Chronological Overview 
 Type-Hierarchical Overview 
Formal Methods in Computing
(Most of the papers antecedent to 1995
are not included in the list)
FRAMES  NO FRAME 

cwcsim:ff:multicore:biomed:14 (Article)
Author(s) Marco Aldinucci, Cristina Calcagno, Mario Coppo, Ferruccio Damiani, Maurizio Drocco, Eva Sciacca, Salvatore Spinella, Massimo Torquati and Angelo Troina
Title« On designing multicore-aware simulators for systems biology endowed with on-line statistics »
JournalBioMed Research International
Year2014
URLhttp://downloads.hindawi.com/journals/bmri/2014/207041.pdf
Abstract & Keywords
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.

Keywords: fastflow,bioinformatics

BibTeX code

@article{cwcsim:ff:multicore:biomed:14,
  keywords = {fastflow,bioinformatics},
  url = {http://downloads.hindawi.com/journals/bmri/2014/207041.pdf},
  title = {On designing multicore-aware simulators for systems biology endowed
           with on-line statistics},
  author = {Marco Aldinucci and Cristina Calcagno and Mario Coppo and Ferruccio
            Damiani and Maurizio Drocco and Eva Sciacca and Salvatore Spinella
            and Massimo Torquati and Angelo Troina},
  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.},
  year = {2014},
  journal = {BioMed Research International},
}


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