# cellGPU: massively parallel simulations of dynamic vertex models

## TL;DR

cellGPU enables massively parallel GPU-based simulations of vertex models, overcoming previous computational limitations and allowing for large-scale, detailed tissue modeling in biological research.

## Contribution

This work introduces cellGPU, a GPU-accelerated framework for simulating vertex models, significantly improving computational efficiency and scalability over traditional CPU methods.

## Key findings

- cellGPU achieves substantial speedups on various graphics hardware.
- Parallel implementation of topological updates enhances simulation performance.
- Enables large-scale, detailed tissue simulations previously infeasible with CPU-only methods.

## Abstract

Vertex models represent confluent tissue by polygonal or polyhedral tilings of space, with the individual cell interacting via force laws that depend on both the geometry of the cells and the topology of the tessellation. This dependence on the connectivity of the cellular network introduces several complications to performing molecular-dynamics-like simulations of vertex models, and in particular makes parallelizing the simulations difficult. cellGPU addresses this difficulty and lays the foundation for massively parallelized, GPU-based simulations of these models. This article discusses its implementation for a pair of two-dimensional models, and compares the typical performance that can be expected between running cellGPU entirely on the CPU versus its performance when running on a range of commercial and server-grade graphics cards. By implementing the calculation of topological changes and forces on cells in a highly parallelizable fashion, cellGPU enables researchers to simulate time- and length-scales previously inaccessible via existing single-threaded CPU implementations.