Developing a Scalable Cellular Automaton Model of 3D Tumor Growth

Parallel three-dimensional (3D) cellular automaton models of tumor growth can efficiently model tumor morphology over many length and time scales. Here, we extended an existing two-dimensional (2D) model of tumor growth to study how tumor morphology could change over time and verified the 3D model with the initial 2D model on a per-slice level. However, increasing the dimensionality of the model imposes constraints on memory and time-to-solution that could quickly become intractable when simulating long temporal durations. Parallelizing such models would enable larger tumors to be investigated and also pave the way for coupling with treatment models. We parallelized the 3D growth model using N-body and lattice halo exchange schemes and further optimized the implementation to adaptively exchange information based on the state of cell expansion. We demonstrated a factor of 20x speedup compared to the serial model when running on 340 cores of Stampede2’s Knight’s Landing compute nodes. This proof-of-concept study highlighted that parallel 3D models could enable the exploration of large problem and parameter spaces at tractable run times.

Duke Scholars

Author Amanda Randles Biomedical Engineering