Open AccessPerformance Tuning Techniques for Face Detection Algorithms on GPGPU
Author(s) -
Yara M. Abdelaal,
Mahmoud Fayez,
Samy Ghoniemy,
Ehab Abozinadah,
H. M. Faheem
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b8234.1210220
Subject(s) - computer science , general purpose computing on graphics processing units , algorithm , parallel computing , face detection , face (sociological concept) , cuda , speedup , computer engineering , facial recognition system , artificial intelligence , pattern recognition (psychology) , computer graphics (images) , graphics , social science , sociology
Face detection algorithms varies in speed and performance on GPUs. Different algorithms can report different speeds on different GPUs that are not governed by linear or near-linear approximations. This is due to many factors such as register file size, occupancy rate of the GPU, speed of the memory, and speed of double precision processors. This paper studies the most common face detection algorithms LBP and Haar-like and study the bottlenecks associated with deploying both algorithms on different GPU architectures. The study focuses on the bottlenecks and the associated techniques to resolve them based on the different GPUs specifications.