TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908563696254976 |
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| author | Breitwieser, Lukas Hesam, Ahmad Yağlıkçı, Abdullah Giray Sadrosadati, Mohammad Rademakers, Fons Mutlu, Onur |
| author_facet | Breitwieser, Lukas Hesam, Ahmad Yağlıkçı, Abdullah Giray Sadrosadati, Mohammad Rademakers, Fons Mutlu, Onur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24063 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents Breitwieser, Lukas Hesam, Ahmad Yağlıkçı, Abdullah Giray Sadrosadati, Mohammad Rademakers, Fons Mutlu, Onur Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science Multiagent Systems Performance Quantitative Methods 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. |
| title | TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents |
| topic | Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science Multiagent Systems Performance Quantitative Methods |
| url | https://arxiv.org/abs/2509.24063 |