Cluster Computing

, Volume 22, Supplement 4, pp 9495–9504 | Cite as

Improving the performance of Smith–Waterman sequence algorithm on GPU using shared memory for biological protein sequences

  • D. Venkata Vara PrasadEmail author
  • Suresh Jaganathan


In Bioinformatics, sequence alignment algorithm aims to find out whether biological sequences (e.g., DNA, RNA, or Protein sequences) are related or not. A variety of algorithms are developed, Smith–Waterman Algorithm (SW) is a well-known local alignment algorithm to find the similarity of two sequences and provides optimal result using dynamic programming. As the size of sequence database is doubling about every 6 months, the computational time also increases. Sequence alignment algorithms performance can have improved by using the parallel computing technology on the GPU. In this paper, we proposed a method to improve the performance of SW algorithm by using GPU’s shared memory instead of global memory. By using shared memory, the data being transferred between the global memory and processing elements is reduced, which in turn improves the performance. The tabulated result showed positive sign of correctness in proposed method and tested using UniProt sequence database.


Bioinformatics Sequence alignment CUDA-GPU Memory Smith–Waterman algorithm 


  1. 1.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)CrossRefGoogle Scholar
  2. 2.
    Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997)CrossRefGoogle Scholar
  3. 3.
    Pearson, W.R., Lipman, D.J.: Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. 85(8), 2444–2448 (1988)CrossRefGoogle Scholar
  4. 4.
    Smith, A.D., Xuan, Z., Zhang, M.Q.: Using quality scores and longer reads improves accuracy of Solexa read mapping. BMC Bioinform. 9(128), 1–8 (2008). CrossRefGoogle Scholar
  5. 5.
    Li, H., Ruan, J., Durbin, R.: Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18(11), 1851–1858 (2008)CrossRefGoogle Scholar
  6. 6.
    Rumble, S.M., Lacroute, P., Dalca, A.V., Fiume, M., Sidow, A., Brudno, M.: SHRiMP: accurate mapping of short color-space reads. PLoS Comput. Biol. (2009). CrossRefGoogle Scholar
  7. 7.
    Li, R., Li, Y., Kristiansen, K., Wang, J.: SOAP: short oligonucleotide alignment program. Bioinformatics. 24(5), 713–714 (2008)CrossRefGoogle Scholar
  8. 8.
    Campagna, D., Albiero, A., Bilardi, A., Caniato, E., Forcato, C., Manavski, S., Vitulo, N., Valle, G.: PASS: a program to align short sequences. Bioinformatics. 25(7), 967–968 (2009)CrossRefGoogle Scholar
  9. 9.
    Zhou, Z.-M., Chen, Z.-W.: Dynamic programming for protein sequence alignment. Int. J. Bio-Sci. Bio-Technol. 5(2), 141–150 (2013)Google Scholar
  10. 10.
    Bustamam, A., Ardaneswari, G., Lestari, D.: Implementation of Cuda GPU-based parallel computing on Smith–Waterman algorithm to sequence database searches. In: IEEE International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 137–142 (2013)Google Scholar
  11. 11.
    Shukla, H., Shah, M.: Optimizing parallel scan Smith–Waterman algorithm on GPU. Int. J. Adv. Comput. Eng. Netw. 2, 86–89 (2014)Google Scholar
  12. 12.
    Huang, L.-T., Wu, C.-C., Lai, L.-F., Li, Y.-J.: Improving the mapping of Smith–Waterman sequence database searches onto CUDA-enabled GPUs. BioMed Res. Int. (2015). CrossRefGoogle Scholar
  13. 13.
    Jain, C., Kumar, S.: Fine-grained GPU parallelization of pairwise local sequence alignment. In: IEEE International Conference on High Performance Computing (HiPC), pp. 1–10 (2014)Google Scholar
  14. 14.
    Liu, Y., Wirawan, A., Schmidt, B.: CUDASW ++ 3.0: accelerating Smith–Waterman protein database search by coupling CPU and GPU SIMD instructions. BMC Bioinform. 14(117), 1–10 (2013)Google Scholar
  15. 15.
    Khoirudin, Shun-Liang, J.: GPU application in CUDA memory. Int. J. Adv. Comput. 6(2), 1–10 (2015). CrossRefGoogle Scholar
  16. 16.
    Ghorpade, Jayashree, Parande, Jitendra, Kulkarni, Madhura, Bawaskar, Amit: GPGPU processing in CUDA architecture. Int. J. Adv. Comput. 3(1), 105–120 (2012). CrossRefGoogle Scholar
  17. 17.
    Ligowski, L., Rudnicki, W.: An efficient implementation of Smith–Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases. In: IEEE International Symposium on Parallel and Distributed Processing (IPDPS), pp. 1–8 (2009), ISSN:1530-2075Google Scholar
  18. 18.
    Pandey, J., Khare, N., Pandey, R.: A survey of parallel models for sequence alignment using Smith-Waterman algorithm. IOSR J. Comput. Eng. 17, 48–52 (2015)Google Scholar
  19. 19.
    Chaibou, A., Sie, O.: Comparative study of the parallelization of the Smith-Waterman algorithm on OpenMP and Cuda C. J. Comput. Commun. 3, 107–117 (2015)CrossRefGoogle Scholar
  20. 20.
    Biradar, S., Desai, V., Madagouda, B., Patil, M.: Comparative analysis of dynamic programming algorithms to find similarity in gene sequences. Int. J. Res. Eng. Technol. 2(8), 312–316 (2013)CrossRefGoogle Scholar
  21. 21.
    El-Saghir, Z., Kelash, H., Elnazly, S., Faheem, H.: Parallel implementation of Smith–Waterman algorithm using MPI, OpenMP and hybrid model. Int. J. Innov. Technol. Explor. Eng. vol. 4, pp. 1–5 (2014). ISSN: 2278-3075Google Scholar
  22. 22.
    Huang, L.-T., Wu, C.-C., Lai, L.-F., Li, Y.-J.: Improving the mapping of Smith–Waterman sequence database searches onto CUDA-enabled GPUs. BioMed Res. Int. 2015, 1–10 (2015)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSSN College of EngineeringChennaiIndia

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