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.

On Parallelizing On-Line Statistics for Stochastic Biological Simulations

ALDINUCCI, MARCO;COPPO, Mario;DAMIANI, Ferruccio;DROCCO, MAURIZIO;SCIACCA, EVA;SPINELLA, SALVATORE;TROINA, ANGELO
2012-01-01

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.
Euro-Par 2011: Parallel Processing Workshops, Revised Selected Papers
Springer-Verlag
7156
3
12
multi-core; parallel simulation; stochastic simulation; on-line clustering
Marco Aldinucci; Mario Coppo; Ferruccio Damiani; Maurizio Drocco; Eva Sciacca; Salvatore Spinella; Massimo Torquati; Angelo Troina
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/116188
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