Simulating Spiking Neural P Systems Without Delays Using GPUs

Abstract

Summary. 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 N -{1} are discussed, as well as recommendations for future work.

A Spiking Neural P System Simulator Based on CUDA

“…The simulation comparison for Π 1 run in both simulators is shown in Figure 5. From Figure 5, the five C 0 values used in the simulation comparison are: (2,1,2), (3,1,3), (4,1,4), (6,1,6), and (9,1,9). The horizontal axis in Figure 5 are the C 0 values, while the vertical axis is the running time (in seconds) of the simulation on a given C 0 .…”

“…Previously, SNP systems have been faithfully simulated in GPUs using their matrix representation [1]. This simulator combined both the object oriented programming language (OOPL) Python (CPU part) and CUDA/C (GPU part) codes, and so it has been improved [2], in performance, by using the PyCUDA [13] library.…”

Spiking Neural P Systems - A Quick Survey and Some Research Topics

“…We only mention here the papers [6], [15], [25], [26], where some applications are described, as well as the papers [1], [13], [27], where simulators (sometimes, called implementations) are reported. General information about the P-lingua programming language (also a platform for executing P systems) can be found at the P-lingua website, http://www.p-lingua.org.…”

An interactive timeline of simulators in membrane computing

“…Among other works detailed there, we can recall the line followed along PMCGPU project [163]. Please look for the details about parallel simulators in [9], including among others [118][119][120][121][122][123][124][125][126][127][128]. Some more recent results appear after that surveys, including [10,129].…”