GPU-Accelerated Subsystem-Based ADMM for Large-Scale Interactive Simulation
Harim Ji, Hyunsu Kim, Jeongmin Lee, Somang Lee, Seoki An, Jinuk Heo, Youngseon Lee, Yongseok Lee
Abstract
In this paper, we implement the GPU-accelerated subsystem-based Alternating Direction Method of Multipliers (SubADMM) for interactive simulation. The challenging objective for interactive simulations is to deliver realistic results under tight performance, even for large-scale scenarios. We aim to achieve this by exploiting the parallelizable nature of SubADMM to the fullest extent. We introduce a new subsystem division strategy to make SubADMM ‘GPU friendly' along with custom kernel designs and optimization regarding efficient memory access patterns. We successfully implement the GPUaccelerated SubADMM and show the accuracy and speed of the framework for large-scale scenarios, highlighted with an interactive ‘Hand demo’ scenario. We also show improved robustness and accuracy compared to other state-of-the-art interactive simulators with several challenging scenarios that introduce large-scale ill-conditioned dynamics problems.
BibTeX
@inproceedings{icra2025_gpuacceleratedsu,
title = {GPU-Accelerated Subsystem-Based ADMM for Large-Scale Interactive Simulation},
author = {Harim Ji and Hyunsu Kim and Jeongmin Lee and Somang Lee and Seoki An and Jinuk Heo and Youngseon Lee and Yongseok Lee and Dongjun Lee},
booktitle = {ICRA 2025},
year = {2025}
}