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. 2018 Oct 15;19(Suppl 10):353. doi: 10.1186/s12859-018-2301-4

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© The Author(s) 2018

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. 2 — CPU/GPU schema of the PARCOSNET parallelization. Multiple CPU threads are launched in parallel each one solving the AFP problem for a given class/protein function. The GPU thread blocks, each composed of several CUDA threads, first solve the coloring problem for the graph and then concurrently process all neurons of a given color, for all colors in sequence. A further fine-grained level of parallelism is finally introduced by assigning to each neuron a thread block to perform the neuron level local computations