Skip to main content
SpringerLink
Log in

Large-Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units

  • Chapter
  • First Online:
Massively Parallel Evolutionary Computation on GPGPUs

Part of the book series: Natural Computing Series ((NCS))

Abstract

The NCBI GEO GSE3494 breast cancer dataset contains hundreds of Affymetrix HG-U133A and HG-U133B GeneChip biopsies each with a million variables. Multiple genetic programming (GP) runs on a graphics processing unit (GPU) hardware, each with a population of five million programs both winnows (selects) useful variables from the chaff and evolves small (three inputs) data models. The SPMD CUDA interpreter exploits the GPU’s single instruction multiple data (SIMD) mode of parallel computing, even though the GP populations contain different programs. A 448 node nVidia Fermi C2050 Tesla graphics card delivers 8.5 giga GPops per second. In addition to describing our implementation, we survey current GPGPU work in bioinformatics and genetic programming.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    pos is 1 for positive training cases and 0 for negative cases.

References

  1. Arabnia, H.R., Oliver, M.A.: A transputer network for the arbitrary rotation of digitised images. Comput. J. 30(5), 425–432 (1987)

    Article  Google Scholar 

  2. Bakhoda, A., et al.: Analyzing CUDA workloads using a detailed GPU simulator. In: International Symposium on Performance Analysis of Systems and Software, Boston, MA, USA, 2009, pp. 163–174. IEEE (2009)

    Google Scholar 

  3. Banzhaf, W., et al.: Genetic Programming. Morgan Kaufmann, Los Altos (1998)

    Book  MATH  Google Scholar 

  4. Banzhaf, W., et al.: Accelerating genetic programming through graphics processing units. In: Riolo, R.L., et al. (eds.) Genetic Programming Theory and Practice VI, Chap. 15, pp. 229–249. Springer, Ann Arbor (2008)

    Google Scholar 

  5. Barrett, T., et al.: NCBI GEO: mining tens of millions of expression profiles—database and tools update. Nucleic Acids Res. 35(Database issue), D760–D765 (2007)

    Google Scholar 

  6. Camargo Bareno, C.I., et al.: Intrinsic evolvable hardware for combinatorial synthesis based on soC + FPGA and GPU platforms. In: Krasnogor, N., et al. (eds.) GECCO Companion, Dublin, 2011, pp. 189–190. ACM, New York (2011)

    Google Scholar 

  7. Cano, A., et al.: Solving classification problems using genetic programming algorithms on GPUs. In: Corchado, E., et al. (eds.) Hybrid Artificial Intelligence Systems, San Sebastian, Spain, 2010. Lecture Notes in Computer Science, vol. 6077, pp. 17–26. Springer, Berlin (2010)

    Google Scholar 

  8. Cano, A., et al.: Speeding up the evaluation phase of GP classification algorithms on GPUs. Soft Comput. Fusion Found. Methodol. Appl. 187–202 (2011)

    Google Scholar 

  9. Charalambous, M., Trancoso, P., Stamatakis, A.: Initial experiences porting a bioinformatics application to a graphics processor. In: Bozanis, P., Houstis, E.N. (eds.) Advances in Informatics, 10th Panhellenic Conference on Informatics, PCI 2005, Volos, Greece, 2005. Lecture Notes in Computer Science, vol. 3746, pp. 415–425. Springer, Berlin (2005)

    Google Scholar 

  10. Chitty, D.M.: A data parallel approach to genetic programming using programmable graphics hardware. In: Thierens, D., et al. (eds.) Genetic and Evolutionary Computation Conference, London, 2007, vol. 2, pp. 1566–1573. ACM, New York (2007)

    Google Scholar 

  11. Christen, M., Schenk, O., Burkhart, H.: Automatic code generation and tuning for stencil kernels on modern shared memory architectures. Comput. Sci. Res. Dev. 26(3), 205–210 (2011)

    Article  Google Scholar 

  12. Corney, D.P.A.: Intelligent analysis of small datasets for food design. Ph.D. thesis, University College, London (2002)

    Google Scholar 

  13. Cupertino, L.F., et al.: Evolving CUDA PTX programs by quantum inspired linear genetic programming. In: Harding, S., et al. (eds.) GECCO 2011 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU), Dublin, 2011, pp. 399–406. ACM, New York (2011)

    Google Scholar 

  14. Dowsey, A.W., Dunn, M.J., Yang, G.-Z.: Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline. Bioinformatics 24(7), 950–957 (2008)

    Article  Google Scholar 

  15. Ebner, M.: Engineering of computer vision algorithms using evolutionary algorithms. In: Blanc-Talon, J., et al. (eds.) Advanced Concepts in Intelligent Vision Systems, Bordeaux, France, 2009. Lecture Notes in Computer Science, vol. 5807, pp. 367–378. Springer, Berlin (2009)

    Google Scholar 

  16. Ebner, M.: Towards automated learning of object detectors. In: Di Chio, C., et al. (eds.) Evolutionary Computation in Image Analysis and Signal Processing, Istanbul, 2010. Lecture Notes in Computer Science, vol. 6024, pp. 231–240. Springer, Berlin (2010)

    Google Scholar 

  17. Ebner, M.: Evolving object detectors with a GPU accelerated vision system. In: Tempesti, G., et al. (eds.) International Conference on Evolvable Systems, York, 2010. Lecture Notes in Computer Science, vol. 6274, pp. 109–120. Springer, Berlin (2010)

    Google Scholar 

  18. Ebner, M., et al.: Evolution of vertex and pixel shaders. In: Keijzer, M., et al. (eds.) European Conference on Genetic Programming, Lausanne, Switzerland, 2005. Lecture Notes in Computer Science, vol. 3447, pp. 261–270. Springer, Berlin (2005)

    Google Scholar 

  19. Faler, W.: Automatic algorithm invention with GPU. In: 28th Chaos Communication Congress, Berlin, 2011, p. ID 4764 (2011)

    Google Scholar 

  20. Fan, Z., et al.: GPU cluster for high performance computing. In: Proceedings of the ACM/IEEE SC2004 Conference Supercomputing (2004)

    Google Scholar 

  21. Feller, W.: An Introduction to Probability Theory and Its Applications, vol. 1, 2nd edn. Wiley, New York (1957)

    Google Scholar 

  22. Fok, K.-L.: et al.: Evolutionary computing on consumer graphics hardware. IEEE Intell. Syst. 22(2), 69–78 (2007)

    Google Scholar 

  23. Francone, F.D.: Discipulus Owner’s Manual. Littleton, USA, version 3.0 draft edition (2001)

    Google Scholar 

  24. Garland, M., Kirk, D.B.: Understanding throughput-oriented architectures. Commun. ACM 53(11), 58–66 (2010)

    Article  Google Scholar 

  25. Gobron, S., Devillard, F., Heit, B.: Retina simulation using cellular automata and GPU programming. Mach. Vis. Appl. 18(6), 331–342 (2007)

    Article  Google Scholar 

  26. Grewe, D., Lokhmotov, A.: Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation. In: General Purpose Processing on Graphics Processing Units, Newport Beach, CA, USA, 2011. ACM, New York (2011)

    Google Scholar 

  27. Harding, S.: Evolution of image filters on graphics processor units using Cartesian genetic programming. In: Wang, J. (ed.) World Congress on Computational Intelligence, Hong Kong, 2008, pp. 1921–1928. IEEE Press, New York (2008)

    Chapter  Google Scholar 

  28. Harding, S.L., Banzhaf, W.: Fast genetic programming and artificial developmental systems on GPUs. In: High Performance Computing Systems and Applications, Canada, 2007, p. 2. IEEE Computer Society, Silver Spring (2007)

    Google Scholar 

  29. Harding, S., Banzhaf, W.: Fast genetic programming on GPUs. In: Ebner, M., et al. (eds.) European Conference on Genetic Programming, Valencia, Spain, 2007. Lecture Notes in Computer Science, vol. 4445, pp. 90–101. Springer, Berlin (2007)

    Google Scholar 

  30. Harding, S.L., Banzhaf, W.: Distributed genetic programming on GPUs using CUDA. In: Hidalgo, I., et al. (eds.) Workshop on Parallel Architectures and Bioinspired Algorithms, Raleigh, NC, USA, 2009, pp. 1–10. Universidad Complutense de Madrid, Madrid (2009)

    Google Scholar 

  31. Harding, S., Banzhaf, W.: Implementing Cartesian genetic programming classifiers on graphics processing units using GPU.NET. In: Harding, S., et al. (eds.) GECCO 2011 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU), Dublin, 2011, pp. 463–470. ACM, New York (2011)

    Google Scholar 

  32. Harding, S.L., Banzhaf, W.: Hardware acceleration for CGP: graphics processing units. In: Miller, J.F. (ed.) Cartesian Genetic Programming, Chap. 8, pp. 231–253. Springer, Berlin (2011)

    Chapter  Google Scholar 

  33. Harding, S.L., et al.: Self-modifying Cartesian genetic programming. In: Thierens, D., et al. (eds.) Genetic and Evolutionary Computation Conference, London, 2007, vol. 1, pp. 1021–1028. ACM, New York (2007)

    Google Scholar 

  34. Harvey, N., Luke, R., Keller, J.M., Anderson, D.: Speed up of fuzzy logic through stream processing on graphics processing units. In: Wang, J. (ed.) World Congress on Computational Intelligence, Hong Kong, 2008, pp. 3809–3815. IEEE Press, New York (2008)

    Chapter  Google Scholar 

  35. Howlett, A., et al.: Evolving pixel shaders for the prototype video game subversion. In: The Thirty Sixth Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB’10), De Montfort University, Leicester, UK, 2010. AI & Games Symposium (2010)

    Google Scholar 

  36. Hu, T., et al.: Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm. Genet. Program. Evolvable Mach. 11(2), 205–225 (2010)

    Article  Google Scholar 

  37. Izydorczyk, J., Izydorczyk, M.: Microprocessor scaling: what limits will hold? IEEE Comput. 43(8), 20–26 (2010)

    Article  Google Scholar 

  38. Juille, H., Pollack, J.B.: Parallel genetic programming and fine-grained SIMD architecture. In: Siegel, E.V., Koza, J.R. (eds.) Working Notes for the AAAI Symposium on Genetic Programming, MIT, 1995, pp. 31–37. AAAI, Menlo Park (1995)

    Google Scholar 

  39. Kannan, S., Ganji, R.: Porting Autodock to CUDA. In: Sobrevilla, P. (ed.) World Congress on Computational Intelligence, Barcelona, 2010, pp. 3815–3822. IEEE, New York (2010)

    Google Scholar 

  40. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  41. Langdon, W.B.: Genetic Programming and Data Structures. Kluwer, Boston (1998)

    Book  MATH  Google Scholar 

  42. Langdon, W.B.: A SIMD interpreter for genetic programming on GPU graphics cards. Technical Report CSM-470, Department of Computer Science, University of Essex, Colchester, 3 July 2007

    Google Scholar 

  43. Langdon, W.B.: Evolving GeneChip correlation predictors on parallel graphics hardware. In: Wang, J. (ed.) World Congress on Computational Intelligence, Hong Kong, 2008, pp. 4152–4157. IEEE Press, New York (2008)

    Google Scholar 

  44. Langdon, W.B.: A fast high quality pseudo random number generator for nVidia CUDA. In: Wilson, G. (ed.) CIGPU Workshop at GECCO, Montreal, 2009, pp. 2511–2513. ACM, New York (2009)

    Google Scholar 

  45. Langdon, W.B.: Large scale bioinformatics data mining with parallel genetic programming on graphics processing units. In: Fernandez de Vega, F., Cantu-Paz, E. (eds.) Parallel and Distributed Computational Intelligence, Chap. 5, pp. 113–141. Springer, Berlin (2010)

    Chapter  Google Scholar 

  46. Langdon, W.B.: A many threaded CUDA interpreter for genetic programming. In: Esparcia-Alcazar, A.I., et al. (eds.) European Conference on Genetic Programming, Istanbul, 2010. Lecture Notes in Computer Science, vol. 6021, pp. 146–158. Springer, Berlin (2010)

    Google Scholar 

  47. Langdon, W.B.: Graphics processing units and genetic programming: an overview. Soft Comput. 15, 1657–1669 (2011)

    Article  Google Scholar 

  48. Langdon, W.B.: Debugging CUDA. In: Harding, S., Langdon, W.B., Wong, M.L., Wilson, G., Lewis, T. (eds.) GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU), Dublin, 2011, pp. 415–422. ACM, New York (2011)

    Google Scholar 

  49. Langdon, W.B.: Generalisation in genetic programming. In: Krasnogor, N., et al. (eds.) Genetic and Evolutionary Computation Conference, Dublin, 2011, p. 205. ACM, New York (2011)

    Google Scholar 

  50. Langdon, W.B.: Creating and debugging performance CUDA C. In: Fernandez de Vega, F., et al. (eds.) Parallel Architectures and Bioinspired Algorithms, Chap. 1, pp. 7–50. Springer, Berlin (2012)

    Chapter  Google Scholar 

  51. Langdon, W.B.: Initial experiences of the Emerald: e-infrastructure south GPU supercomputer. Research Note RN/12/08, Department of Computer Science, University College London, 2012

    Google Scholar 

  52. Langdon, W.B.: Distilling GeneChips with genetic programming on the Emerald GPU supercomputer. SIGEvolution 6(1), 15–21 (2012)

    MathSciNet  Google Scholar 

  53. Langdon, W.B., Banzhaf, W.: A SIMD interpreter for genetic programming on GPU graphics cards. In: O’Neill, M., et al. (eds.) European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 4971, Naples, 2008, pp. 73–85. Springer, Berlin (2008)

    Google Scholar 

  54. Langdon, W.B., Barrett, S.J.: Genetic programming in data mining for drug discovery. In: Ghosh, A., Jain, L.C. (eds.) Evolutionary Computing in Data Mining, Chap. 10, pp. 211–235. Springer, Berlin (2004)

    Google Scholar 

  55. Langdon, W.B., Buxton, B.F.: Genetic programming for mining DNA chip data from cancer patients. Genet. Program. Evolvable Mach. 5(3), 251–257 (2004)

    Article  Google Scholar 

  56. Langdon, W.B., Harman, M.: Evolving a CUDA kernel from an nVidia template. In: Sobrevilla, P. (ed.) World Congress on Computational Intelligence, Barcelona, 2010, pp. 2376–2383. IEEE, New York (2010)

    Google Scholar 

  57. Langdon, W.B., Harrison, A.P.: GP on SPMD parallel graphics hardware for mega bioinformatics data mining. Soft Comput. 12(12), 1169–1183 (2008)

    Article  Google Scholar 

  58. Langdon, W.B., Harrison, A.P., Sanchez Graillet, O.: RNAnet a map of human gene expression. In: EMBO-2008, Heidelberg, 2008. Abstract presented

    Google Scholar 

  59. Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Berlin (2002)

    Book  MATH  Google Scholar 

  60. Langdon, W.B., Upton, G.J.G., da Silva Camargo, R., Harrison, A.P.: A survey of spatial defects in Homo Sapiens Affymetrix GeneChips. IEEE/ACM Trans. Comput. Biol. Bioinform. 7(4), 647–653 (2009)

    Article  Google Scholar 

  61. Langdon, W.B., Yoo, S., Harman, M.: Formal concept analysis on graphics hardware. In: Napoli, A., Vychodil, V. (eds.) The Eighth International Conference on Concept Lattices and Their Applications, Nancy, France, 2011, pp. 413–416 (2011) [INRIA Nancy and LORIA]

    Google Scholar 

  62. Lewis, T.E., Magoulas, G.D.: Strategies to minimise the total run time of cyclic graph based genetic programming with GPUs. In: Raidl, G., et al. (eds.) Genetic and Evolutionary Computation Conference, Montreal, 2009, pp. 1379–1386. ACM, New York (2009)

    Google Scholar 

  63. Lewis, T.E., Magoulas, G.D.: Identifying similarities in TMBL programs with alignment to quicken their compilation for GPUs. In: Harding, S., et al. (eds.) GECCO 2011 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU), Dublin, 2011, pp. 447–454. ACM, New York (2011)

    Google Scholar 

  64. Lewis, T.E., Magoulas, G.D. TMBL kernels for CUDA GPUs compile faster using PTX. In: Harding, S., et al. (eds.) GECCO 2011 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU), Dublin, 2011, pp. 455–462. ACM, New York (2011)

    Google Scholar 

  65. Lindblad, F., et al.: Evolving 3D model interpretation of images using graphics hardware. In: Fogel, D.B., et al. (eds.) Conference on Evolutionary Computation, 2002, pp. 225–230. IEEE Press, New York (2002)

    Google Scholar 

  66. Liu, B., et al.: Approximate probabilistic analysis of biopathway dynamics. Bioinformatics 28(11), 150–1516 (2012)

    Google Scholar 

  67. Liu, C.-M., et al.: SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics 28(6), 878–879 (2012)

    Article  Google Scholar 

  68. Liu, W., et al.: Bio-sequence database scanning on a GPU. In: International Parallel and Distributed Processing Symposium, Rhodes, Greece, 2006. IEEE Press, New York (2006)

    Google Scholar 

  69. Liu, Y., Suvranu, D.: CUDA-based real time surgery simulation. Stud. Health Technol. Inform. 132, 260–262 (2008)

    Google Scholar 

  70. Loviscach, J., Meyer-Spradow, J.: Genetic programming of vertex shaders. In: Chover, M., et al. (eds.) Proceedings of EuroMedia 2003, University of Plymouth, UK, 2003, pp. 29–31 (2003)

    Google Scholar 

  71. Luo, Z., Liu, H., Wu, X.: Artificial neural network computation on graphic process unit. In: International Joint Conference on Neural Networks, 2005, vol. 1, pp. 622–626. IEEE, New York (2005)

    Google Scholar 

  72. Luong, T.V., Melab, N., Talbi, E.-G.: Parallel hybrid evolutionary algorithms on GPU. In: Sobrevilla, P. (ed.) World Congress on Computational Intelligence, Barcelona, 2010, pp. 2734–2741. IEEE, New York (2010)

    Google Scholar 

  73. Maitre, O., et al.: Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA. In: Raidl, G., et al. (eds.) Genetic and Evolutionary Computation Conference, Montreal, 2009, pp. 1403–1410. ACM, New York (2009)

    Google Scholar 

  74. Maitre, O., et al.: Fast evaluation of GP trees on GPGPU by optimizing hardware scheduling.  In: Esparcia-Alcazar, A.I., et al. (eds.) European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 6021, Istanbul, 2010, pp. 301–312. Springer, Berlin (2010)

    Google Scholar 

  75. Maitre, O., et al.: EASEA parallelization of tree-based genetic programming. In: Sobrevilla, P. (ed.) World Congress on Computational Intelligence, Barcelona, 2010, pp. 1997–2004. IEEE, New York (2010)

    Google Scholar 

  76. Manavski, S., Valle, G.: CUDA compatible GPU cards as efficient hardware accelerators for Smith–Waterman sequence alignment. BMC Bioinformatics 9(Suppl. 2), S10 (2008)

    Article  Google Scholar 

  77. Meyer-Spradow, J., Loviscach, J.: Evolutionary design of BRDFs. In: Chover, M., et al. (eds.) Eurographics 2003 Short Paper Proceedings, pp. 301–306 (2003)

    Google Scholar 

  78. Miller, L.D., et al.: An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc. Natl. Acad. Sci. USA 102(38), 13550–13555 (2005)

    Article  Google Scholar 

  79. Moore, G.E.: Cramming more components onto integrated circuits. Electronics 38(8), 114–117 (1965)

    Google Scholar 

  80. Munawar, A., et al.: Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nvidia CUDA framework. Genet. Program. Evolvable Mach. 10(4), 391–415 (2009)

    Article  Google Scholar 

  81. Nordin, P.: A compiling genetic programming system that directly manipulates the machine code. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, Chap. 14, pp. 311–331. MIT Press, Cambridge (1994)

    Google Scholar 

  82. Owens, J.: Experiences with GPU computing, 2007. Presentation slides

    Google Scholar 

  83. Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Kruger, J., Lefohn, A.E., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. Comput. Graph. Forum 26(1), 80–113 (2007)

    Article  Google Scholar 

  84. Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU computing. Proc. IEEE 96(5), 879–899 (2008) [Invited paper]

    Google Scholar 

  85. Pedemonte, M., e al.: Bitwise operations for GPU implementation of genetic algorithms. In: Harding, S., et al. (eds.) GECCO 2011 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU), Dublin, 2011, pp. 439–446. ACM, New York (2011)

    Google Scholar 

  86. Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (2008) [With contributions by J.R. Koza]

  87. Pospichal, P., et al.: Acceleration of grammatical evolution using graphics processing units: computational intelligence on consumer games and graphics hardware. In: Harding, S., et al. (eds.) GECCO 2011 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU), Dublin, 2011, pp. 431–438. ACM, New York (2011)

    Google Scholar 

  88. Prabhu, R.D.: SOMGPU: an unsupervised pattern classifier on graphical processing unit. In: Wang, J. (ed.) World Congress on Computational Intelligence, Hong Kong, 2008, pp. 1011–1018. IEEE Press, New York (2008)

    Chapter  Google Scholar 

  89. Price, G.R.: Selection and covariance. Nature 227, 520–521 (1970)

    Google Scholar 

  90. Reggia, J., et al.: Development of a large-scale integrated neurocognitive architecture—part 2: design and architecture. Technical Report TR-CS-4827, UMIACS-TR-2006-43, University of Maryland, USA, October 2006

    Google Scholar 

  91. Ribeiro, B., Lopes, N., Silva, C.: High-performance bankruptcy prediction model using graphics processing units. In: Sobrevilla, P. (ed.) World Congress on Computational Intelligence, Barcelona, 2010, pp. 2210–2216. IEEE, New York (2010)

    Google Scholar 

  92. Robilliard, D., et al.: Population parallel GP on the G80 GPU. In: O’Neill, M., et al. (eds.) European Conference on Genetic Programming, Naples, 2008. Lecture Notes in Computer Science, vol. 4971, pp. 98–109. Springer, Berlin (2008)

    Google Scholar 

  93. Robilliard, D., et al.: Genetic programming on graphics processing units. Genet. Program. Evolvable Mach. 10(4), 447–471 (2009)

    Article  Google Scholar 

  94. Rouhipour, M., et al.: Systemic computation using graphics processors. In: Tempesti, G., et al. (eds.) International Conference on Evolvable Systems, York, 2010. Lecture Notes in Computer Science, vol. 6274, pp. 121–132. Springer, Berlin (2010)

    Google Scholar 

  95. Sato, M., Sato, Y., Namiki, M.: Acceleration experiment of genetic computations for Sudoku solution on multi-core processors. In: Blum, C. (ed.) GECCO Late Breaking Abstracts, Dublin, 2011, pp. 823–824. ACM, New York (2011)

    Google Scholar 

  96. Sitthi-amorn, P., et al.: Genetic programming for shader simplification. ACM Trans. Graph. 30(6), article:152 (2011) [Proceedings of ACM SIGGRAPH Asia 2011]

    Google Scholar 

  97. Soca, N., et al.: PUGACE, a cellular evolutionary algorithm framework on GPUs. In: Sobrevilla, P. (ed.) World Congress on Computational Intelligence, Barcelona, 2010, pp. 3891–3898. IEEE, New York (2010)

    Google Scholar 

  98. Stamatakis, A.: RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22(21), 2688–2690 (2006)

    Article  Google Scholar 

  99. Trapnell, C., Schatz, M.C.: Optimizing data intensive GPGPU computations for DNA sequence alignment. Parallel Comput. 35(8–9), 429–440 (2009)

    Article  Google Scholar 

  100. Unemi, T.: SBArt4—breeding abstract animations in realtime. In: World Congress on Computational Intelligence, Barcelona, Spain, 2010. IEEE Press, New York (2010)

    Google Scholar 

  101. Vouzis, P.D., Sahinidis, N.V.: GPU-BLAST: using graphics processors to accelerate protein sequence alignment. Bioinformatics 27(2), 182–188 (2011)

    Article  Google Scholar 

  102. Wilson, G., Banzhaf, W.: Linear genetic programming GPGPU on Microsoft’s Xbox 360. In: Wang, J., (ed.) World Congress on Computational Intelligence, Hong Kong, 2008, pp. 378–385. IEEE Press, New York (2008)

    Chapter  Google Scholar 

  103. Wilson, G.C., Banzhaf, W.: Deployment of CPU and GPU-based genetic programming on heterogeneous devices. In: Esparcia, A.I., et al. (eds.) GECCO Workshop on Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU-2009), Montreal, 2009, pp. 2531–2538. ACM, New York (2009)

    Google Scholar 

  104. Wilson, G., Banzhaf, W.: Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms. Genet. Program. Evolvable Mach. 11(2), 147–184 (2010)

    Article  Google Scholar 

  105. Wilson, G., Harding, S.: WCCI 2008 special session: computational intelligence on consumer games and graphics hardware (CIGPU-2008). SIGEvolution 3(1), 19–21 (2008)

    Google Scholar 

  106. Wirawan, A., Kwoh, C., Hieu, N., Schmidt, B.: CBESW: sequence alignment on the Playstation 3. BMC Bioinformatics 9(1), 377 (2008)

    Article  Google Scholar 

  107. Wong, M.L.: Parallel multi-objective evolutionary algorithms on graphics processing units. In: Genetic and Evolutionary Computation Conference, Montreal, 2009, pp. 2515–2522. ACM, New York (2009)

    Google Scholar 

  108. Yoo, S.: Evolving human competitive spectra-based fault localisation techniques. Research Note RN/12/03, Department of Computer Science, University College, London, UK, 2012

    Google Scholar 

  109. Yu, J., et al.: Feature selection and molecular classification of cancer using genetic programming. Neoplasia 9(4), 292–303 (2007)

    Article  Google Scholar 

  110. Yudanov, D., Shaaban, M., Melton, R., Reznik, L.: GPU-based implementation of real-time system for spiking neural networks. In: Sobrevilla, P., (ed.) World Congress on Computational Intelligence, Barcelona, 2010, pp. 2143–2150. IEEE, New York (2010)

    Google Scholar 

  111. Yung, L.S., Yang, C., Wan, X., Yu, W.: GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. Bioinformatics 27(9), 1309–1310 (2011)

    Article  Google Scholar 

  112. Zhou, J., Liu, X., Stones, D.S., Xie, Q., Wang, G.: MrBayes on a graphics processing unit. Bioinformatics 27(9), 1255–1261 (2011)

    Article  Google Scholar 

  113. Zhou, Y., Liepe, J., Sheng, X., Stumpf, M.P.H., C. Barnes: GPU accelerated biochemical network simulation. Bioinformatics 27(6), 874–876 (2011)

    Google Scholar 

  114. Zipf, G.K.: Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Addison-Wesley, Cambridge (1949)

    Google Scholar 

Download references

Acknowledgements

I would like to thank Shigeyoshi Tsutsui, Stan Seibert, Neil Daeche (UCL) and Derek Ross (STFC Rutherford Appleton Laboratory). The two C2050s were donated by nVidia as part of the GISMO EPSRC project.

Author information

Authors and Affiliations

  1. Department of Computer Science, University College, London, UK

    William B. Langdon

Authors
  1. William B. Langdon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William B. Langdon .

Editor information

Editors and Affiliations

  1. and Information Science, Hannan University Dept. of Management, Matsubara, Osaka, Japan

    Shigeyoshi Tsutsui

  2. ICube Laboratory UMR CNRS 7357, Université de Strasbourg, Illkirch, France

    Pierre Collet

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Langdon, W.B. (2013). Large-Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units. In: Tsutsui, S., Collet, P. (eds) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37959-8_15

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37958-1

  • Online ISBN: 978-3-642-37959-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation