RELEASE · CODING · #897
NVIDIA demonstrates using Warp and MJWarp to run MuJoCo at GPU scale (up to 2,048 envs)
NVIDIA describes migrating a MuJoCo workflow (an SO-101 follower arm) to MJWarp, an implementation of MuJoCo’s physics pipeline in NVIDIA Warp, and demonstrates advancing as many as 2,048 parallel environments on NVIDIA GPUs. The article explains Warp’s GPU kernel model, device arrays, composable launches and graph capture, and notes Warp features such as differentiable kernels and an opt-in deterministic mode (Warp ≥ 1.15); it focuses on preparing and scaling simulation environments rather than training policies.
KEY POINTS
- NVIDIA describes migrating a MuJoCo workflow (an SO-101 follower arm) to MJWarp, an implementation of MuJoCo’s physics pipeline in NVIDIA Warp, and demonstrates advancing as many as 2,048 parallel environments on NVIDIA GPUs.
- The article explains Warp’s GPU kernel model, device arrays, composable launches and graph capture, and notes Warp features such as differentiable kernels and an opt-in deterministic mode (Warp ≥ 1.15); it focuses on preparing and scaling simulation environments rather than training policies.
- Moving MuJoCo simulation onto GPUs with MJWarp can massively increase world-step throughput for reinforcement learning and large-scale sampling by running hundreds-to-thousands of environments in parallel and keeping data on-device.
WHY IT MATTERS
Moving MuJoCo simulation onto GPUs with MJWarp can massively increase world-step throughput for reinforcement learning and large-scale sampling by running hundreds-to-thousands of environments in parallel and keeping data on-device.