GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP
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 opti...
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| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
23-03-2024
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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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| DOI: | 10.48550/arxiv.2403.15913 |