Custom Compiler for MuJoCo Physics Engine

Specialized compiler for accelerating physics-based simulation on heterogeneous hardware

Project Overview:

This project addresses the computational bottleneck in physics-based simulation for reinforcement learning and robotics applications. By developing a specialized compiler for MuJoCo physics engine computations, the system achieves significant performance improvements while maintaining simulation accuracy and enabling gradient-based optimization through automatic differentiation.

Key Contributions:

  • Developed a domain-specific compiler for MuJoCo physics that performs optimizations specific to dynamics simulation
  • Implemented automatic differentiation for the entire simulation pipeline, enabling gradient-based optimization
  • Created a code generator targeting both CPU (with SIMD vectorization) and GPU (CUDA) backends
  • Designed a memory layout optimizer that improves cache locality and reduces memory transfers
  • Integrated the compiler with popular reinforcement learning frameworks like PyTorch and JAX

Technical Implementation:

The compiler pipeline consists of several stages:

  1. Intermediate Representation: Physics computations are represented in a domain-specific IR that captures the mathematical structure of dynamics problems.

  2. Analysis and Optimization: The compiler performs graph-based analyses to identify parallelizable computations, redundant calculations, and opportunities for memory optimization.

  3. Automatic Differentiation: The entire simulation pipeline is made differentiable through source code transformation, enabling efficient computation of gradients.

  4. Backend Code Generation: Optimized code is generated for multiple targets including multicore CPUs with SIMD extensions and NVIDIA GPUs via CUDA.

The system supports the full MuJoCo feature set while providing a clean Python API that integrates with machine learning frameworks.

Performance Results:

  • 10x average speedup for forward dynamics simulation compared to standard MuJoCo
  • 15x faster gradient computation compared to finite-difference methods
  • 8x acceleration of reinforcement learning training loops on benchmark tasks
  • Efficient scaling from desktop computers to HPC clusters

Applications:

The compiler has been successfully applied to accelerate reinforcement learning for robotic control, trajectory optimization for legged locomotion, and high-throughput evolutionary algorithms for robot design. The ability to efficiently compute gradients through physics simulations enables new approaches to controller design and system identification problems.