Initial Experiences Porting a Bioinformatics Application to a Graphics Processor

  • Maria Charalambous
  • Pedro Trancoso
  • Alexandros Stamatakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3746)


Bioinformatics applications are one of the most relevant and compute-demanding applications today. While normally these applications are executed on clusters or dedicated parallel systems, in this work we explore the use of an alternative architecture. We focus on exploiting the compute-intensive characteristics offered by the graphics processors (GPU) in order to accelerate a bioinformatics application. The GPU is a good match for these applications as it is an inexpensive, high-performance SIMD architecture.

In our initial experiments we evaluate the use of a regular graphics card to improve the performance of RAxML, a bioinformatics program for phylogenetic tree inference. In this paper we focus on porting to the GPU the most time-consuming loop, which accounts for nearly 50% of the total execution time. The preliminary results show that the loop code achieves a speedup of 3x while the whole application with a single loop optimization, achieves a speedup of 1.2x.


Graphic Processing Unit Graphic Card Total Execution Time Graphic Hardware Graphic Processor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    GPGPU: General-Purpose Computation Using Graphics Hardware,
  2. 2.
    Segal, M., Akeley, K.: The OpenGL Graphics System: A Specification, Version 2.0 (2004)Google Scholar
  3. 3.
    Peeper, C.: DirectX High Level Shading Language. Microsoft Meltdown UK Presentation, Microsoft Corporation (2002)Google Scholar
  4. 4.
    Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., Hanrahan, P.: Brook for GPUs: Stream Computing on Graphics Hardware. ACM Transactions on Graphics 23, 777–786 (2004)CrossRefGoogle Scholar
  5. 5.
    Larsen, E., McAllister, D.: Fast matrix multiplies using graphics hardware. In: Supercomputing 2001: Proceedings of the 2001 ACM/IEEE conference on Supercomputing (CDROM), pp. 55–55. ACM Press, New York (2001)CrossRefGoogle Scholar
  6. 6.
    Kruger, J., Westermann, R.: Linear algebra operators for GPU implementation of numerical algorithms. ACM Transactions on Graphics 22, 908–916 (2003)CrossRefGoogle Scholar
  7. 7.
    Bolz, J., Farmer, I., Grinspun, E., Schrooder, P.: Sparse matrix solvers on the GPU: conjugate gradients and multigrid. ACM Transactions on Graphics 22, 917–924 (2003)CrossRefGoogle Scholar
  8. 8.
    Govindaraju, N., Lloyd, B., Wang, W., Lin, M., Manocha, D.: Fast Computation of Database Operations using Graphics Processors. In: SIGMOD 2004: Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp. 215–226. ACM Press, New York (2004)CrossRefGoogle Scholar
  9. 9.
    Flynn, M.: Very high-speed computing systems. Proceedings of the IEEE 54, 1901–1909 (1966)CrossRefGoogle Scholar
  10. 10.
    Mark, W., Glanville, R., Akeley, K., Kilgard, M.: Cg: a system for programming graphics hardware in a C-like language. ACM Transactions on Graphics 22, 896–907 (2003)CrossRefGoogle Scholar
  11. 11.
    Kessenich, J., Baldwin, D., Rost, R.: The OpenGL Shading Language (2004)Google Scholar
  12. 12.
    Stamatakis, A., Ludwig, T., Meier, H.: RAxML-III: A Fast Program for Maximum Likelihood-based Inference of Large Phylogenetic Trees. Bioinformatics 21, 456–463 (2005)CrossRefGoogle Scholar
  13. 13.
    Stamatakis, A.: An Efficient Program for phylogenetic Inference Using Simulated Annealing. In: Proceedings of IPDPS 2005, Denver, Colorado, USA (2005)Google Scholar
  14. 14.
    Felsenstein, J.: Evolutionary trees from DNA sequences: A maximum likelihood approach. Journal of Molecular Evolution 17, 368–376 (1981)CrossRefGoogle Scholar
  15. 15.
    Bader, D., Moret, B.M., Vawter, L.: Industrial Applications of High-Performance Computing for Phylogeny Reconstruction. In: Proceedings of SPIE ITCom: Commercial Applications for High-Performance Computing, Denver, Colorado, USA, pp. 159–168 (2001)Google Scholar
  16. 16.
    Gascuel, O.: BIONJ: An improved version of the NJ algorithm based on a simple model of sequence data. Molecular Biology and Evolution 14, 685–695 (1997)Google Scholar
  17. 17.
    Guindon, S., Gascuel, O.: A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood. Systematic Biology 52, 696–704 (2003)CrossRefGoogle Scholar
  18. 18.
    Williams, T., Moret, B.M.: An Investigation of Phylogenetic Likelihood Methods. In: Proceedings of 3rd IEEE Symposium on Bioinformatics and Bioengineering (BIBE 2003), Bethesda, Maryland, USA (2003)Google Scholar
  19. 19.
    Stamatakis, A., Ludwig, T., Meier, H.: New Fast and Accurate Heuristics for Inference of Large Phylogenetic Trees. In: Proceedings of IPDPS 2004, Santa Fe, New Mexico, USA (2004)Google Scholar
  20. 20.
    Stamatakis, A., Ott, M., Ludwig, T.: RAxML-OMP: An Efficient Program for Phylogenetic Inference on SMPs. In: Malyshkin, V.E. (ed.) PaCT 2005. LNCS, vol. 3606, pp. 288–302. Springer, Heidelberg (2005) Preprint available on-line at CrossRefGoogle Scholar
  21. 21.
    Intel: IA-32 Intel Architecture: Software Developers Manual. Volume 3 of System Programming Guide. Intel (2003)Google Scholar
  22. 22.
    Trancoso, P., Charalambous, M.: Exploring Graphics Processor Performance for General Purpose Applications. In: Proceedings of the Euromicro Symposium on Digital System Design, Architectures, Methods and Tools, DSD 2005 (2005)Google Scholar
  23. 23.
    NVIDIA: NVIDIA GeForce FX: Performance (2005),
  24. 24.
    TechPowerUp: GPU Database (2005),
  25. 25.
    PriceWatch: Price Comparison Search Engine (2005),
  26. 26.
    Tscheblockov, T.: Power Consumption of Contemporary Graphics Accelerators (2004),

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Maria Charalambous
    • 1
  • Pedro Trancoso
    • 1
  • Alexandros Stamatakis
    • 2
  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus
  2. 2.Institute of Computer ScienceFoundation for Research and Technology-HellasHeraklion, CreteGreece

Personalised recommendations