This work concerns a general technique to enrich parallel version of stochastic simulators for biological systems with tools for on- line statistical analysis of the results. In particular, within the FastFlow parallel programming framework, we describe the methodology and the implementation of a parallel Monte Carlo simulation infrastructure ex- tended with user-defined on-line data filtering and mining functions. The simulator and the on-line analysis were validated on large multi-core plat- forms and representative proof-of-concept biological systems.
Marco Aldinucci, Mario Coppo, Ferruccio Damiani, Maurizio Drocco, Eva Sciacca, Salvatore Spinella, et al. (2012). On Parallelizing On-Line Statistics for Stochastic Biological Simulations. BERLIN HEIDELBERG : Springer-Verlag [10.1007/978-3-642-29740-3_2].
On Parallelizing On-Line Statistics for Stochastic Biological Simulations
TROINA, ANGELO
2012
Abstract
This work concerns a general technique to enrich parallel version of stochastic simulators for biological systems with tools for on- line statistical analysis of the results. In particular, within the FastFlow parallel programming framework, we describe the methodology and the implementation of a parallel Monte Carlo simulation infrastructure ex- tended with user-defined on-line data filtering and mining functions. The simulator and the on-line analysis were validated on large multi-core plat- forms and representative proof-of-concept biological systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
