<?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%">Ignacio Pérez-Hurtado</style></author><author><style face="normal" font="default" size="100%">Miguel A. Martínez-del-Amor</style></author><author><style face="normal" font="default" size="100%">G. Zhang</style></author><author><style face="normal" font="default" size="100%">F. Neri</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning</style></title><secondary-title><style face="normal" font="default" size="100%">Integrated Computer-Aided Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CUDA</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">OpenMP</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimal Motion Planning</style></keyword><keyword><style  face="normal" font="default" size="100%">Rapidly-exploring Random Tree</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.3233/ICA-190616</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">121-138</style></pages><abstract><style face="normal" font="default" size="100%">In recent years, incremental sampling-based motion planning algorithms have been widely used to solve robot motion planning problems in high-dimensional configuration spaces. In particular, the Rapidly-exploring Random Tree (RRT) algorithm and its asymptotically-optimal counterpart called RRT* are popular algorithms used in real-life applications due to its desirable properties. Such algorithms are inherently iterative, but certain modules such as the collision-checking procedure can be parallelized providing significant speedup with respect to sequential implementations. In this paper, the RRT and RRT* algorithms have been adapted to a bioinspired computational framework called Membrane Computing whose models of computation, a.k.a. P systems, run in a non-deterministic and massively parallel way. A large number of robotic applications are currently using a variant of P systems called Enzymatic Numerical P systems (ENPS) for reactive controlling, but there is a lack of solutions for motion planning in the framework. The novel models in this work have been designed using the ENPS framework. In order to test and validate the ENPS models for RRT and RRT*, we present two ad-hoc implementations able to emulate the computation of the models using OpenMP and CUDA. Finally, we show the speedup of our solutions with respect to sequential baseline implementations. The results show a speedup up to 6x using OpenMP with 8 cores against the sequential implementation and up to 24x using CUDA against the best multi-threading configuration.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><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>