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. 2016 Jun 7;5:26. doi: 10.1186/s13742-016-0132-7

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© Spjuth et al. 2016

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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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Fig. 3 — Average resource usage for the human whole genome sequencing pipeline at the National Genomics Infrastructure at SciLifeLab during the 6 month period May to October 2015. The pipeline consists of the GATK best practice variant calling workflow [33, 34] plus a number of quality control jobs. Each point in the figure is a job and the axes show the average number of CPUs and GiB RAM used by the corresponding job. The graph illustrates how this standard high-throughput production pipeline has a very clear resource usage pattern that does not achieve full CPU utilization on the 16 core nodes it runs on