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GPU-powered Simulation Methodologies for Biological Systems

Daniela Besozzi, Giulio Caravagna, Paolo Cazzaniga, Marco Nobile, Dario Pescini, Alessandro Re
Universita degli Studi di Milano, Dipartimento di Informatica, Via Comelico 39, 20135 Milano, Italy
arXiv:1309.7695 [cs.CE], (30 Sep 2013)

@article{2013arXiv1309.7695B,

   author={Besozzi}, D. and {Caravagna}, G. and {Cazzaniga}, P. and {Nobile}, M. and {Pescini}, D. and {Re}, A.},

   title={"{GPU-powered Simulation Methodologies for Biological Systems}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1309.7695},

   primaryClass={"cs.CE"},

   keywords={Computer Science – Computational Engineering, Finance, and Science, Computer Science – Distributed, Parallel, and Cluster Computing},

   year={2013},

   month={sep},

   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1309.7695B},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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The study of biological systems witnessed a pervasive cross-fertilization between experimental investigation and computational methods. This gave rise to the development of new methodologies, able to tackle the complexity of biological systems in a quantitative manner. Computer algorithms allow to faithfully reproduce the dynamics of the corresponding biological system, and, at the price of a large number of simulations, it is possible to extensively investigate the system functioning across a wide spectrum of natural conditions. To enable multiple analysis in parallel, using cheap, diffused and highly efficient multi-core devices we developed GPU-powered simulation algorithms for stochastic, deterministic and hybrid modeling approaches, so that also users with no knowledge of GPUs hardware and programming can easily access the computing power of graphics engines.
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