%0 Generic
%D 2012
%T Parallel Simulation of Probabilistic P Systems on Multicore Platforms
%A Miguel A. Martínez-del-Amor
%A Ian Karlin
%A Rune E. Jensen
%A Mario J. Pérez-Jiménez
%A Anne C. Elster
%C Seville, Spain
%I Fénix Editora
%K Multicore Computing
%K OpenMP
%K P systems
%K Parallel Simulation
%K Population Dynamics
%P 17-26
%S Proceedings of the Tenth Brainstorming Week on Membrane Computing
%U http://www.gcn.us.es/10BWMC/10BWMCvolII/papers/parallel-dcba.pdf
%V II
%X Ecologists need to model ecosystems to predict how they will evolve over time. Since ecosystems are non-deterministic phenomena, they must express the likeli- hood of events occurring, and measure the uncertainty of their models' predictions. One method well suited to these demands is Population Dynamic P systems (PDP systems, in short), which is a formal framework based on multienvironment probabilistic P systems. In this paper, we show how to parallelize a Population Dynamics P system simulator, used to model biological systems, on multi-core processors, such as the Intel i5 Nehalem and i7 Sandy Bridge. A comparison of three dierent techniques, discuss their strengths and weaknesses, and evaluate their performance on two generations of Intel processors with large memory sub-system dierences is presented. We show that P systems are memory bound computations and future performance optimization eorts should focus on memory trac reductions. We achieve runtime gains of up to 2.5x by using all the cores of a single socket 4-core Intel i7 built on the Sandy Bridge architecture. From our analysis of these results we identify further ways to improve the runtime of our simulator.
%8 02/2012