Abstract
Parallel 3D cellular automaton models of tumor growth can efficiently capture emergent morphology. We extended a 2D growth model to 3D to examine the influence of symmetric division in heterogeneous tumors on growth dynamics. As extending to 3D severely increased time-to-solution, we parallelized the model using N-body, lattice halo exchange, and adaptive communication schemes. Supplementing prior work from Tanade et al. (2022), we demonstrated over 55x speedup and evaluated performance on ≤30 nodes of Stampede2. This work established a framework to parametrically study 3D growth dynamics, and of the cancer phenotypes we studied, the parallel model better scaled when tumor boundaries were radially symmetric.
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