Abstract
The paper arguments are on enabling methodologies for the design of a fully parallel, online, interactive tool aiming to support the bioinformatics scientists .In particular, the features of these methodologies, supported by the FastFlow parallel programming framework, are shown on a simulation tool to perform the modeling, the tuning, and the sensitivity analysis of stochastic biological models. A stochastic simulation needs thousands of independent simulation trajectories turning into big data that should be analysed by statistic and data mining tools. In the considered approach the two stages are pipelined in such a way that the simulation stage streams out the partial results of all simulation trajectories to the analysis stage that immediately produces a partial result. The simulation-analysis workflow is validated for performance and effectiveness of the online analysis in capturing biological systems behavior on a multicore platform and representative proof-of-concept biological systems. The exploited methodologies include pattern-based parallel programming and data streaming that provide key features to the software designers such as performance portability and efficient in-memory (big) data management and movement. Two paradigmatic classes of biological systems exhibiting multistable and oscillatory behavior are used as a testbed.
Highlights
This paper presents a critical rethinking of the parallelization of biological computational tools in the light of multicore platforms, which nowadays equip all scientific laboratories.We will focus on the features that are required to derive an efficient simulator of stochastic processes considering, in particular, performance and easy engineering
This latter aspect will be of crucial importance for generation of biological tools that will be largely designed by bioinformatics scientists, who are likely to be more interested in the accurate modeling of natural phenomena rather than on the synchronisation protocols required to build efficient tools on multicore platforms
In this paper we focused on a methodology to accelerate the simulation of stochastic models and analysis of simulation results on multicore platforms
Summary
We will focus on the features that are required to derive an efficient simulator of stochastic processes considering, in particular, performance and easy engineering This latter aspect will be of crucial importance for generation of biological tools that will be largely designed by bioinformatics scientists, who are likely to be more interested in the accurate modeling of natural phenomena rather than on the synchronisation protocols required to build efficient tools on multicore platforms. The stochastic simulation of biological systems is an increasingly popular technique in bioinformatics as either an alternative or a complementary tool to ordinary differential equations (ODEs). In particular it allows studying rare or divergent behaviors, spikes, and discriminate families of possible behaviors that
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