CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control
Se Hwan Jeon, Seungwoo Hong, Ho Jae Lee, Charles Khazoom, Sangbae Kim
Abstract
The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of instances. In this work, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CusADi</monospace>, an extension of the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">casadi</monospace> symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CUDA</monospace>. We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CusADi</monospace> for various applications, including parallel simulation, parameter sweeps, and policy training.
BibTeX
@inproceedings{ral2025_cusadiagpuparall,
title = {CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control},
author = {Se Hwan Jeon and Seungwoo Hong and Ho Jae Lee and Charles Khazoom and Sangbae Kim},
booktitle = {RA-L 2025},
year = {2025}
}