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Access Type
Open Access
Document Type
thesis
Degree Program
Mechanical Engineering
Degree Type
Master of Science in Mechanical Engineering (M.S.M.E.)
Year Degree Awarded
January 2008
Month Degree Awarded
September
Keywords
CFD, GPU
Abstract
High performance streaming processors have achieved the distinction of being very efficient and cost-effective in terms of floating-point capacity, thereby making them an attractive option for scientific algorithms that involve large arithmetic effort. Graphics Processing Units (GPUs) are an example of this new initiative to bring vector-processing to desktop computers; and with the advent of 32-bit floating-point capabilities, these architectures provide a versatile platform for the efficient implementation of such algorithms. To exemplify this, the implementation of a Conjugate Gradient iterative solver for PDE solutions on unstructured two- and three-dimensional grids using such hardware is described. This would greatly benefit applications such as fluid-flow solvers which seek efficient methods to solve large sparse systems. The implementation has also been successfully incorporated into an existing object oriented CFD code, thereby enabling the option of using these architectures as efficient math co-processors in the computational framework.
DOI
https://doi.org/10.7275/582206
First Advisor
Blair Perot