Efficient Implementing of DNA Computing-inspired Pattern Classifier Using GPU

GPU를 이용한 DNA 컴퓨팅 기반 패턴 분류기의 효율적 구현

  • 최선욱 (인하대 공대 정보통신공학과) ;
  • 이종호 (인하대 공대 정보통신공학과)
  • Published : 2009.07.01


DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.


  1. Zhang, B.-T., 'Molecular nanobiointelligence computers: computer science meets biotechnology, nonotechnology, and cognitive science (in Korean)', Communications of the Korea Information Science Society, 23(5):41-56, 2005.
  2. Sienko, T., Adamatzky, A., Rambidi, N.G., and Conrad, M., 'Molecular Computing', MIT Press, 2003
  3. Adleman, L., 'Molecular computation of solutions to combinatorial problems', Science, 266: 1021-1024, 1994 https://doi.org/10.1126/science.7973651
  4. Braich, R.S., Chelyapov, N., Johnson, C., Rothemund, P. W.K., and Adleman, L., 'Solution of a 20-variable 3-SAT problem on a DNA computer', Science, 296: 499-502, 2002 https://doi.org/10.1126/science.1069528
  5. Faulhammer, D., Cukras, A.R, Lipton, R.J., and Landweber, L.F., 'Molecular computation: RNA solutions to chess problems', Proc. Natl. Acad. Sci. USA, 97(4):1385-1389, 2000 https://doi.org/10.1073/pnas.97.4.1385
  6. Zhang, B.T., Jang, H.Y., 'A bayesian algorithm for in vitro molecular evolution of pattern classifiers', DNA computing 10. LNCS 3384, pp. 458-467, 2002 https://doi.org/10.1007/11493785_39
  7. Wetmur, J., 'Physical chemistry of nucleic acid hybridization. DNA Based Computers III', DIMACS Series in Discrete Mathematics and Theoretical Computer Science 48, pp. 1-23, 1999
  8. Zhang, B.T., Kim, J.K., 'Dna hypernetworks for information storage and retrieval', DNA computing 12. LNCS 4287, pp. 298-307, 2003 https://doi.org/10.1007/11925903_23
  9. Zhang, B.T., 'Solving logic problems by DNA: self-assembly process of double helix is a computational algorithm (in Korean)', Donga Science, 22(6), pp. 78-81, 2007
  10. Ha, J., Eom, J., Kim, S., Zhang, B., 'Evolutionary hypernetwork models for aptamer-based cardiovascu lar disease diagnosis', In: Proc. of the GECCO 2007 ACM New York, NY, USA, pp. 2709-2716, 2007 https://doi.org/10.1145/1274000.1274073
  11. Kim, J.K., Zhang, B.T., 'Evolving hypernetworks for pattern classification', In: Proc. of IEEE CEC 2007, pp. 1856-1862, 2007 https://doi.org/10.1109/CEC.2007.4424699
  12. Poli, Gustavo; Saito, Jose Hiroki; Mari, Joao F.; Zorzan, Marcelo R, 'Processing Neocognitron of Face Recognition on High Performance Environment Based on GPU with CUDA Architecture,' Computer Architecture and High Performance Computing, 200S. SBAC-PAD '08. 20th International Symposium on , vol., no., pp.81-88, Oct. 29 2008-Nov. 1 2008 https://doi.org/10.1109/SBAC-PAD.2008.25
  13. Andreas Brandstetter, Alessandro Artusi, 'Radial Basis Function Networks GPU-Based Implemen tation', IEEE TRANSACTIONS ON NEURAL NETWO RKS, VOL. 19, NO. 12, Dec., 2008 https://doi.org/10.1109/TNN.2008.2003284
  14. K-S. Oh, K. Jung, 'GPU implementation of neural networks,' Pattern Recognition, vol. 37, no. 6, pp. 1311-1314, 2004 https://doi.org/10.1016/j.patcog.2004.01.013
  15. Woodbeck, K., Roth, G., Huiqiong Chen, 'Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition', Computer Vision and Pattern Recognition Workshops, Computer Vision and Pattern Recognition Workshops, 1-8, 2008. https://doi.org/10.1109/CVPRW.2008.4563091
  16. Zhong wen Luo, Hongzhi Liu, Zhengping Yang, Xincai Wu, 'Self-Organizing Maps computing on Graphic Process Unit,' in 13th European Symposium on Artificial Neural Network, Bkselgium,2005, pp. 557-562
  17. Prabhu, R.D., 'SOMGPU: An unsupervised pattern classifier on Graphical Processing Unit,' Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on , vol., no., pp.1011-1018, 1-6 June 2008 https://doi.org/10.1109/CEC.2008.4630920
  18. 이만희, 박인규, 원석진, 조성대, 'GPU를 이용한 DWT 및 JPEG2000 구현', 전자공학회논문지, 제44권 SP편 제6호, pp. 9-15, 2007. 11
  19. NVIDIA, 'NVIDIA CUDA Programming Guide 2.0', 2008
  20. Zhang, B.T., Kim, J.K, 'Dna hypernetworks for information storage and retrieval', DNA computing 12. LNCS 42S7, pp. 298-307, 2003. https://doi.org/10.1007/11925903_23
  21. Zhang, B.T., 'Hypernetworks: A molecular evolutio nary architecture for cognitive learning and memory', Computational Intelligence Magazine, IEEE 3 (3), pp. 49-63, 2008 https://doi.org/10.1109/MCI.2008.926615
  22. Mark Harris, Optimizing Parallel Reduction in CUDA, 2008
  23. UCI Machine Learning Repository, http://archive.ic s.uci.edu/ml
  24. Yeoh, E., Ross, M., Shurtleff, S., Williams, W., Patel, D., Mahfouz, R, Behm, F., Raimondi, S., Reiling, M., Patel, A., et aI., 'Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling', Cancer Cell 1(2), pp. 133-143, 2002 https://doi.org/10.1016/S1535-6108(02)00032-6
  25. Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., et aI., 'Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring', Science 286(5439), pp. 531-537, 1999 https://doi.org/10.1126/science.286.5439.531
  26. Oliva, A., Torralba, A, 'Modeling the shape of the scene: A holistic representation of the spatial envelope', International Journal of Computer Vision 42(3), pp. 145-175, 2001 https://doi.org/10.1023/A:1011139631724
  27. Won, C.S., 'Feature Extraction and Evaluation Using Edge Histogram Descriptor in MPEG-7', Advances in Multimedia Information Processing-PCM 3333, pp. 583-590, 2004 https://doi.org/10.1007/978-3-540-30543-9_73