Hybrid MPI/OpenMP Implementation of PCA

Dalia Shouman Ibrahim, Salma Hamdy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Most surveillance systems depend on fully automated face recognition applications. The main concern is achieving high accuracy in real time. Principle Component Analysis algorithm is used for reducing the number of variables and getting the maximum variance between low dimensional data. The proposed approaches focus on data partitioning to minimize the execution time of the algorithm by distributing data over a cluster with parallel computing architecture. The first approach achieves 2975X and 102X relatively faster than the sequential implementation in the training and recognition phases, respectively. However, the second approach achieves 74X relatively faster than the sequential implementation in the recognition phase.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE Eighth International Conference on Intelligent Computing and Information Systems (ICICIS 2017)
Place of PublicationCairo, Egypt
PublisherIEEE
Pages205-211
Number of pages7
ISBN (Electronic)9781538608210
ISBN (Print)9781538608227
DOIs
Publication statusPublished - 5 Dec 2017
Externally publishedYes
Event8th International Conference on Intelligent Computing and Information Systems - Ain Shams University, Cairo, Egypt
Duration: 5 Dec 20177 Dec 2017
Conference number: 8
http://net2.asu.edu.eg/icicis/2017/

Publication series

Name
ISSN (Print)1687-1103

Conference

Conference8th International Conference on Intelligent Computing and Information Systems
Abbreviated titleICICIS 2017
CountryEgypt
CityCairo
Period5/12/177/12/17
Internet address

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Cite this

Ibrahim, D. S., & Hamdy, S. (2017). Hybrid MPI/OpenMP Implementation of PCA. In Proceedings of the 2017 IEEE Eighth International Conference on Intelligent Computing and Information Systems (ICICIS 2017) (pp. 205-211). IEEE . https://doi.org/10.1109/INTELCIS.2017.8260048