Agent-based simulation is an indispensable paradigm for studying complex systems. These systems can comprise billions of agents, requiring the computing resources of multiple servers to simulate. Unfortunately, the state-of-the-art platform, BioDynaMo, does not scale out across servers due to its shared-memory-based implementation. To overcome this key limitation, we introduce TeraAgent, a distributed agent-based simulation engine. A critical challenge in distributed execution is the exchange of agent information across servers, which we identify as a major performance bottleneck. We propose two solutions: 1) a tailored serialization mechanism that allows agents to be accessed and mutated directly from the receive buffer, and 2) leveraging the iterative nature of agent-based simulations to reduce data transfer with delta encoding. Built on our solutions, TeraAgent enables extreme-scale simulations with half a trillion agents (an 84x improvement), reduces time-to-result with additional compute nodes, improves interoperability with third-party tools, and provides users with more hardware flexibility.
@article{arxiv.2509.24063,
title = {TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents},
author = {Lukas Breitwieser and Ahmad Hesam and Abdullah Giray Yağlıkçı and Mohammad Sadrosadati and Fons Rademakers and Onur Mutlu},
journal= {arXiv preprint arXiv:2509.24063},
year = {2025}
}