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Parallel computing in bioinformatics: a view from high-performance, heterogeneous, and cloud computing

  • Miguel A. Vega-RodríguezEmail author
  • Sergio Santander-Jiménez
Article
  • 27 Downloads

Abstract

Bioinformatics allows and encourages the application of many different parallel computing approaches. This special issue brings together high-quality state-of-the-art contributions about parallel computing in bioinformatics, from different points of view or perspectives, that is, from high-performance, heterogeneous, and cloud computing. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2018 (6th International Workshop on Parallelism in Bioinformatics, and part of EuroMPI 2018). The domains and topics covered in these five papers are timely and important, and the authors have done an excellent job of presenting the material.

Keywords

Parallel computing Bioinformatics High-performance computing Heterogeneous computing Cloud computing 

Notes

Acknowledgements

This work was partially funded by the AEI (State Research Agency, Spain) and the ERDF (European Regional Development Fund, European Union), under the Contract TIN2016-76259-P (PROTEIN project), as well as Portuguese national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) with reference UID/CEC/50021/2019. Sergio Santander-Jiménez is supported by the Post-Doctoral Fellowship from FCT under Grant SFRH/BPD/119220/2016.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    PBio: 6th International Workshop on Parallelism in Bioinformatics (2018). http://arco.unex.es/mavega/pbio/2018/. Accessed 26 May 2019
  2. 2.
    Vitali E, Gadioli D, Palermo G, Beccari A, Cavazzoni C, Silvano C (2019) Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes. J Supercomput.  https://doi.org/10.1007/s11227-019-02875-w Google Scholar
  3. 3.
    Escobar JJ, Ortega J, Díaz AF, González J, Damas M (2019) Time-energy analysis of multi-level parallelism in heterogeneous clusters: the case of EEG classification in BCI tasks. J Supercomput.  https://doi.org/10.1007/s11227-019-02908-4 Google Scholar
  4. 4.
    Daberdaku S (2019) Accelerating the computation of triangulated molecular surfaces with OpenMP. J Supercomput.  https://doi.org/10.1007/s11227-019-02803-y Google Scholar
  5. 5.
    González P, Argüeso-Alejandro P, Penas DR, Pardo XC, Saez-Rodriguez J, Banga JR, Doallo R (2019) Hybrid parallel multimethod hyperheuristic for mixed-integer dynamic optimization problems in computational systems biology. J Supercomput.  https://doi.org/10.1007/s11227-019-02871-0 Google Scholar
  6. 6.
    Ferretti M, Santangelo L (2019) Optimized cloud-based scheduling for protein secondary structure analysis. J Supercomput.  https://doi.org/10.1007/s11227-019-02859-w Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Miguel A. Vega-Rodríguez
    • 1
    Email author
  • Sergio Santander-Jiménez
    • 2
  1. 1.Department of Computer and Communications Technologies, Escuela PolitecnicaUniversity of ExtremaduraCáceresSpain
  2. 2.INESC-ID, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal

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