@misc{10481/88754,
year = {2019},
month = {1},
url = {https://hdl.handle.net/10481/88754},
abstract = {In silico” experimentation allows us to simulate the effect of different therapies by handling model parameters. Although the computational simulation of tumors is currently a well-known technique, it is however possible to contribute to its improvement by parallelizing simulations on computer systems of many and multi-cores. This work presents a proposal to parallelize a tumor growth simulation that is based on cel-lular automata by partitioning of the data domain and by dynamic load balancing. The initial results of this new approach show that it is pos-sible to successfully accelerate the calculations of a known algorithm for tumor growth.},
publisher = {Springer},
keywords = {Cellular automaton},
keywords = {High performance computing},
keywords = {Mathematical oncology},
keywords = {Tumoral growth simulation Parallel programming},
keywords = {Speedup},
title = {Parallel cellular automaton tumor growth model},
doi = {https://doi.org/10.1007/978-3-319-98702-6_21},
author = {Salguero Hidalgo, Alberto Gabriel and Capel Tuñón, Manuel Isidoro and Tomeu Hardasmal, Antonio J.},
}