Premium
Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's® Xeon Phi
Author(s) -
Huang Fang,
Yang Hao,
Tao Jian,
Wang Jian,
Tan Xicheng
Publication year - 2021
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6260
Subject(s) - computer science , xeon phi , parallel computing , xeon , algorithm , thread (computing) , image processing , parallel algorithm , multi core processor , automatic parallelization , image (mathematics) , artificial intelligence , compiler , programming language , operating system
In unmanned aerial vehicle (UAV) image‐processing applications, one needs to implement different parallel image‐enhancement algorithms on several high‐performance computing platforms utilizing various programming models. To speed up the parallelization procedure and improve its efficiency, the automatic parallel software package, Par4All, is applied in this work. We find that the performance of the original automatic parallelization algorithm produced with Par4All is inefficient. To resolve this problem, we propose different optimization approaches for Par4All based on Intel®'s Xeon Phi high‐performance computing platform that are based on the structural features of the image‐enhancement algorithms, which can further optimize the original parallel algorithm. These approaches mainly include: (1) Par4All automatic parallel search module optimization, (2) dynamic thread setting optimization, and (3) the collaborative parallelization of both CPU and many integrated core (MIC) processors. According to the results of the comparison experiments involving different algorithms, it is shown that the proposed optimization approaches for these kinds of algorithms can significantly improve the performance of automatic parallel algorithms. The acceleration ratio increases approximately by 30%, 70%, and 80% for the multiscale Retinex, Gaussian‐filtering and median‐filtering algorithms, respectively. As continuation and deepening of our previous research work, this research has the potential to be beneficial for other researchers in image‐processing applications with image‐enhancement algorithms.