Research Group on Natural Computing

GPUs (Graphics Processing Unit) have been con-
solidated as a massively data-parallel coprocessor to
develop many general purpose computations, and en-
able developers to utilize several levels of parallelism
to obtain better performance of their applications.
The massively parallel nature of certain computa-
tions leads to use GPUs as an underlying architec-
ture, becoming a good alternative to other paral-
lel approaches. P systems or membrane systems
are theoretical devices inspired in the way that liv-
ing cells work, providing computational models and
a high level computational modeling framework for
biological systems. They are massively parallel dis-
tributed, and non-deterministic systems. In this pa-
per, we evaluate the GPU as the underlying archi-
tecture to simulate the class of recognizer P systems
with active membranes. We analyze the performance
of three simulators implemented on CPU, CPU-GPU
and GPU respectively. We compare them using a pre-
sented P system as a benchmark, showing that the
GPU is better suited than the CPU to simulate those
P systems due to its massively parallel nature.