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dc.contributor.advisorAlom, Md. Zahangir
dc.contributor.advisorAli, Ashfaque
dc.contributor.authorZawad, Syed Amer
dc.date.accessioned2014-05-14T07:15:36Z
dc.date.available2014-05-14T07:15:36Z
dc.date.issued2014-04
dc.identifier.otherID 13301094
dc.identifier.otherID 10101009
dc.identifier.urihttp://hdl.handle.net/10361/3226
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.description.abstractThis paper was conducted to analyze the performance benefits of parallelizing the Adaptive Weighted Sub-patterned Principle Component Analysis (Aw SP PCA) algorithm, given that the algorithm is implemented so as to retain the accuracy from its serialized version. The serialized execution of this algorithm is analyzed first and then compared against its parallel implementation, both compiled and run on the same computer. Throughout this paper, the methodology is to undergo a step by step procedure which can clearly outline and describe the problems faced when trying to parallelize this algorithm. It will also describe where, how and why parallelizing procedures were used. The results of the research have shown that while not all parts of the algorithm can be implemented in parallel in the first place, some of the sections that can be parallelized does not necessarily yield a considerable amount of benefits. Also, it was seen that not all sections scale well with problem size, meaning that some portions of the algorithm can be left in its serialized state without much loss in time. The sections which can be parallelized were discussed in detail. Some changes were also made to certain variables to ensure the best accuracy possible. Finally, through analysis and experimentation, a speedup of 2.76 was achieved, with a recognition accuracy of 92.6%.en_US
dc.description.statementofresponsibilitySyed Amer Zawad
dc.description.statementofresponsibilityAshfaque Ali
dc.format.extent46 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectComputer science and engineeringen_US
dc.titleParalleizing AwSpPCA for robust facial recognition using CUDAen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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