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arXiv · 2403.15913

GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP

François Pacaud·Sungho Shin
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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 25 for the largest instance.

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François Pacaud, Sungho Shin. 2024-03-23. GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP. https://arxiv.org/abs/2403.15913

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