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. 2015 Jun 4;4:26. doi: 10.1186/s13742-015-0058-5

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© Siretskiy et al.; licensee BioMed Central. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Ratios of the short reads alignment time to the communication time. All of the runs use Dataset S as an input. Two HPC scenarios are shown as ‘HPC SLURM’ and ‘HPC random’, which correspond to standard SLURM behavior and a modified behavior that allocates nodes from random racks. Linear fit was done with the least-squares method. One can see two defined linear regions for the HPC approach with very different tangents, which are caused by the network saturation. For the HPC platform, as the number of nodes increases a greater amount of time is spent on communication than on mapping. The Hadoop approach reveals better scaling properties.