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. 2020 Aug 5;21:343. doi: 10.1186/s12859-020-03697-x

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 3 — Performance comparison of ABEA on CPU vs GPU over a wide range of systems. Runtime for C-opti (left bars) and the G-opti (right bars). Runtime for the GPU has been broken down into: compute kernel time; different overheads (memory copying to/from the GPU, data serialisation time), and the extra CPU time due to CPU processing of the reads. The compute kernel time includes the sum of time for all GPU kernels. The extra CPU time is the additional time spent by the CPU to process very long reads and ultra long reads (see Supplementary materials) assigned to the CPU (excluding the processing time that overlaps with the GPU execution, i.e. only the extra time which the GPU has to wait after the execution is included)