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gNek: A GPU Accelerated Incompressib...

Stilwell, Nichole.

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gNek: A GPU Accelerated Incompressible Navier Stokes Solver.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	gNek: A GPU Accelerated Incompressible Navier Stokes Solver./
作者:	Stilwell, Nichole.
面頁冊數:	142 p.
附註:	Source: Masters Abstracts International, Volume: 52-04.
Contained By:	Masters Abstracts International52-04(E).
標題:	Applied Mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1524727
ISBN:	9781303621581

gNek: A GPU Accelerated Incompressible Navier Stokes Solver.
Stilwell, Nichole.

gNek: A GPU Accelerated Incompressible Navier Stokes Solver. - 142 p.

Source: Masters Abstracts International, Volume: 52-04.

Thesis (M.S.)--Rice University, 2013.

This thesis presents a GPU accelerated implementation of a high order splitting scheme with a spectral element discretization for the incompressible Navier Stokes (INS) equations. While others have implemented this scheme on clusters of processors using the Nek5000 code, to my knowledge this thesis is the first to explore its performance on the GPU. This work implements several of the Nek5000 algorithms using OpenCL kernels that efficiently utilize the GPU memory architecture, and achieve massively parallel on-chip computations. These rapid computations have the potential to significantly enhance computational fluid dynamics (CFD) simulations that arise in areas such as weather modeling and aircraft design procedures. I present convergence results for several test cases including channel, shear, Kovasznay, and lid-driven cavity flow problems, which achieve the proven convergence results.

ISBN: 9781303621581Subjects--Topical Terms:

1669109
Applied Mathematics.

gNek: A GPU Accelerated Incompressible Navier Stokes Solver.
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520 $a This thesis presents a GPU accelerated implementation of a high order splitting scheme with a spectral element discretization for the incompressible Navier Stokes (INS) equations. While others have implemented this scheme on clusters of processors using the Nek5000 code, to my knowledge this thesis is the first to explore its performance on the GPU. This work implements several of the Nek5000 algorithms using OpenCL kernels that efficiently utilize the GPU memory architecture, and achieve massively parallel on-chip computations. These rapid computations have the potential to significantly enhance computational fluid dynamics (CFD) simulations that arise in areas such as weather modeling and aircraft design procedures. I present convergence results for several test cases including channel, shear, Kovasznay, and lid-driven cavity flow problems, which achieve the proven convergence results.
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