A CPU-GPU Hybrid Boundary-Based Algorithm for Accelerating Cellular Potts Model Simulations.

The Cellular Potts Model (CPM) is a lattice-based computational framework for simulating tissue dynamics such as cell sorting, collective cell migration, and morphogenesis. While the model effectively captures essential biological properties, its high computational cost poses a significant challenge when simulating large-scale multicellular systems over extended simulation times. In our previous work, we proposed a boundary-based algorithm that reduces computation time by limiting copy attempts to cell boundaries and performing local energy calculations. In this paper, we further accelerate CPM simulations with a CPU-GPU hybrid boundary-based algorithm: local energy calculations are offloaded to the GPU to leverage its parallel processing capabilities, while the CPU continues to manage cell boundaries and connectivity checks. Benchmark results demonstrate a substantial reduction in computation time compared to the CPU-based boundary-based algorithm, enabling more efficient and scalable simulations of complex multicellular systems.