Paper Info
Title
A Scalable Multiple Pairwise Protein Sequence Alignment Acceleration Using Hybrid Cpu-Gpu Approach
Abstract
Bioinformatics is an interdisciplinary field that applies trending techniques in information technology, mathematics, and statistics in studying large biological data. Bioinformatics involves several computational techniques such as sequence and structural alignment, data mining, macromolecular geometry, prediction of protein structure and gene finding. Protein structure and sequence analysis are vital to the understanding of cellular processes. Understanding cellular processes contributes to the development of drugs for metabolic pathways. Protein sequence alignment is concerned with identifying the similarities and the relationships among different protein structures. In this paper, we target two well-known protein sequence alignment algorithms, the Needleman-Wunsch and the Smith-Waterman algorithms. These two algorithms are computationally expensive which hinders their applicability for large data sets. Thus, we propose a hybrid parallel approach that combines the capabilities of multi-core CPUs and the power of contemporary GPUs, and significantly speeds up the execution of the target algorithms. The validity of our approach is tested on real protein sequences. Moreover, the scalability of the approach is verified on randomly generated sequences with predefined similarity levels. The results showed that the proposed hybrid approach was up to 242 times faster than the sequential approach.
Year
DOI
Venue
2020
10.1007/s10586-019-03035-8
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Bioinformatics, Needleman-Wunsch, Smith-Waterman, Parallel programming, Dynamic parallelism, CUDA
Journal
23
Issue
ISSN
Citations 
4
1386-7857
0
PageRank 
References 
Authors
0.34
22
5
Name
Order
Citations
PageRank
Luay Alawneh1709.18
Mohammed A. Shehab21046.94
Mahmoud Al-Ayyoub373063.41
Yaser Jararweh496888.95
Ziad Al-sharif5104.53