Adaptative parallel simulators for bioinspired computing models

TitleAdaptative parallel simulators for bioinspired computing models
Publication TypeJournal Papers
Year of Publication2020
AuthorsMartínez-del-Amor, M. A., Pérez-Hurtado I., Orellana-Martín D., & Pérez-Jiménez M. J.
Journal TitleFuture Generation Computer Systems

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.

Keywordsbio-inspired computing, GPU Computing, P systems, P-Lingua, parallel computing, Parallel simulators
Impact Factor

5.768 (2018)


Q1 - 8/105 - Computer Science, Theory & Methods

ISSN Number0167-739X