Skip to main content
SpringerLink
Log in

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

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Pearson, W.R., Lipman, D.J.: Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. 85(8), 2444–2448 (1988)

    Article  Google Scholar 

  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). https://doi.org/10.1186/1471-2105-9-128

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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). https://doi.org/10.1371/journal.pcbi.1000386

    Article  Google Scholar 

  7. Li, R., Li, Y., Kristiansen, K., Wang, J.: SOAP: short oligonucleotide alignment program. Bioinformatics. 24(5), 713–714 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

  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. 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). https://doi.org/10.1155/2015/185179

    Article  Google Scholar 

  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)

  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. Khoirudin, Shun-Liang, J.: GPU application in CUDA memory. Int. J. Adv. Comput. 6(2), 1–10 (2015). https://doi.org/10.5121/acij.2015.6201

    Article  Google Scholar 

  16. Ghorpade, Jayashree, Parande, Jitendra, Kulkarni, Madhura, Bawaskar, Amit: GPGPU processing in CUDA architecture. Int. J. Adv. Comput. 3(1), 105–120 (2012). https://doi.org/10.5121/acij.2012.3109

    Article  Google Scholar 

  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:1530-2075

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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: 2278-3075

  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 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SSN College of Engineering, Chennai, Tamilnadu, India

    D. Venkata Vara Prasad & Suresh Jaganathan

Authors
  1. D. Venkata Vara Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suresh Jaganathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Venkata Vara Prasad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prasad, D.V.V., Jaganathan, S. Improving the performance of Smith–Waterman sequence algorithm on GPU using shared memory for biological protein sequences. Cluster Comput 22 (Suppl 4), 9495–9504 (2019). https://doi.org/10.1007/s10586-018-2421-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2421-7

Keywords

Search

Navigation