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A Spiking Neural P System Simulator Based on CUDA

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7184)

Abstract

In this paper we present a Spiking Neural P system (SNP system) simulator based on graphics processing units (GPUs). In particular we implement the simulator using NVIDIA CUDA enabled GPUs. The massively parallel architecture of current GPUs is very suitable for the maximally parallel computations of SNP systems. We simulate a wider variety of SNP systems, after presenting a previous work on SNP system matrix representation which led to their simulation in GPUs, and the simulation algorithm included here. Finally, we compare and present the performance speedups of the CPU-GPU based simulator over the CPU only simulator.

Keywords

  • Membrane Computing
  • Spiking Neural P systems
  • Parallel Computing
  • GPU Computing
  • CUDA

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References

  1. Cabarle, F., Adorna, H., Martínez-del-Amor, M.A.: Simulating Spiking Neural P systems without delays using GPUs. In: Proceedings of the 9th Brainstorming Week on Membrane Computing, Sevilla, Spain (February 2011)

    Google Scholar 

  2. Cabarle, F., Adorna, H., Martínez-del-Amor, M.A.: An Improved GPU Simulator For Spiking Neural P Systems. In: IEEE Sixth International Conference on Bio-Inspired Computing: Theories and Applications, Penang, Malaysia (September 2011), doi:10.1109/BIC-TA.2011.37

    Google Scholar 

  3. 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)

    CrossRef  Google Scholar 

  4. 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. Journal of Logic and Algebraic Programming 79(6), 317–325 (2010)

    CrossRef  MathSciNet  MATH  Google Scholar 

  5. Chen, H., Ionescu, M., Ishdorj, T.-O., Păun, A., Păun, G., Pérez-Jiménez, M.: Spiking neural P systems with extended rules: universality and languages. Natural Computing: an International Journal 7(2), 147–166 (2008)

    CrossRef  MathSciNet  MATH  Google Scholar 

  6. Ciobanu, G., Wenyuan, G.: P Systems Running on a Cluster of Computers. In: Martín-Vide, C., Mauri, G., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2003. LNCS, vol. 2933, pp. 123–139. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  7. Díaz, D., Graciani, C., Gutiérrez, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J.: Software for P systems. In: Păun, G., Rozenberg, G., Salomaa, A. (eds.) The Oxford Handbook of Membrane Computing, ch. 17, pp. 437–454. Oxford University Press, Oxford (2009)

    Google Scholar 

  8. Fatahalian, K., Sugerman, J., Hanrahan, P.: Understanding the efficiency of GPU algorithms for matrix-matrix multiplication. In: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware (HWWS 2004), pp. 133–137. ACM, NY (2004)

    CrossRef  Google Scholar 

  9. Garland, M., Kirk, D.B.: Understanding throughput-oriented architectures. Communications of the ACM 53(11), 58–66 (2010)

    CrossRef  Google Scholar 

  10. Harris, M.: Mapping computational concepts to GPUs. In: ACM SIGGRAPH 2005 Courses, NY, USA (2005)

    Google Scholar 

  11. Ionescu, M., Păun, G., Yokomori, T.: Spiking Neural P Systems. Journal Fundamenta Informaticae 71(2,3), 279–308 (2006)

    MathSciNet  MATH  Google Scholar 

  12. Kirk, D., Hwu, W.: Programming Massively Parallel Processors: A Hands on Approach, 1st edn. Morgan Kaufmann, MA, USA (2010)

    Google Scholar 

  13. Klöckner, A., Pinto, N., Lee, Y., Catanzaro, B., Ivanov, P., Fasih, A.: PyCUDA: GPU Run-Time Code Generation for High-Performance Computing, no. 2009-40. Scientific Computing Group, Brown University, RI, USA (2009)

    Google Scholar 

  14. Păun, G., Ciobanu, G., Pérez-Jiménez, M. (eds.): Applications of Membrane Computing. Natural Computing Series. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  15. Nguyen, V., Kearney, D., Gioiosa, G.: A Region-Oriented Hardware Implementation for Membrane Computing Applications. In: Păun, G., Pérez-Jiménez, M.J., Riscos-Núñez, A., Rozenberg, G., Salomaa, A. (eds.) WMC 2009. LNCS, vol. 5957, pp. 385–409. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  16. Zeng, X., Adorna, H., Martínez-del-Amor, M.Á., Pan, L., Pérez-Jiménez, M.J.: Matrix Representation of Spiking Neural P Systems. In: Gheorghe, M., Hinze, T., Păun, G., Rozenberg, G., Salomaa, A. (eds.) CMC 2010. LNCS, vol. 6501, pp. 377–391. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  17. NVIDIA corporation, NVIDIA CUDA C programming guide, version 3.0. NVIDIA, CA, USA (2010)

    Google Scholar 

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Cabarle, F.G.C., Adorna, H., Martínez, M.A. (2012). A Spiking Neural P System Simulator Based on CUDA. In: Gheorghe, M., Păun, G., Rozenberg, G., Salomaa, A., Verlan, S. (eds) Membrane Computing. CMC 2011. Lecture Notes in Computer Science, vol 7184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28024-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-28024-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28023-8

  • Online ISBN: 978-3-642-28024-5

  • eBook Packages: Computer ScienceComputer Science (R0)