<?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%">José M. Cecilia</style></author><author><style face="normal" font="default" size="100%">Ginés D. Guerrero</style></author><author><style face="normal" font="default" size="100%">José M. García</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%">Mario J. Pérez-Jiménez</style></author><author><style face="normal" font="default" size="100%">Manuel Ujaldón</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enhancing the Simulation of P Systems for the SAT Problem on GPUs</style></title><secondary-title><style face="normal" font="default" size="100%"> Symposium on Application Accelerators in High Performance Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">July 2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://saahpc.ncsa.illinois.edu/10/agenda.html</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Knoxville, USA</style></pub-location><abstract><style face="normal" font="default" size="100%">GPUs constitute nowadays a solid alternative for
high performance computing, and the advent of CUDA/OpenCL
allow programmers a friendly model to accelerate a broad range
of applications. The way GPUs exploit parallelism differ from
multi-core CPUs, which raises new challenges to take advantage
of its tremendous computing power. In this respect, P systems or
Membrane Systems provide a high-level computational modeling
framework that combines the structure and dynamic aspects
of biological systems while being inherently parallel and non-
deterministic. In this work, we implement on GPUs the simula-
tion for a solution provided by Membrane Computing to solve the
Satisfiability (SAT) problem. The overall speed up reaches 100x
versus a sequential CPU, with an additional 16x due to CUDA
optimizations. A promising scalability is also proven on more
sophisticated GPU clusters and/or demanding problem sizes.</style></abstract></record></records></xml>