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GPU hardware acceleration for industrial applications : using computation to push beyond physical limitations

University of British Columbia

This thesis explores the possibility of utilizing Graphics Processing Units (GPUs) to address the computational demand of algorithms used to mitigate the inherent physical limitations in devices such as microscopes and 3D-scanners. We investigate the outcome and test our methodology for the following case studies: - the narrow field of view found in microscopes. - the limited pixel-resolution available in active 3D sensing technologies such as laser scanners. The algorithms that offer to mitigate these limitations suffer from high computational requirements, rendering them ineffective for time-sensitive applications. In our methodology we exploit parallel programming and software engineering practices to efficiently harness the GPU's potential to provide the needed computational performance. Our goal is to show that it is feasible to use GPU hardware acceleration to address computational requirements of these algorithms for time-sensitive industrial applications. The results of this work demonstrate the potential for using GPU hardware acceleration in meeting computational requirements of such applications. We achieved twice the performance required to algorithmically extend the narrow field of view in microscopes for micro-pathology, and we reached the performance required to upsample the pixel-resolution of a 3D scanner in real-time, for use in autonomous excavation and collision detection in mining.

Text

2013-12-09

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/45599

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/