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Optimized cloud-based scheduling for protein secondary structure analysis

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A Correction to this article was published on 17 June 2019

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Abstract

In the domain of proteomics, an in-depth analysis of the 3D structure of a protein is of paramount importance for many biological studies and applications. At the secondary level, protein structure can be described in terms of motifs, recurrent patterns of smaller biological structures called secondary structure elements. In this paper, the focus is on the identification of geometrical motifs in different proteins using the Cross Motif Search (CMS) algorithm. Such task, due to the high computational cost of CMS with respect to traditional alignment algorithms, is very demanding, and thus, parallel processing is mandatory. In previous papers, CMS parallelization has been already studied from the HPC standpoint. Since cloud computing is emerging as an alternative to on-premise HPC systems, it is worthwhile examining the feasibility and possible advantages in terms of both performance and costs, of migrating to a cloud implementation. This paper is an extension of a preliminary work carried out on the cloud parallelization of CMS. The paper has two main contributions. First of all, an analytic model of the communication pattern of CMS is described, in order to get insights on the performance of the application when executed on a cloud infrastructure. Secondly, an optimized “location-aware” scheduling policy to assign workload to the application workers is introduced, in order to minimize internode communication in a cloud setting. Experiments are presented in order to validate the newly introduced scheduling policy and assess the performance of the cloud implementation of CMS. The results presented in this paper are general, in the sense that they can be applied to any other algorithm with a communication pattern similar to the one of the target applications.

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  • 17 June 2019

    Mirto Musci was not listed among the authors. The original article has been corrected.

Notes

  1. https://vision.unipv.it/bioinformatics/contents/tools.php.

  2. Note that 1k32 and 1bgl share identical chains; using a priori biological information would greatly reduce the computational time. As stated before, however, CMS only focuses on geometrical information.

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Authors and Affiliations

  1. Department of Electrical, Computer and Biological Engineering, University of Pavia, Pavia, Italy

    Marco Ferretti, Luigi Santangelo & Mirto Musci

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  1. Marco Ferretti
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  2. Luigi Santangelo
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  3. Mirto Musci
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Correspondence to Luigi Santangelo.

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The original version of this article was revised: Mirto Musci was not listed among the authors.

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Ferretti, M., Santangelo, L. & Musci, M. Optimized cloud-based scheduling for protein secondary structure analysis. J Supercomput 75, 3499–3520 (2019). https://doi.org/10.1007/s11227-019-02859-w

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