Parallelization of AdaBoost algorithm on multi-core processors
Yen-Kuang Chen, Wenlong Li, Xiaofeng Tong · 2008
This paper examines and extracts the parallelism in the AdaBoost person detection algorithm on multi-core processors. As multi-core processors become pervasive, effectively executing many threads simultaneously is crucial in harnessing the computation power. Although the application exposes many levels of parallelism, none of them delivers a satisfactory scaling performance on newest multi-core processors due to load imbalance and parallel overhead. This paper demonstrates how to analyze the thread-level parallelism, and how to choose appropriate one to utilize current 4-core and 8-core processors. With careful optimization and parallelization, the AdaBoost person detection algorithm can efficiently utilize the power of multi-core processors, and now it is 7 times faster than the serial version.