Cluster Computing

, Volume 12, Issue 3, pp 341–352 | Cite as

Scalable and highly parallel implementation of Smith-Waterman on graphics processing unit using CUDA

  • Ali Akoglu
  • Gregory M. Striemer


Program development environments have enabled graphics processing units (GPUs) to become an attractive high performance computing platform for the scientific community. A commonly posed problem in computational biology is protein database searching for functional similarities. The most accurate algorithm for sequence alignments is Smith-Waterman (SW). However, due to its computational complexity and rapidly increasing database sizes, the process becomes more and more time consuming making cluster based systems more desirable. Therefore, scalable and highly parallel methods are necessary to make SW a viable solution for life science researchers. In this paper we evaluate how SW fits onto the target GPU architecture by exploring ways to map the program architecture on the processor architecture. We develop new techniques to reduce the memory footprint of the application while exploiting the memory hierarchy of the GPU. With this implementation, GSW, we overcome the on chip memory size constraint, achieving 23× speedup compared to a serial implementation. Results show that as the query length increases our speedup almost stays stable indicating the solid scalability of our approach. Additionally this is a first of a kind implementation which purely runs on the GPU instead of a CPU-GPU integrated environment, making our design suitable for porting onto a cluster of GPUs.


Graphics processing unit Scalable Parallel Alignment Smith-Waterman CUDA 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Eddy, S.R.: Where did the BLOSUM62 alignment score matrix come from? Nat. Biotechnol. 22, 1035–1036 (2004) CrossRefGoogle Scholar
  2. 2.
    Farrar, M.: Striped Smith-Waterman speeds database searches six times over other SIMD implementations. Bioinformatics 23, 156–161 (2007) CrossRefGoogle Scholar
  3. 3.
    Walker, J.M.: The Proteomics Protocols Handbook. Humana Press, Clifton (2005), pp. 504 CrossRefGoogle Scholar
  4. 4.
    Liao, Y.H., Yin, L.M., Cheng, Y.: A parallel implementation of the Smith-Waterman algorithm for sequence searching. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS. San Francisco, California, September 1–5, 2004 Google Scholar
  5. 5.
    Liu, W., Schmidt, B., Voss, G., Schröder, A., Müller-Wittig, W.: Bio-sequence database scanning on a GPU. In: Proceedings of the 20th IEEE International Parallel & Distributed Processing Symposium, HICOMB Workshop (2006) Google Scholar
  6. 6.
    Manavski, S.S.: Smith-Waterman User Guide. (2008)
  7. 7.
    Manavski, S.S., Valle, G.: Cuda compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment. BMC Bioinformatics 9(Suppl 2), S10 (2008) CrossRefGoogle Scholar
  8. 8.
    Mount, D.W.: Bioinformatics: Sequence and Genome Analysis. Cold Spring Harbor Laboratory Press, Cold Spring Harbor (2004), pp. 64–65, 71–87 Google Scholar
  9. 9.
    NVIDIA Corporation: NVIDIA CUDA compute unified device architecture programming guide. (2008)
  10. 10.
    NVIDIA Corporation: NVIDIA Tesla C870 overview. (May 2008)
  11. 11.
    Sanchez, F., Salamí, E., Ramierez, A., Valero, M.: Performance analysis of sequence alignment applications. In: Proceedings of the IEEE International Symposium on Workload Characterization, pp. 51–60 (2006) Google Scholar
  12. 12.
    Smith, T., Waterman, M.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981) CrossRefGoogle Scholar
  13. 13.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of ArizonaTucsonUSA

Personalised recommendations