Affiliations: Research Group on Natural Computing, Universidad de Sevilla, Seville, Spain. [email protected] | University of Minnesota, Minneapolis-St. Paul, United States. [email protected] | Research Group on Natural Computing, Universidad de Sevilla, Seville, Spain. {lfmaciasr,lvalencia,ariscosn,marper}@us.es
Note: [] M.A. Martínez-del-Amor acknowledges the support of NVIDIA for the CUDA Research Center program at the University of Seville, and of the 3rd postdoctoral phase of the PIF program associated with the project of excellence of the “Junta de Andalucía” under grant P08-TIC04200, co-financed by FEDER funds. Address for correspondence: Dept. Computer Science and Artificial Intelligence, E.T.S Ingeniería Informática, Avda. Reina Mercedes S/N, 41012 Sevilla, Spain
Note: [] The authors acknowledge the support of the Project TIN2012-37434 of the Ministerio de Economía y Competitividad of Spain, co-financed by FEDER funds.
Abstract: P systems have been proven to be useful as modeling tools in many fields, such as Systems Biology and Ecological Modeling. For such applications, the acceleration of P system simulation is often desired, given the computational needs derived from these kinds of models. One promising solution is to implement the inherent parallelism of P systems on platforms with parallel architectures. In this respect, GPU computing proved to be an alternative to more classic approaches in Parallel Computing. It provides a low cost, and a manycore platform with a high level of parallelism. The GPU has been already employed to speedup the simulation of P systems. In this paper, we look over the available parallel P systems simulators on the GPU, with special emphasis on those included in the PMCGPU project, and analyze some useful guidelines for future implementations and developments.
Keywords: Membrane Computing, P systems, Parallel Computing, GPU computing, CUDA
