@article {861,
	title = {Parallel Simulation of Probabilistic P Systems on Multicore Platforms},
	journal = {Tenth Brainstorming Week on Membrane Computing},
	volume = {II},
	year = {2012},
	month = {02/2012},
	pages = {17-26},
	publisher = {F{\'e}nix Editora},
	address = {Seville, Spain},
	abstract = {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{\textquoteright} 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.},
	keywords = {Multicore Computing, OpenMP, P systems, Parallel Simulation, Population Dynamics},
	url = {http://www.gcn.us.es/10BWMC/10BWMCvolII/papers/parallel-dcba.pdf},
	author = {Miguel A. Mart{\'\i}nez-del-Amor and Ian Karlin and Rune E. Jensen and Mario J. P{\'e}rez-Jim{\'e}nez and Anne C. Elster}
}
