# An Extensible, Data-Oriented Architecture for High-Performance, Many-World Simulation

*Source* · `src-shacklett-2023-madrona` · status: **n/a**

| field | value |
|---|---|
| kind | Peer-reviewed paper |
| authors | Brennan Shacklett, Luc Guy Rosenzweig, Bidipta Sarkar, Zhiqiang Xie, Andrew Szot, Erik Wijmans, Vladlen Koltun, Dhruv Batra, Kayvon Fatahalian |
| venue | ACM Transactions on Graphics 42(4), SIGGRAPH 2023 |
| date | 2023 |
| url | https://madrona-engine.github.io/ |
| accessed | 2026-10-24 |

## Notes

Madrona batch ECS engine. Project page: Hide and Seek over 1.9M steps/s, Overcooked-AI 40M steps/s, Hanabi 20M steps/s (RTX 4090); 2-3 orders of magnitude over open-source CPU baselines, 5-33x over 32-thread CPU baselines.

## Backlinks (computed)

**cited by:** [Massively parallel GPU simulation](../technique/massively-parallel-simulation.md)

## Citation

Brennan Shacklett, Luc Guy Rosenzweig, Bidipta Sarkar, Zhiqiang Xie, Andrew Szot, Erik Wijmans, Vladlen Koltun, Dhruv Batra, Kayvon Fatahalian (2023). *An Extensible, Data-Oriented Architecture for High-Performance, Many-World Simulation*. ACM Transactions on Graphics 42(4), SIGGRAPH 2023 https://madrona-engine.github.io/

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Provenance: created in run `2026-10-24-techniques-sim-math`.