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IJMLC 2021 Vol.11(3): 256-261 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.3.1044

High-Performance FPGA-Based BWA-MEM Accelerator

Binh Kieu-Do-Nguyen, Cuong Pham-Quoc, and Cong-Kha Pham

Abstract—There is no denying that Bioinformatics is one of the most important realms for our forthcoming development. As a demonstration of this fact, a plethora of new algorithms that were published over the last decade. Those significantly boost up the processes of biological analysis, especially for DNA alignment. Despite their undeniable contributions, it is still far more to state that DNA alignment has already achieved the ideal performance. In this work, we focus on the DNA alignment system which is based on our improved BWA-MEM algorithm that we have already published. Besides that, we also propose some optimization methods which was applied in order to improve the performance as well as the stability of our entire system. The system offers a speed-up by 46.52x when compared with the other computing platforms.

Index Terms—DNA alignment, BWA-MEM algorithm, FPGA, IP Core Seed extension.

Binh Kieu-Do-Nguyen and Cuong Pham-Quoc are with the Ho Chi Minh City University of Technology – VNU-HCM, Ho Chi Minh City, Viet Nam (corresponding author: Cuong Pham-Quoc; e-mail: 1770283@hcmut.edu.vn, cuongpham@hcmut.edu.vn).
Cong-Kha Pham is with the Department of Computer and Network Engineering, Cluster II (Emerging Multi-interdisciplinary Engineering), The University of Electro-Communications, the city of Chofu, Tokyo, Japan (e-mail: phamck@uec.ac.jp).

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Cite: Binh Kieu-Do-Nguyen, Cuong Pham-Quoc, and Cong-Kha Pham, "High-Performance FPGA-Based BWA-MEM Accelerator," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 256-261, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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