Research Group on Natural Computing

We present in this paper our work regarding simulating a type of P sys-
tem 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 scientic 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 N - 1 are discussed, as well as recommendations for future work.