Communication Dans Un Congrès Année : 2025

GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP Franc ¸ois Pacaud and Sungho Shin

Résumé

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 25 for the largest instance.

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hal-04875892 , version 1 (09-01-2025)

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  • HAL Id : hal-04875892 , version 1

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François Pacaud, Sungho Shin. GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP Franc ¸ois Pacaud and Sungho Shin. Conference on Decision and Control, IEEE, Dec 2024, Milano, Italy. ⟨hal-04875892⟩
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