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| # HybridKKT.jl | ||
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| Artifact to reproduce the benchmarks presented in | ||
| the manuscript ["Condensed-space methods for nonlinear programming on GPUs"](https://arxiv.org/abs/2405.14236). | ||
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| A `Manifest.toml` file is provided to duplicate the exact package versions we used for the | ||
| benchmarks presented in the paper. A `Makefile` is used as a main entry point. | ||
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| *Important notice:* | ||
| This repository is provided only for reproduction purpose. Please use | ||
| the following implementation if you want to use the condensed KKT systems in | ||
| your own work: | ||
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| - LiftedKKT has been implemented in [MadNLP](https://github.com/MadNLP/MadNLP.jl/blob/master/src/KKT/Sparse/condensed.jl) | ||
| - HyKKT has been implemented in a separate extension: [HybridKKT.jl](https://github.com/MadNLP/HybridKKT.jl) | ||
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| ## Installation | ||
| To install all the dependencies, please use: | ||
| ```shell | ||
| make install | ||
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| ``` | ||
| This command installs MadNLP and all the required dependencies | ||
| (including CUDA and cuDSS). | ||
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| Note that HSL has to be installed independently using | ||
| [libHSL](https://licences.stfc.ac.uk/product/libhsl), and then do: | ||
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| ```shell | ||
| export LIBHSL="/your/path/to/HSL_jll.jl" | ||
| julia --project -e "using Pkg; Pkg.develop(path=ENV[\"LIBHSL\"])" | ||
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| ``` | ||
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| ## Tests the installation | ||
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| You can check the installation has succeeded by running: | ||
| ```shell | ||
| make tests | ||
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| ``` | ||
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| ## Reproduce the results | ||
| You can reproduce the PGLIB and the COPS benchmarks using: | ||
| ```shell | ||
| make benchmarks | ||
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| ``` |
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| Original file line number | Diff line number | Diff line change |
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| # nlp-on-gpu-paper | ||
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| Make it a review paper comparing different methods for implementing a nonlinear sparse, large-scale optimization solver on the GPU. We will focus on methods, not the implementation. For numerical comparison, we will use MadNLP. We will compare: | ||
| - HyKKT method | ||
| - condensed space inequality relaxation | ||
| - condensed then reduce | ||
| This repository is an artifact storing the tex sources and the Julia scripts | ||
| used to generate the results in the manuscript "Condensed-space methods for nonlinear programming on GPUs", | ||
| available on [arxiv](https://arxiv.org/abs/2405.14236). | ||
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| - The directory `HybridKKT.jl/` stores the Julia implementation of the method presented in the paper. | ||
| - The directory `tex/` stores the tex sources. | ||
| - The directory `scripts/` stores script files to generate the plots presented in the paper using `matplotlib`. | ||
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| To cite the paper, please use the following bibtex reference: | ||
| ```tex | ||
| @article{pacaud2024condensed, | ||
| title={Condensed-space methods for nonlinear programming on {GPU}s}, | ||
| author={Pacaud, Fran{\c{c}}ois and Shin, Sungho and Montoison, Alexis and Schanen, Michel and Anitescu, Mihai}, | ||
| journal={arXiv preprint arXiv:2405.14236}, | ||
| year={2024} | ||
| } | ||
| ``` | ||
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| For sparse solver, we compare two options: | ||
| - CUDSS | ||
| - CUSOLVERRF | ||
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| Portability (not our primary focus, but if we want to say something) | ||
| - https://github.com/ORNL/ReSolve/blob/v0.99.1/resolve/LinSolverDirectRocSolverRf.cpp | ||
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| # Friday, January 19th | ||
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| * Goal of the optimization paper | ||
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| Assess the capabilities of three linear solvers to solve nonlinear optimization problem on the GPU: | ||
| - Null-space method (aka reduced Hessian, Argos) | ||
| - Hybrid-condensed KKT solver (HyKKT) | ||
| - Sparse-condensed KKT with equality relaxation strategy | ||
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| The two last methods require efficient sparse Cholesky available on the GPU. | ||
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| * Latests developments (https://github.com/exanauts/nlp-on-gpu-paper/tree/main/scripts) | ||
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| - Implementation of HyKKT in MadNLP, now gives correct result. | ||
| * works on the GPU | ||
| * no iterative refinement (yet): limited precision | ||
| * solve OPF problems with tol=1e-3 | ||
| * it looks like CG is the bottleneck in the algorithm | ||
| - Full integration of cuDSS into MadNLP for sparse-Cholesky | ||
| - Integration of CHOLMOD on the CPU for comparison | ||
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| * To discuss | ||
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| - Improve the HyKKT implementation | ||
| * Implement iterative refinement on the GPU @FP | ||
| * double check the accuracy of the linear solve (and its interplay with CG convergence) @FP | ||
| * identify the computation bottleneck and address them @FP | ||
| * Implement AMD ordering for sparse Cholesky @AM | ||
| * scaling of the problem (use KrylovPreconditioner?) @AM | ||
| ^ Implement symmetric scaling on the GPU | ||
| - Decide what we want to showcase exactly | ||
| * go deeper into the LA aspect | ||
| * Benchmark on OPF and SCOPF instances? | ||
| * Include additional benchmarks? | ||
| ^ COPS benchmark in ExaModels? Have a look at the Goddard problem | ||
| ^ PDE-constrained optimization? | ||
| - NCL ? Can we finish the implementation in a reasonable time? | ||
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| * |