great opportunities for CFD. A cell-centred finite volume method is applied to solve three-dimensional compressible Navier–Stokes equations on structured meshes with an upwind AUSM+UP numerical scheme for space discretization, and four-stage Runge–Kutta method is used for time discretization

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Rathnasuriya, Ravishka, Nidhi Majoju, Zihe Song, and Wei Yang. "An Investigation on Numerical Bugs in GPU Programs Towards Automated Bug Detection." Proceedings of the ACM on Software Engineering 2, ISSTA (2025): 1654–77. https://doi.org/10.1145/3728950.

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General-purpose graphics processing unit (GPU) computing has emerged as a leading parallel computing paradigm, offering significant performance gains in various domains such as scientific computing and deep learning. However, GPU programs are susceptible to numerical bugs, which can lead to incorrect results or crashes. These bugs are difficult to detect, debug, and fix due to their dependence on specific input values or types and the absence of reliable error-checking mechanisms and oracles. Additionally, the unique programming conventions of GPUs complicate identifying the root causes of bug

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Couturier, David, and Michel R. Dagenais. "LTTng CLUST: A System-Wide Unified CPU and GPU Tracing Tool for OpenCL Applications." Advances in Software Engineering 2015 (August 19, 2015): 1–14. http://dx.doi.org/10.1155/2015/940628.

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As computation schemes evolve and many new tools become available to programmers to enhance the performance of their applications, many programmers started to look towards highly parallel platforms such as Graphical Processing Unit (GPU). Offloading computations that can take advantage of the architecture of the GPU is a technique that has proven fruitful in recent years. This technology enhances the speed and responsiveness of applications. Also, as a side effect, it reduces the power requirements for those applications and therefore extends portable devices battery life and helps computing c

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Chen, Cheng, Zheng Wang, Deepak Majeti, et al. "Optimization of Lattice Boltzmann Simulation With Graphics-Processing-Unit Parallel Computing and the Application in Reservoir Characterization." SPE Journal 21, no. 04 (2016): 1425–35. http://dx.doi.org/10.2118/179733-pa.

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Summary Shale permeability is sufficiently low to require an unconventional scale of stimulation treatments, such as very-large-volume, high-rate, multistage hydraulic-fracturing applications. Upscaling of hydrocarbon transport processes in shales is challenging because of the low permeability and strong heterogeneity. Rock characterization with high-resolution imaging [X-ray tomography and scanning electron microscope (SEM)] is usually highly localized and contains significant uncertainties because of the small field of view. Therefore, an effective high-performance computing method is requir

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Souri, Milad, Pooria Akbarzadeh, and Hossein Mahmoodi Darian. "Parallel Thomas approach development for solving tridiagonal systems in GPU programming − steady and unsteady flow simulation." Mechanics & Industry 21, no. 3 (2020): 303. http://dx.doi.org/10.1051/meca/2020013.

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The solution of tridiagonal system of equations using graphic processing units (GPU) is assessed. The parallel-Thomas-algorithm (PTA) is developed and the solution of PTA is compared to two known parallel algorithms, i.e. cyclic-reduction (CR) and parallel-cyclic-reduction (PCR). Lid-driven cavity problem is considered to assess these parallel approaches. This problem is also simulated using the classic Thomas algorithm that runs on a central processing unit (CPU). Runtimes and physical parameters of the mentioned GPU and CPU algorithms are compared. The results show that the speedup of CR, PC

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Gnatowski, Andrzej, and Teodor Niżyński. "A Parallel Algorithm for Scheduling a Two-Machine Robotic Cell in Bicycle Frame Welding Process." Applied Sciences 11, no. 17 (2021): 8083. http://dx.doi.org/10.3390/app11178083.

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Welding frames with differing geometries is one of the most crucial stages in the production of high-end bicycles. This paper proposes a parallel algorithm and a mixed integer linear programming formulation for scheduling a two-machine robotic welding station. The time complexity of the introduced parallel method is O(log2n) on an n3-processor Exclusive Read Exclusive Write Parallel Random-Access Machine (EREW PRAM), where n is the problem size. The algorithm is designed to take advantage of modern graphics cards to significantly accelerate the computations. To present the benefits of the para

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Mohammadi, Sajad, Hamidreza Karami, Mohammad Azadifar, and Farhad Rachidi. "On the Efficiency of OpenACC-aided GPU-Based FDTD Approach: Application to Lightning Electromagnetic Fields." Applied Sciences 10, no. 7 (2020): 2359. http://dx.doi.org/10.3390/app10072359.

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An open accelerator (OpenACC)-aided graphics processing unit (GPU)-based finite difference time domain (FDTD) method is presented for the first time for the 3D evaluation of lightning radiated electromagnetic fields along a complex terrain with arbitrary topography. The OpenACC directive-based programming model is used to enhance the computational performance, and the results are compared with those obtained by using a CPU-based model. It is shown that OpenACC GPUs can provide very accurate results, and they are more than 20 times faster than CPUs. The presented results support the use of Open

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Semylitko, Mykola, and Gennadii Malaschonok. "Parallel SVD Algorithm for a Three-Diagonal Matrix on a Video Card Using the Nvidia CUDA Architecture." NaUKMA Research Papers. Computer Science 4 (December 10, 2021): 16–22. http://dx.doi.org/10.18523/2617-3808.2021.4.16-22.

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SVD (Singular Value Decomposition) algorithm is used in recommendation systems, machine learning, image processing, and in various algorithms for working with matrices which can be very large and Big Data, so, given the peculiarities of this algorithm, it can be performed on a large number of computing threads that have only video cards.CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general purpose processing – an approach termed GPGPU (g

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Ubbiali, Stefano, Christian Kühnlein, Christoph Schär, et al. "Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes." Geoscientific Model Development 18, no. 2 (2025): 529–46. https://doi.org/10.5194/gmd-18-529-2025.

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Abstract. We explore the domain-specific Python library GT4Py (GridTools for Python) for implementing a representative physical parametrization scheme and the related tangent-linear and adjoint algorithms from the Integrated Forecasting System (IFS) of ECMWF. GT4Py encodes stencil operators in an abstract and hardware-agnostic fashion, thus enabling more concise, readable, and maintainable scientific applications. The library achieves high performance by translating the application into targeted low-level coding implementations. Here, the main goal is to study the correctness and performance p

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Stankevičš, Jānis, and Sergejs Kodors. "NVIDIA HIGH PERFORMANCE COMPUTING SOLUTION CUDA APPLICATION FOR IMAGE PROCESSING." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 22 (April 25, 2018): 63–68. http://dx.doi.org/10.17770/het2018.22.3634.

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The goal of this research report is to compare the program execution times using the Central Processing Unit and NVIDIA’s high throughput solution CUDA to execute the program on the Graphic Processing Unit. The programs will run an algorithm to convert a raster image into dotted half-tone. CUDA`s programming model allows programmers to run their programs on a parallel GPU architecture consisting of several blocks of threads that are being ran in parallel. This dynamic technology allows to further decrease the time it takes to run a program, by simply adding more GPU’s or upgrading the existing

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Navarro, Cristóbal A., Nancy Hitschfeld-Kahler, and Luis Mateu. "A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures." Communications in Computational Physics 15, no. 2 (2014): 285–329. http://dx.doi.org/10.4208/cicp.110113.010813a.

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AbstractParallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions. The evolution of computer architectures (multi-coreandmany-core) towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm. In the last decade, the graphics processing unit, or GPU, has gained an important place in the field of high performance computing (HPC) because of its low cost and massive parallel processing power. Super-computing has become, for the first time,

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