Solving the Caputo Fractional Reaction-Diffusion Equation on GPU

We present a parallel GPU solution of the Caputo fractional reaction-diffusion equation in one spatial dimension with explicit finite difference approximation. The parallel solution, which is implemented with CUDA programming model, consists of three procedures: preprocessing, parallel solver, and p...

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Main Authors: Jie Liu, Chunye Gong, Weimin Bao, Guojian Tang, Yuewen Jiang
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2014/820162
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author Jie Liu
Chunye Gong
Weimin Bao
Guojian Tang
Yuewen Jiang
author_facet Jie Liu
Chunye Gong
Weimin Bao
Guojian Tang
Yuewen Jiang
author_sort Jie Liu
collection DOAJ
description We present a parallel GPU solution of the Caputo fractional reaction-diffusion equation in one spatial dimension with explicit finite difference approximation. The parallel solution, which is implemented with CUDA programming model, consists of three procedures: preprocessing, parallel solver, and postprocessing. The parallel solver involves the parallel tridiagonal matrix vector multiplication, vector-vector addition, and constant vector multiplication. The most time consuming loop of vector-vector addition and constant vector multiplication is optimized and impressive performance improvement is got. The experimental results show that the GPU solution compares well with the exact solution. The optimized GPU solution on NVIDIA Quadro FX 5800 is 2.26 times faster than the optimized parallel CPU solution on multicore Intel Xeon E5540 CPU.
format Article
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institution Kabale University
issn 1026-0226
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language English
publishDate 2014-01-01
publisher Wiley
record_format Article
series Discrete Dynamics in Nature and Society
spelling doaj-art-7e89ab048c5346ae93420f50f13c65ad2025-02-03T06:11:17ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2014-01-01201410.1155/2014/820162820162Solving the Caputo Fractional Reaction-Diffusion Equation on GPUJie Liu0Chunye Gong1Weimin Bao2Guojian Tang3Yuewen Jiang4School of Computer Science, National University of Defense Technology, Changsha 410073, ChinaSchool of Computer Science, National University of Defense Technology, Changsha 410073, ChinaScience and Technology on Space Physics Laboratory, Beijing 100076, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaDepartment of Engineering Science, University of Oxford, Oxford OX2 0ES, UKWe present a parallel GPU solution of the Caputo fractional reaction-diffusion equation in one spatial dimension with explicit finite difference approximation. The parallel solution, which is implemented with CUDA programming model, consists of three procedures: preprocessing, parallel solver, and postprocessing. The parallel solver involves the parallel tridiagonal matrix vector multiplication, vector-vector addition, and constant vector multiplication. The most time consuming loop of vector-vector addition and constant vector multiplication is optimized and impressive performance improvement is got. The experimental results show that the GPU solution compares well with the exact solution. The optimized GPU solution on NVIDIA Quadro FX 5800 is 2.26 times faster than the optimized parallel CPU solution on multicore Intel Xeon E5540 CPU.http://dx.doi.org/10.1155/2014/820162
spellingShingle Jie Liu
Chunye Gong
Weimin Bao
Guojian Tang
Yuewen Jiang
Solving the Caputo Fractional Reaction-Diffusion Equation on GPU
Discrete Dynamics in Nature and Society
title Solving the Caputo Fractional Reaction-Diffusion Equation on GPU
title_full Solving the Caputo Fractional Reaction-Diffusion Equation on GPU
title_fullStr Solving the Caputo Fractional Reaction-Diffusion Equation on GPU
title_full_unstemmed Solving the Caputo Fractional Reaction-Diffusion Equation on GPU
title_short Solving the Caputo Fractional Reaction-Diffusion Equation on GPU
title_sort solving the caputo fractional reaction diffusion equation on gpu
url http://dx.doi.org/10.1155/2014/820162
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