2018
DOI: 10.1177/1094342018816772
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Efficient model of tumor dynamics simulated in multi-GPU environment

Abstract: The application of computer simulation as a tool in predicting cancer dynamics (e.g. during anticancer therapy) requires tumor models, which are nontrivial and, simultaneously, not computationally demanding. To this end, both the level of details and computational efficiency of the model should be well balanced. The restrictions on computational time are forced by very demanding data assimilation process in the phase of parameters learning and their correction on the basis of incoming medical data. Herein we p… Show more

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Cited by 11 publications
(2 citation statements)
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References 39 publications
(65 reference statements)
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“…Importantly, the challenging demand on the computational cost can be met by properly using high-performance computing techniques involving parallelization to solve, select, or average the models in the digital twin, while accounting for data uncertainties, model inadequacies, and new data availability. The most common approaches are MPI (Message Passing Interface), which splits the computational tasks among several computers connected in a cluster (each one being a node); OpenMP (Open Multi-Processing) [239][240][241], which can further parallelize each task among the CPUs (Central Processing Units) on a node; and, most recently, solving the model using a GPU (Graphics Processing Unit) [240,242,243], which divides the tasks among the processing units present in video cards.…”
Section: Limitations Of Computational Techniquesmentioning
confidence: 99%
“…Importantly, the challenging demand on the computational cost can be met by properly using high-performance computing techniques involving parallelization to solve, select, or average the models in the digital twin, while accounting for data uncertainties, model inadequacies, and new data availability. The most common approaches are MPI (Message Passing Interface), which splits the computational tasks among several computers connected in a cluster (each one being a node); OpenMP (Open Multi-Processing) [239][240][241], which can further parallelize each task among the CPUs (Central Processing Units) on a node; and, most recently, solving the model using a GPU (Graphics Processing Unit) [240,242,243], which divides the tasks among the processing units present in video cards.…”
Section: Limitations Of Computational Techniquesmentioning
confidence: 99%
“…However, the connection coefficients seem untrained in comparison to non-biological application areas, and the coupling remains weak [156]. This modeling group is very active in melanoma, refines the model continuously, and also uses particle automata models to produce visually realistic models [157,158]. Taken together, while these models provide valuable insight into the vasculature, much work is needed to ensure adequate melanoma-specific parametrizations and validations.…”
Section: Models Of Melanoma-associated Vascularizationmentioning
confidence: 99%