scholarly journals Voxelization Parallelism Using CUDA Architecture

2020 ◽  
Vol 25 (1) ◽  
pp. 1-11
Author(s):  
Sura Alrawy ◽  
Fakhrulddin Ali
Keyword(s):  
Cuda Architecture

Parallel CUDA Architecture for Solving de VRP with ACO

2012 ◽  
pp. 385-393 ◽  
Author(s):  
Francisco Javier Diego ◽  
Eva María Gómez ◽  
Miguel Ortega-Mier ◽  
Álvaro García-Sánchez
Keyword(s):  
Cuda Architecture

scholarly journals Implementation of algebraic procedures on the GPU using CUDA architecture on the example of generalized eigenvalue problem

2016 ◽  
Vol 6 (1) ◽  
pp. 79-90
Author(s):  
Łukasz Syrocki ◽  
Grzegorz Pestka
Keyword(s):  
Eigenvalue Problem ◽  
Graphics Processing Unit ◽  
Generalized Eigenvalue Problem ◽  
Processing Unit ◽  
Graphics Processors ◽  
Central Processing ◽  
Generalized Eigenvalue ◽  
Cuda Technology ◽  
Cuda Architecture ◽  
High Level

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.

A new CUDA-based GPU implementation of the two-dimensional Athena code

2013 ◽  
Vol 61 (1) ◽  
pp. 239-250
Author(s):  
A. Wasiljew ◽  
K. Murawski
Keyword(s):  
Dimensional Space ◽  
Test Cases ◽  
Double Precision ◽  
Central Processor ◽  
Two Dimensional ◽  
Central Processor Unit ◽  
Hardware Configuration ◽  
Cuda Architecture ◽  
Processor Unit ◽  
Gpu Implementation

Abstract We present a new version of the Athena code, which solves magnetohydrodynamic equations in two-dimensional space. This new implementation, which we have named Athena-GPU, uses CUDA architecture to allow the code execution on Graphical Processor Unit (GPU). The Athena-GPU code is an unofficial, modified version of the Athena code which was originally designed for Central Processor Unit (CPU) architecture. We perform numerical tests based on the original Athena-CPU code and its GPU counterpart to make a performance analysis, which includes execution time, precision differences and accuracy. We narrowed our tests and analysis only to double precision floating point operations and two-dimensional test cases. Our comparison shows that results are similar for both two versions of the code, which confirms correctness of our CUDA-based implementation. Our tests reveal that the Athena-GPU code can be 2 to 15-times faster than the Athena-CPU code, depending on test cases, the size of a problem and hardware configuration.

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