Date of Award

Summer 8-7-2020

Degree Type

Thesis-Restricted

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Dr. Shaikh M Arifuzzaman

Second Advisor

Dr. Minhaz Zibran

Third Advisor

Dr. Tamjidul Hoque

Abstract

An algorithm designer working with parallel computing systems should know how the characteristics of their implemented algorithm affects various performance aspects of their parallel program. It would be beneficial to these designers if each algorithm came with a specific set of standards that identified which algorithms worked better for a specified system. Therefore, the goal of this paper is to take implementations of four graphing algorithms, extract their features such as memory consumption, scalability using profilers (Vtunes /Tau) to determine which algorithms work to their fullest potential in one of the three systems: GPU, shared memory system, or distributed memory system. The features extracted in this study were scalability, speedup, and parallel efficiency. We find that when looking at various parallel algorithms: Community Detection, Communities through Directed Affiliations (Coda), BigClam, and Breadth First Search all achieved noticeable speedup with increasing number of cores.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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