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Simulating Spiking Neural P systems without delays using GPUs

Francis Cabarle, Henry Adorna, Miguel A. Martinez-del-Amor
Algorithms & Complexity Lab, Department of Computer Science, University of the Philippines Diliman, Diliman 1101 Quezon City, Philippines
arXiv:1104.1824 [cs.DC] (11 Apr 2011)

@article{2011arXiv1104.1824C,

   author={Cabarle}, F. and {Adorna}, H. and {Martinez-del-Amor}, M.~A.},

   title={“{Simulating Spiking Neural P systems without delays using GPUs}”},

   journal={ArXiv e-prints},

   archivePrefix={“arXiv”},

   eprint={1104.1824},

   primaryClass={“cs.DC”},

   keywords={Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Emerging Technologies, Computer Science – Formal Languages and Automata Theory, Computer Science – Graphics, Computer Science – Neural and Evolutionary Computing, 68Q85},

   year={2011},

   month={apr},

   adsurl={http://adsabs.harvard.edu/abs/2011arXiv1104.1824C},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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We present in this paper our work regarding simulating a type of P system known as a spiking neural P system (SNP system) using graphics processing units (GPUs). GPUs, because of their architectural optimization for parallel computations, are well-suited for highly parallelizable problems. Due to the advent of general purpose GPU computing in recent years, GPUs are not limited to graphics and video processing alone, but include computationally intensive scientific and mathematical applications as well. Moreover P systems, including SNP systems, are inherently and maximally parallel computing models whose inspirations are taken from the functioning and dynamics of a living cell. In particular, SNP systems try to give a modest but formal representation of a special type of cell known as the neuron and their interactions with one another. The nature of SNP systems allowed their representation as matrices, which is a crucial step in simulating them on highly parallel devices such as GPUs. The highly parallel nature of SNP systems necessitate the use of hardware intended for parallel computations. The simulation algorithms, design considerations, and implementation are presented. Finally, simulation results, observations, and analyses using an SNP system that generates all numbers in $mathbb N$ – {1} are discussed, as well as recommendations for future work.
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