A Multi GPU Read Alignment Algorithm with Model-Based Performance Optimization

  • Aleksandr Drozd
  • Naoya Maruyama
  • Satoshi Matsuoka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7851)

Abstract

This paper describes a performance model for read alignment problem, one of the most computationally intensive tasks in bioinformatics. We adapted Burrows Wheeler transform based index to be used with GPUs to reduce overall memory footprint. A mathematical model of computation and communication costs was developed to find optimal memory partitioning for index and queries. Last we explored the possibility of using multiple GPUs to reduce data transfers and achieved super-linear speedup. Performance evaluation of experimental implementation supports our claims and shows more than 10fold performance gain per device.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Kruger, J., Lefohm, A.E., Purcell, T.J.: A survay on general-purpose computation on graphics hardware. Computer Graphics Forum 26(1), 80–113 (2007)CrossRefGoogle Scholar
  2. 2.
    Delcher, A.L., Kasif, S., Fleischmann, R.D., Peterson, J., et al.: Alignment of whole genomes. Nucleic Acids Res. 27, 2369 (1999)CrossRefGoogle Scholar
  3. 3.
    Schatz, M.C., Trapnell, C., Delcher, A.L., Varshney, A.: High-throughput sequence alignment using graphics processing units. BMC Bioinformatics 8, 474 (2007)CrossRefGoogle Scholar
  4. 4.
    Gharaibeh, A., Ripeanu, M.: Size matters: Space/time tradeoffs to improve gpgpu applications performance. In: SC 2010 Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society (2010)Google Scholar
  5. 5.
    Ferragina, P., Manzini, G.: Indexing compressed text. Journal of the ACM 53(4), 552–581 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Pop, M.: Genome assembly reborn: recent computational challenges. Briefings in Bioinformatics 10, 354 (2009)CrossRefGoogle Scholar
  7. 7.
    Rothberg, J.M., Hinz, W., Rearick, T.M., et al.: An integrated semiconductor device enabling non-optical genome sequencing. Nature (475), 348–352 (2011)Google Scholar
  8. 8.
    Gusfield, D.: Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge University Press (1997)Google Scholar
  9. 9.
    Burrows, M., Wheeler, D.J.: A block-sorting lossless data compression algorithm. Technical Report 124, Digital Equipment Corporation (1994)Google Scholar
  10. 10.
    Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short dna sequences to the human genome. Genome Biology 10(3), 10(25) (2009)Google Scholar
  11. 11.
    Li, R., Yu, C., Li, Y., et al.: Soap2: an improved ultrafast tool for short read alignment. Bioinformatics 15(25), 1966–1967 (2009)CrossRefGoogle Scholar
  12. 12.
    Chen, S., Jiang, H.: An exact matching approach for high throughput sequencing based on bwt and gpus. In: 2011 IEEE 14th International Conference on Computational Science and Engineering (CSE). IEEE Computer Society (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aleksandr Drozd
    • 1
  • Naoya Maruyama
    • 1
  • Satoshi Matsuoka
    • 1
  1. 1.Tokyo Institute of TechnologyMeguro-kuJapan

Personalised recommendations