Evaluation of spatial trees for simulation of biological tissue

TL;DR

This paper evaluates how different spatial tree structures impact the performance of biological tissue simulations, aiming to identify the most efficient options for large agent-based models.

Contribution

It systematically compares various spatial tree families within a biological tissue simulation framework to determine their effects on performance and accuracy.

Findings

Spatial tree choice significantly affects simulation efficiency.

Some tree structures outperform others in biological tissue modeling.

The study provides guidelines for selecting optimal spatial hierarchies.

Abstract

Spatial organization is a core challenge for all large agent-based models with local interactions. In biological tissue models, spatial search and reinsertion are frequently reported as the most expensive steps of the simulation. One of the main methods utilized in order to maintain both favorable algorithmic complexity and accuracy is spatial hierarchies. In this paper, we seek to clarify to which extent the choice of spatial tree affects performance, and also to identify which spatial tree families are optimal for such scenarios. We make use of a prototype of the new BioDynaMo tissue simulator for evaluating performances as well as for the implementation of the characteristics of several different trees.