GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912023578673152 |
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| author | Pacaud, François Shin, Sungho |
| author_facet | Pacaud, François Shin, Sungho |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP Pacaud, François Shin, Sungho Optimization and Control 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. |
| title | GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2403.15913 |