The stochastic modelling of biological systems is informative and often very adequate, but it may easily be more expensive than other modelling approaches, such as differential equations. We present StochKit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations. StochKit-FF is based on the FastFlow programming toolkit for multicores and on the novel concept of selective memory. We experiment StochKit-FF on a model of HIV infection dynamics, with the aim of extracting information from efficiently run experiments, here in terms of average and variance and, on a longer term, of more structured data.

StochKit-FF: Efficient systems biology on multicore architectures

ALDINUCCI, MARCO;Andrea Bracciali;
2011-01-01

Abstract

The stochastic modelling of biological systems is informative and often very adequate, but it may easily be more expensive than other modelling approaches, such as differential equations. We present StochKit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations. StochKit-FF is based on the FastFlow programming toolkit for multicores and on the novel concept of selective memory. We experiment StochKit-FF on a model of HIV infection dynamics, with the aim of extracting information from efficiently run experiments, here in terms of average and variance and, on a longer term, of more structured data.
2011
Parallel Processing Workshops, Euro-Par 2010: HeteroPar 2010, HPPC 2010, HiBB 2010, CoreGrid 2010, UCHPC 2010, HPCF 2010, PROPER 2010, CCPI 2010, VHPC 2010, Revised Selected Papers
Springer-Verlag
6586
167
175
9783642218774
http://www.springerlink.com/content/5214qwp01503271q/
Stochastic biological models; simulation; multicore
Marco Aldinucci; Andrea Bracciali; Pietro Liò; Anil Sorathiya; Massimo Torquati
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/89775
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