Advertisement

FPGA-Based Smith-Waterman Algorithm: Analysis and Novel Design

  • Yoshiki Yamaguchi
  • Hung Kuen Tsoi
  • Wayne Luk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6578)

Abstract

This paper analyses two methods of organizing parallelism for the Smith-Waterman algorithm, and show how they perform relative to peak performance when the amount of parallelism varies. A novel systolic design is introduced, with a processing element optimized for computing the affine gap cost function. Our FPGA design is significantly more energy-efficient than GPU designs. For example, our design for the XC5VLX330T FPGA achieves around 16 GCUPS/W, while CPUs and GPUs have a power efficiency of lower than 0.5 GCUPS/W.

Keywords

Performance comparison dynamic programming 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic Local Alignment Search Tool. Molecular Biology 215(3), 403–410 (1990)CrossRefGoogle Scholar
  2. 2.
    Pearson, W.R.: Comparison of methods for searching protein sequence databases. Profein Science 4(6), 1145–1160 (1995)CrossRefGoogle Scholar
  3. 3.
    Shpaer, E.G., Robinson, M., Yee, D., Candlin, J.D., Mines, R., Hunkapiller, T.: Sensitivity and selectivity in protein similarity searches: A comparison of Smith-Waterman in hardware to BLAST and FASTA. Genomics 38, 179–191 (1996)CrossRefGoogle Scholar
  4. 4.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48(3), 443–453 (1970)CrossRefGoogle Scholar
  5. 5.
    Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. Journal of Molecular Biology 147(1), 195–197 (1981)CrossRefGoogle Scholar
  6. 6.
    Van Court, T., Herbordt, M.C.: Families of FPGA-based accelerators for approximate string matching. Microprocessors & Microsystems 31, 135–145 (2007)CrossRefGoogle Scholar
  7. 7.
    ALTERA. Implementation of the smith-waterman algorithm on a reconfigurable supercomputing platform (September 2007)Google Scholar
  8. 8.
    Benkrid, K., Liu, Y., Benkrid, A.: A highly parameterised and efficient FPGA-based skeleton for pairwise biological sequence alignment. IEEE Transactions on Very Large Scale Integration (VLSI Systems) 17(4), 561–570 (2009)CrossRefGoogle Scholar
  9. 9.
    Ligowski, Ł., Rudnicki, W.R.: An efficient implementation of smith waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases. In: Proceedings of the IEEE International Symposium on Parallel and Distributed Processing (appeared in HICOMB), pp. 1–8 (May 2009)Google Scholar
  10. 10.
    Liu, Y., Maskell, D.L., Schmidt, B.: CUDASW++: optimizing smith-waterman sequence database searches for CUDA-enabled graphics processing units. BMC Research Notes 2(1), 73–82 (2009)CrossRefGoogle Scholar
  11. 11.
    Ligowski, Ł., Rudnicki, W.R.: GPU-SW sequence alignment server. In: Proceedings of International Conference on Computational Science, pp. 1–10 (June 2010)Google Scholar
  12. 12.
    Dohi, K., Benkrid, K., Ling, C., Hamada, T., Shibata, Y.: Highly efficient mapping of the smith-waterman algorithm on CUDA-compatible GPUs. In: Proceedings of the IEEE International Conference on Application-specific Systems Architectures and Processors, pp. 29–36 (July 2010)Google Scholar
  13. 13.
    Aldinucci, M., Danelutto, M., Meneghin, M., Kilpatrick, P., Torquati, M.: Efficient streaming applications on multi-core with fastflow: the biosequence alignment test-bed. In: Proceedings of International Conference on Parallel Computing, pp. 273–280 (September 2009)Google Scholar
  14. 14.
    Dayhoff, M.O., Schwartz, R.M., Orcutt, B.C.: A model of evolutionary change in proteins, vol. 5. National Biomedical Research Foundation (1978)Google Scholar
  15. 15.
    Altschul, S.F.: Amino acid substitution matrices from an information theoretic perspective. Journal of Molecular Biology 219(3), 555–565 (1991)CrossRefGoogle Scholar
  16. 16.
    Gotoh, O.: An improved algorithm for matching biological sequences. Journal of Molecular Biology 162(3), 705–708 (1982)CrossRefGoogle Scholar
  17. 17.
    Jacob, A.C., Buhler, J.D., Chamberlain, R.D.: Design of throughput-optimized arrays from recurrence abstractions. In: Proceedings of the IEEE International Conference on Application-specific Systems Architectures and Processors, pp. 133–140 (July 2010)Google Scholar
  18. 18.
    Manavski, S.A., Valle, G.: CUDA compatible GPU cards as efficient hardware accelerators for smith-waterman sequence alignment. BMC Bioinformatics 9(suppl. 2), S10 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yoshiki Yamaguchi
    • 1
  • Hung Kuen Tsoi
    • 2
  • Wayne Luk
    • 2
  1. 1.Graduate School of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  2. 2.Department of ComputingImperial College LondonLondonUnited Kingdom

Personalised recommendations