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A Comparative Analysis of the Performance of Scalable Parallel Patterns Applied to Genetic Algorithms and Configured for NVIDIA GPUs

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Title: A Comparative Analysis of the Performance of Scalable Parallel Patterns Applied to Genetic Algorithms and Configured for NVIDIA GPUs
Author: Radford, Dave
Department: School of Computer Science
Program: Computer Science
Advisor: Calvert, DaveGrewal, Gary
Abstract: Parallel programming is becoming the norm for modern computer programming. In order to utilize system resources effectively, programmers can use programming patterns to improve their programs. Parallel programming patterns are built upon a foundation of serial programming patterns to maximize the efficiency of parallel code and effectively use parallel resources available in a given system. This thesis focuses on using NVIDIA GPUs with the CUDA C library for parallel computing. The goal is to successfully implement two parallel versions of a genetic algorithm using the Map and Fork-Join parallel patterns to improve its performance compared to an equivalent serial genetic algorithm. The intent is to demonstrate that the parallel patterns can be implemented successfully on the CUDA platform and achieve increases in speedup, efficiency, and scalability with the parallel genetic algorithms. A comparative assessment of the two parallel patterns is conducted by configuring them to evaluate instances of the Travelling Salesman Problem using four different datasets. This assessment considers each algorithm's runtime performance, their use of system resources, and the amount of parallel overhead they use. The results of this assessment are used to determine which parallel algorithm performed best.
URI: http://hdl.handle.net/10214/9762
Date: 2016-06
Rights: Attribution 2.5 Canada


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Attribution 2.5 Canada Except where otherwise noted, this item's license is described as Attribution 2.5 Canada