In this work a previously published bioinformatics pipeline was reimplemented across various computational platforms, and the performances of its steps evaluated. The tested environments were: (I) dedicated bioinformatics-specific server (II) low-power single node (III) HPC single node (IV) virtual machine. The pipeline was tested on a use case of the analysis of a single patient to assess single-use performances, using the same configuration of the pipeline to be able to perform meaningful comparison and search the optimal environment/hybrid system configuration for biomedical analysis. Performances were evaluated in terms of execution wall time, memory usage and energy consumption per patient. Our results show that, albeit slower, low power single nodes are comparable with other environments for most of the steps, but with an energy consumption two to four times lower. These results indicate that these environments are viable candidates for bioinformatics clusters where long term efficiency is a factor.

Cross-environment comparison of a bioinformatics pipeline: Perspectives for hybrid computations

Curti, Nico;Giampieri, Enrico;Ferraro, Andrea;Castellani, Gastone
2019

Abstract

In this work a previously published bioinformatics pipeline was reimplemented across various computational platforms, and the performances of its steps evaluated. The tested environments were: (I) dedicated bioinformatics-specific server (II) low-power single node (III) HPC single node (IV) virtual machine. The pipeline was tested on a use case of the analysis of a single patient to assess single-use performances, using the same configuration of the pipeline to be able to perform meaningful comparison and search the optimal environment/hybrid system configuration for biomedical analysis. Performances were evaluated in terms of execution wall time, memory usage and energy consumption per patient. Our results show that, albeit slower, low power single nodes are comparable with other environments for most of the steps, but with an energy consumption two to four times lower. These results indicate that these environments are viable candidates for bioinformatics clusters where long term efficiency is a factor.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
638
649
Curti, Nico*; Giampieri, Enrico; Ferraro, Andrea; Vistoli, Cristina; Ronchieri, Elisabetta; Cesini, Daniele; Martelli, Barbara; Doina, Cristina Duma; Castellani, Gastone
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/678120
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