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 extended with user-defined on-line data filtering and mining functions. The simulator and the on-line analysis were validated on large multi-core platforms and representative proof-of-concept biological systems.
On Parallelizing On-Line Statistics for Stochastic Biological Simulations
TORQUATI, MASSIMO;
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 extended with user-defined on-line data filtering and mining functions. The simulator and the on-line analysis were validated on large multi-core platforms and representative proof-of-concept biological systems.File in questo prodotto:
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