High-Performance and Scalable Agent-Based Simulation with BioDynaMo

TL;DR

This paper introduces BioDynaMo, a high-performance, scalable agent-based simulation engine that significantly outperforms existing platforms, enabling large-scale biological and social simulations on a single server.

Contribution

The paper presents novel optimization techniques for parallelization, memory access, and collision force calculation to enhance agent-based simulation performance.

Findings

Order of magnitude faster than Biocellion

Three orders of magnitude speedup over Cortex3D and NetLogo

Able to simulate 1.72 billion agents on a single server

Abstract

Agent-based modeling plays an essential role in gaining insights into biology, sociology, economics, and other fields. However, many existing agent-based simulation platforms are not suitable for large-scale studies due to the low performance of the underlying simulation engines. To overcome this limitation, we present a novel high-performance simulation engine. We identify three key challenges for which we present the following solutions. First, to maximize parallelization, we present an optimized grid to search for neighbors and parallelize the merging of thread-local results. Second, we reduce the memory access latency with a NUMA-aware agent iterator, agent sorting with a space-filling curve, and a custom heap memory allocator. Third, we present a mechanism to omit the collision force calculation under certain conditions. Our evaluation shows an order of magnitude improvement over Biocellion, three orders of magnitude speedup over Cortex3D and NetLogo, and the ability to simulate 1.72 billion agents on a single server. Supplementary Materials, including instructions to reproduce the results, are available at: https://doi.org/10.5281/zenodo.6463816