@article {MARTINEZDELAMOR2020,
	title = {Adaptative parallel simulators for bioinspired computing models},
	journal = {Future Generation Computer Systems},
	volume = {107},
	year = {2020},
	pages = {469-484},
	abstract = {In the Membrane Computing area, P systems are unconventional devices of computation inspired by the structure and processes taking place in living cells. Main successful P system applications lie in computability and computational complexity theories, as well as in biological modelling. Given that models become too complex to deal with, simulators for P systems are essential tools and their efficiency is critical. In order to handle the diverse situations that may arise during the computation, these simulators have to take into account that worst-case scenarios can happen, even though they rarely occur. As a result, there is a significant loss of performance. In this paper, the concept of adaptative simulation for P systems is introduced to palliate this problem. This is achieved by passing high-level information provided directly by P system model designers to the simulator, helping it to better adapt to the target model. For this purpose, an existing simulator for an ecosystem modelling framework, named Population Dynamics P systems, is extended to include the information of modules, that are usually employed to define ecosystem models. Moreover, the standard description language for P systems, P-Lingua, has been re-engineered in its version 5. It now includes a new syntactical item, called feature, to express this kind of high-level semantic information. Experiments show that this simple adaptative simulator supporting modules as features doubles the performance when running on GPUs and on multicore processors.},
	keywords = {bio-inspired computing, GPU Computing, P systems, P-Lingua, parallel computing, Parallel simulators},
	issn = {0167-739X},
	doi = {https://doi.org/10.1016/j.future.2020.02.012},
	url = {http://www.sciencedirect.com/science/article/pii/S0167739X19308817},
	author = {Miguel A. Mart{\'\i}nez-del-Amor and Ignacio P{\'e}rez-Hurtado and David Orellana-Mart{\'\i}n and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {1813,
	title = {Simulating P Systems on GPU Devices: A Survey},
	journal = {Fundamenta Informaticae},
	volume = {136},
	year = {2015},
	month = {02/2015},
	pages = {269-284},
	publisher = {IOS Press},
	address = {Warsaw, Poland},
	keywords = {CUDA, GPU Computing, Membrane computing, P systems, parallel computing},
	issn = {0169-2968},
	doi = {10.3233/FI-2015-1157},
	url = {http://iospress.metapress.com/content/101684hh3785365u/},
	author = {Miguel A. Mart{\'\i}nez-del-Amor and Manuel Garc{\'\i}a-Quismondo and Luis F. Mac{\'\i}as-Ramos and Luis Valencia-Cabrera and Agust{\'\i}n Riscos-N{\'u}{\~n}ez and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {876,
	title = {Improving GPU Simulations of Spiking Neural P Systems},
	journal = {Romanian Journal of Information Science and Technology},
	volume = {15},
	year = {2012},
	month = {06/2012},
	pages = {5-20},
	publisher = {EDITURA ACADEMIEI ROM{\^A}NE},
	edition = {Bio-Inspired Computing {\textendash} Theory and Applications (BIC-TA) - Selected papers},
	address = {Bucure{\c s}ti, Rom{\^a}nia},
	abstract = {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 compu-
tation simulation algorithm on the GPU. A two-level parallelism is introduced
for the computation simulations. We also present a set of benchmark SNP sys-
tems 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.
},
	keywords = {CUDA, GPU Computing, Membrane computing, spiking neural network, spiking neural P systems},
	issn = {1453-8245},
	url = {http://www.imt.ro/romjist/Volum15/Number15_1/cuprins15_1.htm},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {860,
	title = {On the Simulations of Evolution-Communication P Systems with Energy without Antiport Rules for GPUs},
	journal = {Tenth Brainstorming Week on Membrane Computing},
	volume = {I},
	year = {2012},
	month = {02/2012},
	pages = {267-290},
	publisher = {F{\'e}nix Editora},
	address = {Seville, Spain},
	abstract = {In this report, we present our initial proposal on simulating computations on
a restricted variant of Evolution-Communication P system with energy (ECPe system)
which will then be implemented in Graphics Processing Units (GPUs). This ECPe sys-
tems variant prohibits the use of antiport rules for communication. Several possible levels
of parallelizations for simulating ECPe systems computations on GPUs are emphasized.
Our work is based on a localized matrix representation for the mentioned variant given
in a previous literature. Our proposal employs a methodology for forward computing also
discussed in the said literature.},
	keywords = {GPU Computing, Membrane computing, parallel computing},
	url = {http://www.gcn.us.es/10BWMC/10BWMCvolI/papers/ecpe-bwmc10.pdf},
	author = {Richelle A.B. Juayong and Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
@article {springerlink:10.1007/978-3-642-33636-2_15,
	title = {Population Dynamics P Systems on CUDA},
	journal = {Lecture Notes in Bioinformatics},
	volume = {7605},
	year = {2012},
	note = {10.1007/978-3-642-33636-2_15},
	pages = {247-266},
	publisher = {Springer Berlin / Heidelberg},
	edition = {Computational Methods in Systems Biology},
	abstract = {Population Dynamics P systems (PDP systems, in short) provide a new formal bio-inspired modeling framework, which has been successfully used by ecologists. These models are validated using software tools against actual measurements. The goal is to use P systems simulations to adopt a priori management strategies for real ecosystems. Software for PDP systems is still in an early stage. The simulation of PDP systems is both computationally and data intensive for large models. Therefore, the development of efficient simulators is needed for this field. In this paper, we introduce a novel simulator for PDP systems accelerated by the use of the computational power of GPUs. We discuss the implementation of each part of the simulator, and show how to achieve up to a 7x speedup on a NVIDA Tesla C1060 compared to an optimized multicore version on a Intel 4-core i5 Xeon for large systems. Other results and testing methodologies are also included.},
	keywords = {CUDA, Ecological Modeling, GPU Computing, Membrane computing, Parallel Simulation, Population Dynamics},
	isbn = {978-3-642-33635-5},
	doi = {10.1007/978-3-642-33636-2_15},
	url = {http://dx.doi.org/10.1007/978-3-642-33636-2_15},
	author = {Miguel A. Mart{\'\i}nez-del-Amor and Ignacio P{\'e}rez-Hurtado and Adolfo Gastalver-Rubio and Anne C. Elster and Mario J. P{\'e}rez-Jim{\'e}nez},
	editor = {Gilbert, David and Heiner, Monika}
}
@article {787,
	title = {A Spiking Neural P system simulator based on CUDA},
	journal = {12th International Conference on Membrane Computing (CMC12)},
	year = {2011},
	month = {08/2011},
	pages = {77-92},
	address = {Fontainebleau, France},
	abstract = {In this paper we present a Spiking Neural P system (SNP
system) simulator based on graphics processing units (GPUs). In particular
we implement the simulator using NVIDIA CUDA enabled GPUs.
The massively parallel architecture of current GPUs is very suitable for
the maximally parallel computations of SNP systems. We simulate a
wider variety of SNP systems, after presenting a previous work on SNP
system matrix representation which led to their simulation in GPUs, and
the simulation algorithm included here. Finally, we compare and present
the performance speedups of the CPU-GPU based simulator over the
CPU only simulator.},
	keywords = {CUDA, GPU Computing, Membrane computing, parallel computing, spiking neural P systems},
	url = {http://cmc12.lacl.fr/cmc12proceedings.pdf},
	author = {Francis Cabarle and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor}
}
