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Improving GPU Simulations of Spiking Neural P Systems

Francis George C. Cabarle, Henry N. Adorna, Miguel A. Martinez-Del-Amor, Mario J. Perez-Jimenez
Algorithms and Complexity lab, Department of Computer Science, University of the Philippines Diliman
Romanian Journal of Information Science and Technology, Volume 15, Number 1, 5-20, 2012

@article{cabarle2012improving,

   title={Improving GPU Simulations of Spiking Neural P Systems},

   author={CABARLE, F.G.C. and ADORNA, H.N. and MART{‘I}NEZ-DEL-AMOR, M.A. and P{‘E}REZ-JIM{‘E}NEZ, M.J.},

   journal={SCIENCE AND TECHNOLOGY},

   volume={15},

   number={1},

   pages={5–20},

   year={2012}

}

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In this work we present further extensions and improvements of a Spiking Neural P system (for short, SNP systems) simulator on graphics processing units (for short, GPUs). Using previous results on representing SNP system computations using linear algebra, we analyze and implement a computation simulation algorithm on the GPU. A two-level parallelism is introduced for the computation simulations. We also present a set of benchmark SNP systems to stress test the simulation and show the increased performance obtained using GPUs over conventional CPUs. For a 16 neuron benchmark SNP system with 65536 nondeterministic rule selection choices, we report a 2.31 speedup of the GPU-based simulations over CPU-based simulations.
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