ForkJoinPcc Algorithm for Computing the Pcc Matrix in Gene Co-Expression Networks

Electronics

Ola S. Ayoub¹  Vidan F. Ghoneim²  Nahed H. Solouma³  Amel Ali Alhussan⁴  Hussah Nasser AlEisa⁴  Rania Ahmed Abdel Azeem Abul Seoud⁵  Ghada Atteia⁶  Nagwan Abdel Samee⁶ 

[1] Biomedical Engineering Department, Cairo University, Giza 12511, Egypt;Biomedical Engineering Department, Helwan University, Helwan 11731, Egypt;Biomedical Engineering Department, King Faisal University, P.O. Box 9149, Alahsa 13980, Saudi Arabia;Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;Department of Electronics and Communication Engineering, Faculty of Engineering, Fayoum University, Fayoum 63511, Egypt;Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;

关键词: Pearson’s correlation;    high performance computing;    multicores;    cluster;    fork–join;    MPI;   

DOI  :  10.3390/electronics11081174

来源: DOAJ

【 摘 要 】

High-throughput microarrays contain a huge number of genes. Determining the relationships between all these genes is a time-consuming computation. In this paper, the authors provide a parallel algorithm for finding the Pearson’s correlation coefficient between genes measured in the Affymetrix microarrays. The main idea in the proposed algorithm, ForkJoinPcc, mimics the well-known parallel programming model: the fork–join model. The parallel MATLAB APIs have been employed and evaluated on shared or distributed multiprocessing systems. Two performance metrics—the processing and communication times—have been used to assess the performance of the ForkJoinPcc. The experimental results reveal that the ForkJoinPcc algorithm achieves a substantial speedup on the cluster platform of 62× compared with a 3.8× speedup on the multicore platform.

【 授权许可】

Unknown