Implementing Enzymatic Numerical P Systems for AI Applications by Means of Graphic Processing Units

  • Manuel García-QuismondoEmail author
  • Luis F. Macías-Ramos
  • Mario J. Pérez-Jiménez
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 4)


A P system represents a distributed and parallel computing model in which basic data structures are, for instance, multisets and strings. Enzymatic Numerical P Systems (ENPS) are a type of P systems whose basic data structures are sets of numerical variables. Separately, GPGPU (general-purpose computing on graphics processing units) is a novel technological paradigm which focuses on the development of tools for graphic cards to solve general purpose problems. This paper proposes an ENPS simulator based on GPUs and presents general concepts about its design and some future ideas and perspectives.


Mobile Robot Production Function Graphic Processing Unit Obstacle Avoidance Parallel Architecture 
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.
    Antonelli, G., Chiaverini, S., Fusco, G.: A calibration method for odometry of mobile robots based on the least-square technique: Theory and experimental validation. IEEE Transactions on Robotics 21(5), 994–1004 (2005)CrossRefGoogle Scholar
  2. 2.
    Cabarle, F.G.C., Adorna, H., Martínez, M.A.: A Spiking Neural P System Simulator Based on CUDA. In: Gheorghe, M., Păun, G., Rozenberg, G., Salomaa, A., Verlan, S. (eds.) CMC 2011. LNCS, vol. 7184, pp. 87–103. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Cardona, M., Colomer, M.A., Pérez-Jiménez, M.J., Sanuy, D., Margalida, A.: Modeling Ecosystems Using P Systems: The Bearded Vulture, a Case Study. In: Corne, D.W., Frisco, P., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC9 2008. LNCS, vol. 5391, pp. 137–156. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Cecilia, J.M., García, J.M., Guerrero, G.D., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J.: Simulation of P systems with Active Membranes on CUDA. Briefings in Bioinformatics 11(3), 313–322 (2010)CrossRefGoogle Scholar
  5. 5.
    Cecilia, J.M., García, J.M., Guerrero, G.D., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J.: Simulating a P system based efficient solution to SAT by using GPUs. The Journal of Logic and Algebraic Programming 79, 317–325 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Ceterchi, R., Mutyam, M., Păun, G., Subramanian, K.G.: Array-rewriting P systems. Natural Computing 2(3), 229–249 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Christinal, H.A., Díaz-Perni, D., Gutiérrez-Naranjo, M.A., Pérez-Jiménez, M.J.: Thresholding of 2D Images with Cell-like P Systems. Romanian Journal of Information Science and Technology (ROMJIST) 13(2), 131–140 (2010)Google Scholar
  8. 8.
    Christinal, H.A., Díaz-Pernil, D., Gutiérrez-Naranjo, M.A., Pérez-Jiménez, M.J.: Tissue-like P Systems Without Environment. In: Martínez del Amor, M.A., Păun, G., Pérez-Hurtado, I., Riscos, A. (eds.) Proceedings of the Eighth Brainstorming Week on Membrane Computing, pp. 53–64. Fenix Editora (2010)Google Scholar
  9. 9.
    Colomer, M.A., Lavín, S., Marco, I., Margalida, A., Pérez-Hurtado, I., Pérez-Jiménez, M.J., Sanuy, D., Serrano, E., Valencia-Cabrera, L.: Modeling Population Growth of Pyrenean Chamois (Rupicapra p. pyrenaica) by Using P-Systems. In: Gheorghe, M., Hinze, T., Păun, G., Rozenberg, G., Salomaa, A. (eds.) CMC 2010. LNCS, vol. 6501, pp. 144–159. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Conforth, M., Meng, Y.: An Artificial Neural Network Based Learning Method for Mobile Robot Localization. Robotics Automation and Control 6 (2008)Google Scholar
  11. 11.
    Dong, J., Liu, B., Peng, K., Yin, Y.: Robot Obstacle Avoidance based on an Improved Ant Colony Algorithm. In: Zhou, S.M., Wang, W. (eds.) Proceedings of WRI Global Congress on Intelligent Systems (GCIS 2009), pp. 103–106. IEEE Computer Society (2009)Google Scholar
  12. 12.
    Du, R., Zhang, X., Chen, C., Guan, X.: Path Planning with Obstacle Avoidance in PEGs: Ant Colony Optimization Method. In: Zhu, P. (ed.) International Conference on Cyber, Physical and Social Computing (CPSCom), pp. 768–773. IEEE Computer Society (2010)Google Scholar
  13. 13.
    García-Quismondo, M., Gutiérrez-Escudero, R., Martínez-del-Amor, M.A., Orejuela-Pinedo, E., Pérez-Hurtado, I.: P-Lingua 2.0: A software framework for cell-like P systems. International Journal of Computers, Communications and Control 4(3), 234–243 (2009)Google Scholar
  14. 14.
    Garland, M., Le Grand, S., Nickolls, J., Anderson, J., Hardwick, J., Morton, S., Phillips, E., Zhang, Y., Volkov, V.: Parallel computing experiences with CUDA. IEEE Micro 28(4), 13–27 (2008)CrossRefGoogle Scholar
  15. 15.
    Gill, M.A.C., Zomaya, A.Y.: Genetic algorithms for robot control. In: IEEE International Conference on Evolutionary Computation, p. 462. IEEE Computer Society (1996)Google Scholar
  16. 16.
    Gerstner, W., Kistler, W.: Spiking Neuron Models. Single Neurons, Populations, Plasticity. Cambridge Univ. Press (2002)Google Scholar
  17. 17.
    Ibarra, O., Pérez-Jimenez, M.J., Yokomori, T.: On spiking neural P systems. Natural Computing 9(2), 475–491 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Ionescu, M., Paun, G., Yokomori, T.: Spiking neural P systems. Fundamenta Informaticae 71 (2-3), 279–308 (2006)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Ionescu, M., Paun, G., Pérez–Jiménez, M.J., Rodríguez-Patón, A.: Spiking Neural P systems with several types of spikes. International Journal of Computers, Communications & Control 4(4), 648–656 (2011)Google Scholar
  20. 20.
    Ivankjo, E., Komšić, I., Petrović, I.: Simple Off-Line Odometry Calibration of Differential Drive Mobile Robots. In: Proceedings of 16th International Workshop on Robotics in Alpe-Adria-Danube Region - RAAD, pp. 164–169 (2007)Google Scholar
  21. 21.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research 5(1), 90–98 (1986)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Maass, W.: Computing with spikes. Special Issue on Foundations of Information Processing of TELEMATIK 8(1), 32–36 (2002)Google Scholar
  23. 23.
    Maass, W., Bishop, C. (eds.): Pulsed Neural Networks. MIT Press, Cambridge (1999)Google Scholar
  24. 24.
    Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with CUDA. Queue 6(2), 40–53 (2008)CrossRefGoogle Scholar
  25. 25.
    NVIDIA. NVIDIA CUDA Programming Guide 2.0 (2008)Google Scholar
  26. 26.
    Pan, L., Pérez-Jiménez, M.J.: Computational complexity of tissue-like P systems. Journal of Complexity 26(3), 296–315 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Pan, L., Paun, G., Pérez-Jiménez, M.J.: Spiking neural P systems with neuron division and budding. Science China. Information Sciences. 54(8), 1596–1607 (2011)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Pan, L., Wang, J., Hoogeboom, H.J.: Asynchronous Extended Spiking Neural P Systems with Astrocytes. In: Gheorghe, M., Păun, G., Rozenberg, G., Salomaa, A., Verlan, S. (eds.) CMC 2011. LNCS, vol. 7184, pp. 243–256. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  29. 29.
    Paun, G., Paun, R.: Membrane Computing and Economics: Numerical P Systems. Fundamenta Informaticae 73(1-2), 213–227 (2006)MathSciNetzbMATHGoogle Scholar
  30. 30.
    Paun, G.: Computing with membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Paun, G.: Membrane Computing. An Introduction. Springer, Heidelberg (2002)zbMATHCrossRefGoogle Scholar
  32. 32.
    Paun, G.: Computing with Membranes. Turku Centre for Computer Science, Turku, Finland, vol. 208 (1998)Google Scholar
  33. 33.
    Paun, G., Pérez-Jiménez, M.J., Riscos, A.: Tissue P systems with cell division. International Journal of Computers, Communications & Control 3(3), 295–303 (2008)Google Scholar
  34. 34.
    Paun, G., Paun, R.: Membrane computing as a framework for modeling economic processes. In: Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2005). IEEE Computer Society, Washington DC (2006)Google Scholar
  35. 35.
    Păun, A., Păun, G.: The power of communication: P systems with symport/antiport. New Generation Computing 20(3), 295–305 (2002)zbMATHCrossRefGoogle Scholar
  36. 36.
    Pavel, A., Arsene, O., Buiu, C.: Enzymatic Numerical P Systems - A New Class of Membrane Computing Systems. In: Proceedings 2010 IEEE Fifth International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2010), pp. 1331–1336. IEEE Computer Society, Liverpool (2010)CrossRefGoogle Scholar
  37. 37.
    Pavel, A., Buiu, C.: Using enzymatic numerical P systems for modeling mobile robot controllers. Natural Computing (2011) (in press)Google Scholar
  38. 38.
    Romero, F.J., Pérez-Jiménez, M.J.: A model of the Quorum Sensing System in Vibrio Fischeri using P systems. Artificial Life 14(1), 95–109 (2008)CrossRefGoogle Scholar
  39. 39.
    Takizawa, H., Koyama, K., Sato, K., Komatsu, K., Kobayashi, H.: CheCL: Transparent Checkpointing and Process Migration of OpenCL Applications. In: International Parallel and Distributed Processing Symposium (IPDPS 2011), pp. 864–876. IEEE Computer Society, Anchorage (2011)Google Scholar
  40. 40.
  41. 41.
  42. 42.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Manuel García-Quismondo
    • 1
    Email author
  • Luis F. Macías-Ramos
    • 1
  • Mario J. Pérez-Jiménez
    • 1
  1. 1.Research Group on Natural Computing, Dpt. of Computer Science and Artificial IntelligenceUniversity of SevillaSevillaSpain

Personalised recommendations