Abstract
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.
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Acknowledgements
We thank Daniel Puleri, Simbarashe Chidyagwai, Sayan Roychowdhury, and Raveena Kothare for fruitful discussions. This work used an Extreme Science and Engineering Discovery Environment (XSEDE) allocation, which is supported by National Science Foundation grant number ACI-1548562. This work used Stampede2 at TACC through allocation TG-MDE210001. The research of Cyrus Tanade was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. NSF GRFP DGE 1644868. The work of Amanda Randles was supported by the National Institutes of Health under award number U01CA253511. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the NSF.
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Tanade, C., Putney, S., Randles, A. (2022). Developing a Scalable Cellular Automaton Model of 3D Tumor Growth. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_1
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