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Parallel Implementation of P Systems for Data Clustering on GPU

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Bio-Inspired Computing -- Theories and Applications (BIC-TA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 562))

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Abstract

Membrane clustering algorithm is a novel membrane computing-inspired clustering algorithm, whose key component is a P system. Although P systems are distributed and parallel computing models, the membrane clustering algorithm was only realized in a serial algorithm because of serial architecture of current computer. Therefore, the membrane clustering algorithm was not able to exhibit the parallel computing characteristic of P systems. This paper focuses on parallel implementation of membrane clustering algorithm and proposes a GPU-based parallel computing framework and parallel version of the membrane clustering algorithm. In the parallel implementation, the blocks are used to represent the cells, while threads are used to realize the evolution-communication mechanism of objects. The comparison results on several artificial and real-life data sets demonstrate that the proposed parallel version not only ensures the clustering quality of the membrane clustering algorithm but also evidently reduce its computing time.

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References

  1. Pǎun, G.: Computing with membranes. J. Comput. Syst. Sci. 61(1), 108–143 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  2. Pǎun, G., Pérez-Jiménez, M.J.: Membrane computing: brief introduction, recent results and applications. BioSystem 85, 11–22 (2006)

    Article  Google Scholar 

  3. PÇŽun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrance Computing. Oxford University Press, New York (2010)

    MATH  Google Scholar 

  4. Pan, L., Zeng, X.: Small universal spiking neural P systems working in exhaustive mode. IEEE Trans. NanBiosci. 10(2), 99–105 (2011)

    Article  Google Scholar 

  5. Zhang, X., Liu, Y., Luo, B., Pan, L.: Computational power of tissue P systems for generating control languages. Inf. Sci. 278(10), 285–297 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  6. Cabarle, F., Adorna, H., Martínez-del-Amor, M.A., Pérez-Jiménez, M.J.: Improving GPU simulations of spiking neural P systems. Rom. J. Inf. Sci. Technol. 15(1), 5–20 (2012)

    Google Scholar 

  7. Cecilia, J.M., Garca, J.M., Guerrero, G.D., Martínez-del-Amor, M.A., Pérez-Jiménez, M.J., Ujaldón, M.: The GPU on the simulation of cellular computing models. Soft Comput. 16(2), 231–246 (2012)

    Article  Google Scholar 

  8. Martínez-del-Amor, M.A., Pérez-Carrasco, J., Pérez-Jiménez, M.J.: Characterizing the parallel simulation of P systems on the GPU. Int. J. Unconventional Comput. 9(5–6), 405–424 (2013)

    Google Scholar 

  9. Shi, X., Wang, Z., Deng, C., Song, T., Pan, L., Chen, Z.: A novel bio-sensor based on DNA strand displacement. PLoS ONE 9(10), e108856 (2014)

    Article  Google Scholar 

  10. Wang, X., Miao, Y., Cheng, M.: Finding motifs in DNA sequences using low-dispersion sequences. J. Comput. Biol. 21(4), 320–329 (2014)

    Article  MathSciNet  Google Scholar 

  11. Wang, X., Miao, Y.: GAEM: a hybrid algorithm incorporating GA with EM for planted edited motif finding problem. Curr. Bioinf. 9(5), 463–469 (2014)

    Article  MathSciNet  Google Scholar 

  12. Martínez-del-Amor, M.A., et al.: DCBA: simulating population dynamics P systems with proportional object distribution. In: Csuhaj-Varjú, E., Gheorghe, M., Rozenberg, G., Salomaa, A., Vaszil, G. (eds.) CMC 2012. LNCS, vol. 7762, pp. 257–276. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  13. Zhang, G.X., Cheng, J.X., Gheorghe, M., Meng, Q.: A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Appl. Soft Comput. 13(3), 1528–1542 (2013)

    Article  Google Scholar 

  14. Wang, J., Shi, P., Peng, H., Pérez-Jiménez, M.J., Wang, T.: Weighted fuzzy spiking neural P systems. IEEE Trans. Fuzzy Syst. 21(2), 209–220 (2013)

    Article  Google Scholar 

  15. Song, T., Pan, L.: Normal forms for some classes of sequential spiking neural P systems. IEEE Trans. NanoBiosci. 12(3), 255–264 (2013)

    Article  Google Scholar 

  16. Peng, H., Wang, J., Pérez-Jiménez, M.J., Wang, H., Shao, J., Wang, T.: Fuzzy reasoning spiking neural P system for fault diagnosis. Inf. Sci. 235, 106–116 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Peng, H., Wang, J., Pérez-Jiménez, M.J., Shi, P.: A novel image thresholding method based on membrane computing and fuzzy entropy. J. Intell. Fuzzy Syst. 24(2), 29–237 (2013)

    Google Scholar 

  18. Peng, H., Wang, J., Pérez-Jiménez, M.J., Riscos-Núñez, A.: The framework of P systems applied to solve optimal watermarking problem. Sig. Process. 101, 256–265 (2014)

    Article  Google Scholar 

  19. Huang, X., Peng, H., Jiang, Y., Zhang, J., Wang, J.: PSO-MC: a novel PSO-based membrane clustering algorithm. ICIC Express Lett. 8(2), 497–503 (2014)

    Google Scholar 

  20. Peng, H., Zhang, J., Jiang, Y., Huang, X., Wang, J.: DE-MC: a membrane clustering algorithm based on differential evolution mechanism. Rom. J. Inf. Sci. Technol. 17(1), 76–88 (2014)

    Google Scholar 

  21. Song, T., Pan, L.: Spiking neural P systems with rules on synapses working in maximum spikes consumption strategy. IEEE Trans. NanoBiosci. 14(1), 38–44 (2015)

    Article  Google Scholar 

  22. Jiang, Y., Peng, H., Huang, X., Zhang, J., Shi, P.: A novel clustering algorithm based on P systems. Int. J. Innovative Comput. Inf. Control 10(2), 753–765 (2014)

    Google Scholar 

  23. Peng, H., Jiang, Y., Wang, J., Pérez-Jiménez, M.J.: Membrne clustering algorithm with hybrid evolutionary mechanisms. J. Softw. 26(5), 1001–1012 (2015) (in Chinese)

    Google Scholar 

  24. Peng, H., Wang, J., Pérez-Jiménez, M.J., Riscos-Núñez, A.: An unsupervised learning algorithm for membrane computing. Inf. Sci. 304, 80–91 (2015)

    Article  MATH  Google Scholar 

  25. Song, T., Pan, L.: Spiking neural P systems with rules on synapses working in maximum spiking strategy. IEEE Trans. NanoBiosci. 14(4), 465–477 (2015)

    Article  Google Scholar 

  26. Song, T., Pan, L., Păun, G.: Asynchronous spiking neural P systems with local synchronization. Inf. Sci. 219, 197–207 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  27. Zhang, X., Pan, L., Paun, A.: On the universality of axon P systems. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2816–2829 (2015). doi:10.1109/TNNLS.2015.2396940

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61170030 and 61472328), and Research Fund of Sichuan Science and Technology Project (No. 2015HH0057), China.

Author information

Authors and Affiliations

  1. School of Computer and Software Engineering, Center for Radio Administration and Technology Development, Xihua University, Chengdu, 610039, China

    Jie Jin, Hui Liu, Fengjuan Wang & Hong Peng

  2. School of Electrical and Information Engineering, Xihua University, Chengdu, 610039, Sichuan, China

    Jun Wang

Authors
  1. Jie Jin
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  2. Hui Liu
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  3. Fengjuan Wang
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  4. Hong Peng
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  5. Jun Wang
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Corresponding author

Correspondence to Hong Peng .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Maoguo Gong

  2. Huazhong Univ. of Science and Technology, Wuhan, China

    Pan Linqiang

  3. University of Petroleum, Qingdao, China

    Song Tao

  4. University of Science and Technology of China, Hefei, China

    Ke Tang

  5. Anhui University, Hefei, China

    Xingyi Zhang

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© 2015 Springer-Verlag Berlin Heidelberg

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Jin, J., Liu, H., Wang, F., Peng, H., Wang, J. (2015). Parallel Implementation of P Systems for Data Clustering on GPU. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_18

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  • DOI: https://doi.org/10.1007/978-3-662-49014-3_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49013-6

  • Online ISBN: 978-3-662-49014-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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