GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP
Name
2403.15913v2.pdf
Description
Accepted version
Size
302.71 KB
Format
Adobe PDF
Checksum (MD5)
ba27e1b2300f541964614cf265fafc0e
Author(s)
Pacaud, François
Shin, Sungho
Date Issued
February 26, 2025
Publisher
IEEE|2024 IEEE 63rd Conference on Decision and Control
Citation
F. Pacaud and S. Shin, "GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP," 2024 IEEE 63rd Conference on Decision and Control (CDC), Milan, Italy, 2024, pp. 5963-5968.
Version
Author's final manuscript
Abstract
We investigate the potential of Graphics Processing Units (GPUs) to solve large-scale nonlinear programs with a dynamic structure. Using ExaModels, a GPU-accelerated automatic differentiation tool, and the interior-point solver MadNLP, we significantly reduce the time to solve dynamic nonlinear optimization problems. The sparse linear systems formulated in the interior-point method is solved on the GPU using a hybrid solver combining an iterative method with a sparse Cholesky factorization, which harness the newly released NVIDIA cuDSS solver. Our results on the classical distillation column instance show that despite a significant pre-processing time, the hybrid solver allows to reduce the time per iteration by a factor of $\mathbf{2 5}$ for the largest instance.
Description
2024 IEEE 63rd Conference on Decision and Control (CDC), Milan, Italy, 2024
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cdc56724.2024.10886720
