Simulação de Neurônios Biologicamente Realistas em GPUs
Resumo
GPUs modernas são compostas por centenas de processadores capazes de executar milhares de threads simultaneamente e estão sendo utilizadas com sucesso em diversas aplicações na área de computação de alto-desempenho. Neste trabalho apresentamos um simulador de modelos neuronais realistas capaz de simular centenas de milhares de neurônios por GPU. Resultados experimentais mostram que as simulações são executadas 5 vezes mais rápidas em GPUs, quando comparadas a CPUs modernas.Referências
U. S. Bhalla. MOOSE (multiscale object-oriented simulation environment) site. http://moose.sourceforge.net.
J. M. Bower and D. Beeman. The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. Springer-Verlag, second edition, 1998.
A. Brandstetter and A. Artusi. Radial basis function networks GPU-based implementation. IEEE Transactions on Neural Networks, 19(12):2150–2154, 2008.
N. Carnevale and M. Hines. The NEURON Book. Cambridge University Press, 2006.
B. Catanzaro, N. Sundaram, and K. Keutzer. Fast support vector machine training and classification on graphics processors. In ICML ’08: Proc. of the 25th Int. Conf. on Machine Learning, pages 104–111. ACM Press, 2008.
M. Hines. Efficient computation of branched nerve equations. International Journal Biomedical Computation, 15(1):69–76, January 1984.
M. Hines and N. Carnevale. Translating network models to parallel hardware in NEURON. Journal of Neuroscience Methods, 169(2):425–455, April 2008.
T.-Y. Ho, P.-M. Lam, and C.-S. Leung. Parallelization of celular neural networks on gpu. Pattern Recognition, 41:2684–2692, 2008.
C. Koch and I. Segev, editors. Methods in Neuronal Modeling: From Ions to Networks. MIT Press, 2nd edition, 1999.
Kyoung-Su and K. Jung. Gpu implementation of neural networks. Pattern Recognition, 37:1311–1314, 2004.
H. Li and L. Petzold. Efficient parallelization of the stochastic simulation algorithm for chemically reacting systems on the GPU. International Journal of High Performance Applications, To appear, 2009.
nVidia Corporation. CUDA 2.1 Programming Guide, 2009.
J. D. Owens, M. Houston, D. Luebke, S. Green, J. E. Stone, and J. C. Phillips. Gpu computing. Proceedings of the IEEE, 96(5):879–899, 2008.
J. M. Bower and D. Beeman. The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. Springer-Verlag, second edition, 1998.
A. Brandstetter and A. Artusi. Radial basis function networks GPU-based implementation. IEEE Transactions on Neural Networks, 19(12):2150–2154, 2008.
N. Carnevale and M. Hines. The NEURON Book. Cambridge University Press, 2006.
B. Catanzaro, N. Sundaram, and K. Keutzer. Fast support vector machine training and classification on graphics processors. In ICML ’08: Proc. of the 25th Int. Conf. on Machine Learning, pages 104–111. ACM Press, 2008.
M. Hines. Efficient computation of branched nerve equations. International Journal Biomedical Computation, 15(1):69–76, January 1984.
M. Hines and N. Carnevale. Translating network models to parallel hardware in NEURON. Journal of Neuroscience Methods, 169(2):425–455, April 2008.
T.-Y. Ho, P.-M. Lam, and C.-S. Leung. Parallelization of celular neural networks on gpu. Pattern Recognition, 41:2684–2692, 2008.
C. Koch and I. Segev, editors. Methods in Neuronal Modeling: From Ions to Networks. MIT Press, 2nd edition, 1999.
Kyoung-Su and K. Jung. Gpu implementation of neural networks. Pattern Recognition, 37:1311–1314, 2004.
H. Li and L. Petzold. Efficient parallelization of the stochastic simulation algorithm for chemically reacting systems on the GPU. International Journal of High Performance Applications, To appear, 2009.
nVidia Corporation. CUDA 2.1 Programming Guide, 2009.
J. D. Owens, M. Houston, D. Luebke, S. Green, J. E. Stone, and J. C. Phillips. Gpu computing. Proceedings of the IEEE, 96(5):879–899, 2008.
Publicado
28/10/2009
Como Citar
CAMARGO, Raphael Y. de.
Simulação de Neurônios Biologicamente Realistas em GPUs. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 10. , 2009, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2009
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p. 153-160.
DOI: https://doi.org/10.5753/wscad.2009.17404.
