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Optimized cloud-based scheduling for protein secondary structure analysis

  • Marco Ferretti
  • Luigi SantangeloEmail author
  • Mirto Musci
Article
  • 44 Downloads

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.

Keywords

Proteomics Cloud computing HPC Cross Motif Search CINECA Google Cloud pLogP 

Notes

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Copyright information

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

Authors and Affiliations

  1. 1.Department of Electrical, Computer and Biological EngineeringUniversity of PaviaPaviaItaly

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