cs.DCSep 28, 2025

TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents

Authors: Lukas Breitwieser, Ahmad Hesam, Abdullah Giray Yağlıkçı, Mohammad Sadrosadati, Fons Rademakers, Onur Mutlu

Organizations: ETH Zurich, Switzerland · Delft University of Technology, The Netherlands · CERN, Switzerland

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Abstract

Agent-based simulation is a method for studying highly complex systems in disciplines as diverse as biology, medicine, sociology, economics, finance, and more. In this method, an agent represents a small part of the system, such as a human cell, and, through interactions among many agents, the system's behavior emerges, like the growth of cancer in the human body. Real-world complex systems, such as a human organ, comprise billions of agents. However, the state-of-the-art agent-based simulation platform, BioDynaMo, does not support the distributed processing of a simulation across servers. This limitation restricts the scalability of agent counts to the computational resources available within a single server, hindering users from conducting larger and more meaningful simulations. In this paper, we introduce TeraAgent, a new distributed agent-based simulation engine. To overcome BioDynaMo's scalability limitation, TeraAgent enables efficient exchange of agent information across servers by implementing two solutions: 1) a tailored serialization mechanism that allows agents to be accessed and mutated directly at the receive buffer, and 2) delta encoding that significantly reduces data transfer across servers by leveraging the iterative nature of agent-based simulations. Built on our solutions, TeraAgent 1) enables extreme-scale simulations with half a trillion agents (292x that of BioDynaMo) on 84,096 CPU cores, 2) reduces time-to-result leveraging parallelism across compute nodes, 3) improves interoperability with third-party tools, and 4) provides users with more hardware flexibility. TeraAgent serializes agents with a median speedup of 110x (37x for deserialization) over the state-of-the-art ROOT I/O baseline, delivers 8x the agent update rate per CPU core of Biocellion, and improves in-situ visualization performance by 39x.

Figures & tables

Fig. 1 : Overview of TeraAgent’s distributed execution, simplified to two dimensions and two ranks. (A) Space partitioning and the neighbor search grid. (B) The four cross-rank operations: 1) aura update, 2) agent migration where the moving agent lands within the aura region, 3) agent migration where the moving agents land outside any locally known aura region, and 4) load balancing.
Fig. 2 : The TeraAgent-IO serialization mechanism. Numbered nodes in (A) are contiguous memory blocks and edges are pointers; the arcs in (C) are pointer fields repointed in place, with no further allocation.
Fig. 3 : TeraAgent’s delta encoding scheme. The reference is identical on the sender and the receiver. Rn\mathrm{R}n corresponds to node nn in the message.
CharacteristicCell proliferationCell clusteringEpidemiologyOncology
Create new agents✓  ✓
Delete agents   ✓
Load imbalance  ✓ 
Agents move randomly  ✓✓
TABLE I : Performance-relevant characteristics of the benchmark simulations (§ IV-B ).
Fig. 4 : Result verification of TeraAgent. (a) Epidemiology and (b) oncology use cases. (c) Cell clustering, final state on eight MPI ranks illustrating the space partitioning grid colored by the rank that owns the volume ( ProcOwner )
Fig. 5 : Scalability of TeraAgent on Snellius: (A) strong scaling and (B) weak scaling. The gray dashed line marks ideal behavior in both panels. The error bars represent the minimum and maximum observed values.
Fig. 6 : Speedup (top) and normalized memory consumption (bottom) with respect to BioDynaMo. Each group shows three bars: OpenMP (the BioDynaMo baseline, normalized to 1), MPI-only, and MPI-hybrid. The legend is shared between the plots.
Fig. 7 : In-situ visualization with ParaView on System B: visualization speedup (top) and normalized memory consumption (bottom) for TeraAgent in MPI-only and MPI-hybrid configurations, both with respect to BioDynaMo (OpenMP). The legend is shared between the plots.
MetricTeraAgentBiocellion
Runtime per iteration15.8 s4.46 s
CPU cores1444,096
Number of agents1.72B1.72B
Agent updates per sec. per core7.56×1057.56\times 10^{5}9.42×1049.42\times 10^{4}
Open sourcePlannedNo
TABLE II : Comparison of TeraAgent and Biocellion on the cell clustering simulation with 1.72 billion agents.
Fig. 8 : Comparison of the TeraAgent-IO serialization mechanism (labeled TA IO in the plots) and ROOT IO on Snellius.
Fig. 9 : Effect of compression on System B. Each group shows three bars: no compression (the TeraAgent-IO baseline, normalized to 1), LZ4 only, and LZ4 with delta encoding. The legend is shared among all plots.
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