Abstract
Existing computational models of cancer evolution mostly represent very general approaches for studying tumor dynamics in a homogeneous tissue. Here we present two very different cancer models: the heterogeneous continuous/discrete and purely discrete one, focusing on a specific cancer type – melanoma. This tumor proliferates in a complicated heterogeneous environment of the human skin. The results from simulations obtained for the two models are confronted in the context of their possible integration into a single multi-scale system. We demonstrate that the interaction between the tissue – represented by both the concentration fields (the continuous model) and the particles (the discrete model) – and the discrete network of blood vessels is the crucial component, which can increase the simulation time even one order of magnitude. To compensate this time lag, we developed GPU/CUDA implementations of the two melanoma models. Herein, we demonstrate that the continuous/discrete model, run on a multi-GPU cluster, almost fifteen times outperforms its multi-threaded CPU implementation.
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Acknowledgement
The work has been supported by the Polish National Science Center (NCN) project 2013/10/M/ST6/00531 and in part by PL-Grid Infrastructure.
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Dzwinel, W., Kłusek, A., Wcisło, R., Panuszewska, M., Topa, P. (2018). Continuous and Discrete Models of Melanoma Progression Simulated in Multi-GPU Environment. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10777. Springer, Cham. https://doi.org/10.1007/978-3-319-78024-5_44
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DOI: https://doi.org/10.1007/978-3-319-78024-5_44
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