Principle:Google deepmind Mujoco MJX Benchmarking
| Knowledge Sources | |
|---|---|
| Domains | Benchmarking, GPU_Computing, Performance |
| Last Updated | 2026-02-15 06:00 GMT |
Overview
Methodology for measuring the throughput and latency of MJX GPU-accelerated physics simulation.
Description
MJX Benchmarking measures the performance of the JAX-based physics engine in terms of JIT compilation time, wall-clock simulation time, steps per second, and real-time factor. It runs a configurable number of simulation steps across a batch of parallel environments, separating one-time compilation cost from per-step execution cost. This is essential for comparing MJX performance against the native C engine and across different GPU hardware.
Usage
Use when evaluating whether MJX provides a performance benefit for a specific model and batch size. Compare against the C engine benchmark (testspeed) to determine the crossover point where GPU parallelism outweighs the per-step overhead.
Theoretical Basis
Key benchmark metrics:
- JIT compile time: One-time cost of XLA compilation
- Total sim time: Wall-clock time for all steps (excluding JIT)
- Steps/sec: Total steps (batch_size * nstep) / sim time
- Real-time factor: (nstep * timestep) / sim time
- us/step: Microseconds per individual step