“An Extensible, Data-oriented Architecture for High-performance, Many-world Simulation” by Shacklett, Rosenzweig, Sarkar, Xie, Szot, et al. …
Conference:
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Title:
- An Extensible, Data-oriented Architecture for High-performance, Many-world Simulation
Session/Category Title: Cloud Rendering: Your GPU Is Somewhere Else
Presenter(s)/Author(s):
- Brennan Shacklett
- Luc Guy Rosenzweig
- Bidipta Sarkar
- Zhiqiang Xie
- Andrew Szot
- Erik Wijmans
- Vladlen Koltun
- Dhruv Batra
- Kayvon Fatahalian
Moderator(s):
Abstract:
Training agents to learn skills in simulated environments is an increasingly important workload at the intersection of AI and graphics. The Madrona framework allows users to build high-performance simulators for novel tasks by leveraging entity-component-system (ECS) interfaces that map task logic onto parallel GPU execution across thousands of environments.
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