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. 2015 Jan 20;16(1):6. doi: 10.1186/s13059-014-0577-x

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© Kelly et al.; licensee BioMed Central. 2015

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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Churchill optimizes load balancing, resulting in improved resource utilization and faster run times. Three different strategies for parallelization of whole genome sequencing secondary data analysis were compared: balanced (utilized by Churchill), chromosomal (utilized by HugeSeq) and scatter-gather (utilized by GATK-Queue). The resource utilization, timing and scalability of the three pipelines were assessed using sequence data for a single human genome sequence dataset (30× coverage). (A) CPU utilization was monitored throughout the analysis process and demonstrated that Churchill improved resource utilization (92%) when compared with HugeSeq (46%) and GATK-Queue (30%). (B) Analysis timing metrics generated with 8 to 48 cores demonstrated that Churchill (green) is twice as fast as HugeSeq (red), four times faster than GATK-Queue (blue), and 10 times faster than a naïve serial implementation (yellow) with in-built multithreading enabled. (C) Churchill scales much better than the alternatives; the speed differential between Churchill and alternatives increases as more cores in a given compute node are used.