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A scalable multiple pairwise protein sequence alignment acceleration using hybrid CPU–GPU approach

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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.

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Acknowledgements

The authors would like to thank the Deanship of Research at Jordan University of Science and Technology for funding this work (Grant Number 20170396).

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Authors and Affiliations

  1. Jordan University of Science and Technology, Irbid, Jordan

    Luay Alawneh, Mahmoud Al-Ayyoub, Yaser Jararweh & Ziad A. Al-Sharif

  2. Concordia University, Montreal, QC, Canada

    Mohammed A. Shehab

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  1. Luay Alawneh
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  2. Mohammed A. Shehab
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  3. Mahmoud Al-Ayyoub
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  4. Yaser Jararweh
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  5. Ziad A. Al-Sharif
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Correspondence to Luay Alawneh.

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Alawneh, L., Shehab, M.A., Al-Ayyoub, M. et al. A scalable multiple pairwise protein sequence alignment acceleration using hybrid CPU–GPU approach. Cluster Comput 23, 2677–2688 (2020). https://doi.org/10.1007/s10586-019-03035-8

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