Date of Award

Summer 8-21-2015

Level of Access

Open-Access Thesis

Degree Name

Master of Science (MS)

Department

Computer Engineering

Advisor

Bruce Segee

Second Committee Member

Yifeng Zhu

Third Committee Member

Peter Koons

Abstract

GPGPUs offer significant computational power for programmers to leverage. This computational power is especially useful when utilized for accelerating scientific models. This thesis analyzes the utilization of GPGPU programming to accelerate scientific computing models.

First the construction of hardware for visualization and computation of scientific models is discussed. Several factors in the construction of the machines focus on the performance impacts related to scientific modeling.

Image processing is an embarrassingly parallel problem well suited for GPGPU acceleration. An image processing library was developed to show the processes of recognizing embarrassingly parallel problems and serves as an excellent example of converting from a serial CPU implementation to a GPU accelerated implementation. Genetic algorithms are biologically inspired heuristic search algorithms based on natural selection. The Tetris genetic algorithm with A* pathfinding discusses memory bound limitations that can prevent direct algorithm conversions from the CPU to the GPU. An analysis of an existing landscape evolution model, CHILD, for GPU acceleration explores that even when a model shows promise for GPU acceleration, the underlying data structures can have a significant impact upon that ability to move to a GPU implementation. CHILD also offers an example of creating tighter MATLAB integration between existing models.

Lastly, a parallel spatial sorting algorithm is discussed as a possible replacement for current spatial sorting algorithms implemented in models such as smoothed particle hydrodynamics.

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