Relevant bibliographies by topics / Graphics Processing Unit (GPU) Programming

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Graphics Processing Unit (GPU) Programming'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Graphics Processing Unit (GPU) Programming"

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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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Dissertations / Theses on the topic "Graphics Processing Unit (GPU) Programming"

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Gibson, Michael John. "Genetic programming and cellular automata for fast flood modelling on multi-core CPU and many-core GPU computers." Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/20364.

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Many complex systems in nature are governed by simple local interactions, although a number are also described by global interactions. For example, within the field of hydraulics the Navier-Stokes equations describe free-surface water flow, through means of the global preservation of water volume, momentum and energy. However, solving such partial differential equations (PDEs) is computationally expensive when applied to large 2D flow problems. An alternative which reduces the computational complexity, is to use a local derivative to approximate the PDEs, such as finite difference methods, or
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McCall, Andrew James. "Multi-level Parallelism with MPI and OpenACC for CFD Applications." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78203.

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High-level parallel programming approaches, such as OpenACC, have recently become popular in complex fluid dynamics research since they are cross-platform and easy to implement. OpenACC is a directive-based programming model that, unlike low-level programming models, abstracts the details of implementation on the GPU. Although OpenACC generally limits the performance of the GPU, this model significantly reduces the work required to port an existing code to any accelerator platform, including GPUs. The purpose of this research is twofold: to investigate the effectiveness of OpenACC in developi
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Pickering, Brent Phillip. "Evaluating the OpenACC API for Parallelization of CFD Applications." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50487.

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Directive-based programming of graphics processing units (GPUs) has recently appeared as a viable alternative to using specialized low-level languages such as CUDA C and OpenCL for general-purpose GPU programming. This technique, which uses directive or pragma statements to annotate source codes written in traditional high-level languages, is designed to permit a unified code base to serve multiple computational platforms and to simplify the transition of legacy codes to new architectures. This work analyzes the popular OpenACC programming standard, as implemented by the PGI compiler suite,
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Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.

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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chun
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Lundgren, Jacob. "Pricing of American Options by Adaptive Tree Methods on GPUs." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-265257.

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An assembled algorithm for pricing American options with absolute, discrete dividends using adaptive lattice methods is described. Considerations for hardware-conscious programming on both CPU and GPU platforms are discussed, to provide a foundation for the investigation of several approaches for deploying the program onto GPU architectures. The performance results of the approaches are compared to that of a central processing unit reference implementation, and to each other. In particular, an approach of designating subtrees to be calculated in parallel by allowing multiple calculation of ove
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Rocha, João Miguel Lopes de Almeida. "Aceleração GPU da animação de superfícies deformáveis." Master's thesis, FCT - UNL, 2008. http://hdl.handle.net/10362/1880.

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Dissertação de Mestrado em Engenharia Informática<br>A simulação de tecidos virtuais desempenha um papel importante em diversas áreas, como as indústrias dos jogos de computador e do cinema, sendo um tópico de investigação com grande actividade. A simulação é, normalmente, efectuada recorrendo a sistemas de partículas. Sobre as partículas são, de uma forma geral, definidas uma série de interacções com base num modelo físico de superfície, que caracteriza as propriedades do tecido, sobretudo no que diz respeito às suas deformações internas. A simulação é uma tarefa de computação extremamente i
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Kelly, Jesse. "Numerical solution of the two-phase incompressible navier-stokes equations using a gpu-accelerated meshless method." Honors in the Major Thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1277.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Engineering and Computer Science<br>Mechanical Engineering
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Coimbra, Andre Rodrigues. "Método automático para descoberta de funções de ordenação utilizando programação genética paralela em GPU." Universidade Federal de Goiás, 2014. http://repositorio.bc.ufg.br/tede/handle/tede/4525.

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Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-05-15T13:33:06Z No. of bitstreams: 2 Dissertação - André Rodrigues Coimbra - 2014.pdf: 5214859 bytes, checksum: d951502129d7be5d60b6a785516c3ad1 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)<br>Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-05-15T13:37:45Z (GMT) No. of bitstreams: 2 Dissertação - André Rodrigues Coimbra - 2014.pdf: 5214859 bytes, checksum: d951502129d7be5d60b6a785516c3ad1 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)
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Flannery, Rebecca Lynn. "A hybrid fluid simulation on the Graphics Processing Unit (GPU)." Texas A&M University, 2008. http://hdl.handle.net/1969.1/85974.

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This thesis presents a method to implement a hybrid particle/grid uid simulation on graphics hardware. The goal is to speed up the simulation by exploiting the parallelism of the graphics processing unit, or GPU. The Fluid Implicit Particle method is adapted to the programming style of the GPU. The methods were implemented on a current generation graphics card. The GPU based program exhibited a small speedup over its CPU based counterpart.
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Passerat-Palmbach, Jonathan. "Contributions to parallel stochastic simulation : application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte Carlo simulations." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2013. http://tel.archives-ouvertes.fr/tel-00858735.

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The race to computing power increases every day in the simulation community. A few years ago, scientists have started to harness the computing power of Graphics Processing Units (GPUs) to parallelize their simulations. As with any parallel architecture, not only the simulation model implementation has to be ported to the new parallel platform, but all the tools must be reimplemented as well. In the particular case of stochastic simulations, one of the major element of the implementation is the pseudorandom numbers source. Employing pseudorandom numbers in parallel applications is not a straigh
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Books on the topic "Graphics Processing Unit (GPU) Programming"

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Engel, Wolfgang. GPU pro 6: Advanced rendering techniques. CRC Press/Taylor Francis Group, 2016.

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Matt, Pharr, and Fernando Randima, eds. GPU gems: Programming techniques for high- performance graphics and general-purpose computation. Addison-Wesley, 2005.

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Matt, Pharr, and Fernando Randima, eds. GPU gems 2: Programming techniques for high- performance graphics and general-purpose computation. Addison-Wesley, 2005.

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F, Engel Wolfgang, ed. GPU Pro: Advanced rendering techniques. A K Peters, 2010.

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Edward, Kandrot, ed. CUDA by example: An introduction to general-purpose GPU programming. Addison-Wesley, 2010.

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Fernando, Randima, and Matt Pharr. GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation (Gpu Gems). Addison-Wesley Professional, 2005.

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Engel, Wolfgang. GPU Pro 6: Advanced Rendering Techniques. CRC Press LLC, 2015.

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Engel, Wolfgang. GPU Pro 6: Advanced Rendering Techniques. CRC Press LLC, 2015.

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Accelerating MATLAB with GPU Computing. Elsevier Science & Technology, 2013.

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GPU Pro2: Advanced rendering techniques. AK Peters, 2011.

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Book chapters on the topic "Graphics Processing Unit (GPU) Programming"

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Reinders, James, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, and Xinmin Tian. "Programming for GPUs." In Data Parallel C++. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5574-2_15.

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Abstract Over the last few decades, Graphics Processing Units (GPUs) have evolved from specialized hardware devices capable of drawing images on a screen to general-purpose devices capable of executing complex parallel kernels. Nowadays, nearly every computer includes a GPU alongside a traditional CPU, and many programs may be accelerated by offloading part of a parallel algorithm from the CPU to the GPU.
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Kim, Hyesoon, Richard Vuduc, Sara Baghsorkhi, Jee Choi, and Wen-mei Hwu. "GPU Design, Programming, and Trends." In Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU). Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01737-7_1.

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Lu, Xinzheng, and Zhen Xu. "Graphics Processing Unit (GPU) Technology in Earthquake Engineering, Application of." In Encyclopedia of Earthquake Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36197-5_64-1.

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Lu, Xinzheng, and Zhen Xu. "Graphics Processing Unit (GPU) Technology in Earthquake Engineering, Application of." In Encyclopedia of Earthquake Engineering. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35344-4_64.

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Hall, Will, and Yun Tian. "Neural Networks Training on Graphics Processing Unit (GPU) Using Dynamic Parallelism (DP)." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16078-3_56.

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Wang, Liang, and Jianxin Zhao. "Performance Accelerators." In Architecture of Advanced Numerical Analysis Systems. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8853-5_7.

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AbstractThe Graphics Processing Unit (GPU) has become one of the most important types of hardware accelerators. It is designed to render 3D graphics and videos and still is core to the gaming industry. Besides creating stunning visual effects, programmers also take advantage of the GPU’s advantage in parallel processing in many fields to perform computing-heavy tasks, such as in health data analytics, physical simulation, artificial intelligence, etc.
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Adhikari, Mainak, and Sukhendu Kar. "Advanced Topics GPU Programming and CUDA Architecture." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8853-7.ch008.

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Graphics processing unit (GPU), which typically handles computation only for computer graphics. Any GPU providing a functionally complete set of operations performed on arbitrary bits can compute any computable value. Additionally, the use of multiple graphics cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs). CUDA gives program developers dire
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Khemiri, Randa, Soulef Bouaafia, Asma Bahba, Maha Nasr, and Fatma Ezahra Sayadi. "Performance Analysis of OpenCL and CUDA Programming Models for the High Efficiency Video Coding." In Digital Image Processing - Advances and Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99823.

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In Motion estimation (ME), the block matching algorithms have a great potential of parallelism. This process of the best match is performed by computing the similarity for each block position inside the search area, using a similarity metric, such as Sum of Absolute Differences (SAD). It is used in the various steps of motion estimation algorithms. Moreover, it can be parallelized using Graphics Processing Unit (GPU) since the computation algorithm of each block pixels is similar, thus offering better results. In this work a fixed OpenCL code was performed firstly on several architectures as C
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Holk Eric, Byrd William, Mahajan Nilesh, Willcock Jeremiah, Chauhan Arun, and Lumsdaine Andrew. "Declarative Parallel Programming for GPUs." In Advances in Parallel Computing. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-041-3-297.

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The recent rise in the popularity of Graphics Processing Units (GPUs) has been fueled by software frameworks, such as NVIDIA's Compute Unified Device Architecture (CUDA) and Khronos Group's OpenCL that make GPUs available for general purpose computing. However, CUDA and OpenCL are still low-level approaches that require users to handle details about data layout and movement across levels of memory hierarchy. We propose a declarative approach to coordinating computation and data movement between CPU and GPU, through a domain-specific language that we called Harlan. Not only does a declarative l
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Cecilia José María, García José Manuel, and Ujaldón Manuel. "The GPU on the Matrix-Matrix Multiply: Performance Study and Contributions." In Advances in Parallel Computing. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-530-3-331.

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Modern graphics processing units (GPUs) have been at the leading edge of increasing chip-level parallelism over the last ten years, and the CUDA programming model has recently allowed us to exploit its power across many computational domains. Within them, dense linear algebra algorithms emerge like a natural fit for CUDA and the GPU because they are usually inherently parallel and can naturally be expressed as a blocked computation. In this paper, we extensively analyze the GPU programming and performance of one of the fundamental building blocks in numerical lineal algebra algorithms: The Mat
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Conference papers on the topic "Graphics Processing Unit (GPU) Programming"

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Cabezas, Javier, Marc Jordà, Isaac Gelado, Nacho Navarro, and Wen-mei Hwu. "GPU-SM: shared memory multi-GPU programming." In GPGPU-8: General-purpose Processing with Graphics Processing Units 8. ACM, 2015. http://dx.doi.org/10.1145/2716282.2716286.

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Gisbert, Fernando, Roque Corral, and Guillermo Pastor. "Implementation of an Edge-Based Navier-Stokes Solver for Unstructured Grids in Graphics Processing Units." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46224.

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The implementation of an edge-based three-dimensional RANS equations solver for unstructured grids that runs on both central processing units (CPUs) and graphics processing units (GPUs) is presented. This CPU/GPU duality is kept without double-writing the code, reducing programming and maintenance costs. The GPU implementation is based on the standard OpenCL language. The code has been parallelized using MPI. Some turbomachinery benchmark cases are presented. For all cases, an order of magnitude reduction in computational time is achieved when the code is executed on GPUs instead of CPUs.
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Li, Jianhua, Yan Wang, Jingyuan Chen, and Li Yan. "GPU-Based Parallel Simulation of Silicon Anisotropic Etching." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71267.

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Silicon anisotropic etching simulation, based on geometric model or cellular automata (CA) model, is highly time-consuming. In this paper, we propose two parallelization methods for the simulation of the silicon anisotropic etching process with CA models on graphics processing units (GPUs). One is the direct parallelization of the serial CA algorithm, and the other is to use a spatial parallelization strategy where each crystal unit cell is allocated to a thread in GPU. The proposed simulation methods are implemented with the Compute Unified Device Architecture (CUDA) application programming i
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Li, Jianhua, Jingyuan Chen, Yan Wang, and Jianhua Huang. "Clustered Cell Parallelization for GPU Computing of Silicon Anisotropic Etching Simulation." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12965.

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The parallelization of silicon anisotropic etching simulation with the cellular automata (CA) model on graphics processing units (GPUs) is challenging, because the numbers of computational tasks in etching simulation dynamically change and the existing parallel CA mechanisms do not fit in GPU computation well. In this paper, an improved CA model, called clustered cell model, is proposed for GPU-based etching simulation. The model consists of clustered cells, each of which manages a scalable number of atoms. In this model, only the etching and update of states for the atoms on the etching surfa
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McCormick, Matthew, and Paul Elliott. "ITK-Wasm." In Python in Science Conference. SciPy, 2024. http://dx.doi.org/10.25080/tcfj5130.

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In recent years, WebAssembly (Wasm) has emerged as a widely-supported technology that offers high performance, compact binary size, support for multiple languages, hardware independence, security, and universal platform support, enabling developers to bring near-native speeds and portability to applications for the web and beyond. ITK-Wasm brings WebAssembly’s capabilities to scientific computing by combining the Insight Toolkit (ITK) and WebAssembly to enable high-performance spatial analysis across programming languages and hardware architectures.In the scientific Python ecosystem, ITK-Wasm
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Middya, Usuf, Abdulrahman Manea, Maitham Alhubail, Todd Ferguson, Thomas Byer, and Ali Dogru. "A Massively Parallel Reservoir Simulator on the GPU Architecture." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203918-ms.

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Abstract Reservoir simulation computational costs have been continuously growing due to high-resolution reservoir characterization, increasing model complexity, and uncertainty analysis workflows. Reducing simulation costs by upscaling is often necessary for operational requirements. Fast evolving High-Performance-Computing (HPC) technologies offer opportunities to reduce cost without compromising fidelity. This work presents a novel in-house massively parallel full-physics reservoir simulator running on the emerging graphics processing unit (GPU) architecture. Almost all the simulation kernel
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Negrut, Dan, Alessandro Tasora, and Mihai Anitescu. "Large-Scale Parallel Multibody Dynamics With Frictional Contact on the GPU." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2139.

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In the context of simulating the frictional contact dynamics of large systems of rigid bodies, this paper reviews a novel method for solving large cone complementarity problems by means of a fixed-point iteration algorithm. The method is an extension of the Gauss-Seidel and Gauss-Jacobi methods with overrelaxation for symmetric convex linear complementarity problems. Convergent under fairly standard assumptions, the method is implemented in a parallel framework by using a single instruction multiple data (SIMD) computation paradigm promoted by the Compute Unified Device Architecture (CUDA) lib
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Tasora, Alessandro, Dan Negrut, and Mihai Anitescu. "A GPU-Based Implementation of a Cone Convex Complementarity Approach for Simulating Rigid Body Dynamics With Frictional Contact." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66766.

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In the context of simulating the frictional contact dynamics of large systems of rigid bodies, this paper reviews a novel method for solving large cone complementarity problems by means of a fixed-point iteration algorithm. The method is an extension of the Gauss-Seidel and Gauss-Jacobi methods with over-relaxation for symmetric convex linear complementarity problems. Convergent under fairly standard assumptions, the method is implemented in a parallel framework by using a single instruction multiple data computation paradigm promoted by the Compute Unified Device Architecture library for grap
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Vulcan, Alexandru mihai, Radu nicolae Pietraru, and Maximilian Nicolae. "VISUAL TOOL FOR LEARNING GPU PROGRAMMING." In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-057.

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Graphic Processing Units (GPUs) are unanimously considered as powerful computational resources. General-purpose computing on GPU (GPGPU), as well, is the de facto infrastructure for most of the today computationally intensive problems that researchers all over the globe dill with. High Performance Computing (HPC) facilities use state of the art GPUs. Many domains like deep learning, machine learning, and computational finance uses GPU's for decreasing the execution time. GPUs are widely used in data centers for high performance computing where virtualization techniques are intended for optimiz
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Collet, Pierre, and Simon Harding. "Evolutionary algorithms and genetic programming on graphic processing units (GPU)." In the fourteenth international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330933.

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Reports on the topic "Graphics Processing Unit (GPU) Programming"

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Collins, Pat. Graphics Processing Unit (GPU) Performance on an N-Body Problem. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada512706.

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