<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miguel A. Martínez-del-Amor</style></author><author><style face="normal" font="default" size="100%">Ian Karlin</style></author><author><style face="normal" font="default" size="100%">Rune E. Jensen</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author><author><style face="normal" font="default" size="100%">Anne C. Elster</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parallel Simulation of Probabilistic P Systems on Multicore Platforms</style></title><secondary-title><style face="normal" font="default" size="100%">Tenth Brainstorming Week on Membrane Computing</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of the Tenth Brainstorming Week on Membrane Computing</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Multicore Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">OpenMP</style></keyword><keyword><style  face="normal" font="default" size="100%">P systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Parallel Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Population Dynamics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.gcn.us.es/10BWMC/10BWMCvolII/papers/parallel-dcba.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Fénix Editora</style></publisher><pub-location><style face="normal" font="default" size="100%">Seville, Spain</style></pub-location><volume><style face="normal" font="default" size="100%">II</style></volume><pages><style face="normal" font="default" size="100%">17-26</style></pages><abstract><style face="normal" font="default" size="100%">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 dierent techniques, discuss their strengths
and weaknesses, and evaluate their performance on two generations of Intel processors
with large memory sub-system dierences is presented. We show that P systems are
memory bound computations and future performance optimization eorts should focus
on memory trac 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.</style></abstract></record></records></xml>