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

Improvement Study of EEMD Decomposition Efficiency Based on CUDA Architecture

2013 ◽  
Vol 8 (5) ◽  
pp. 200-209
Author(s):  
Zhu Xiaojun ◽  
Lv Shiqin ◽  
Yu Xueli
Keyword(s):  
Cuda Architecture ◽  
Decomposition Efficiency

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.

scholarly journals Analysis of Fast Fourier Transformations algorithm for CUDA Architecture

2012 ◽  
Vol 53 ◽  
Author(s):  
Beatričė Andziulienė ◽  
Evaldas Žulkas ◽  
Audrius Kuprinavičius
Keyword(s):  
Graphics Processing Unit ◽  
General Purpose ◽  
Fast Fourier Transformation ◽  
Processing Unit ◽  
Data Allocation ◽  
Analysis Method ◽  
Central Processing ◽  
Execution Speed ◽  
Cuda Architecture ◽  
Graphics Processing

In this work Fast Fourier transformation algorithm for general purpose graphics processing unit processing (GPGPU) is discussed. Algorithm structure and individual stages performance were analysed. With performance analysis method algorithm distribution and data allocation possibilities were determined, depending on algorithm stages execution speed and algorithm structure. Ratio between CPU and GPU execution during Fast Fourier transform signal processing was determined using computer-generated data with frequency. When adopting CPU code for CUDA execution, it not becomes more complex, even if stream procesor parallelization and data transfering algorith stages are considered. But central processing unit serial execution).

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