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. 2020 Sep 29;21:425. doi: 10.1186/s12859-020-03743-8

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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, visit http://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 — Distribution of execution time between GPU-related operations in CUDA implementations for 3-element normalizers. Tables below the graphs indicate percentage of the total execution time (CPU+GPU) dedicated to operations on GPU. Test 1 on platform 1. cudaMalloc: memory allocation on GPU, memcpyHtD: copying data from RAM to GPU memory, memcpyDtH: copying data from GPU memory to RAM