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- SIMD parallelism ($f0$, $f$, $g$) + sparsity: Kernels for GPU ([KernelAbstraction.jl](XXXX)) and sparse linear algebra ([CUDSS.jl](https://github.com/exanauts/CUDSS.jl))
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- SIMD parallelism ($f0$, $f$, $g$) + sparsity: Kernels for GPU ([KernelAbstraction.jl](https://juliagpu.github.io/KernelAbstractions.jl/stable/)) and sparse linear algebra ([CUDSS.jl](https://github.com/exanauts/CUDSS.jl))
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- Modelling and optimising for GPU: [ExaModels.jl](https://exanauts.github.io/ExaModels.jl/dev/guide) + [MadNLP.jl](https://madnlp.github.io/MadNLP.jl), with **built-in AD**
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-[Simple example, DSL](@ref tutorial-double-integrator-energy)
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- Compile into an ExaModel (one pass compiler, [syntax + semantics](XXXX))
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- Compile into an ExaModel (one pass compiler, [syntax + semantics](https://github.com/control-toolbox/CTParser.jl/blob/20c6be5c953587fef10b054a95f9dc8c66b90577/src/onepass.jl#L145))
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- Simple example, generated code
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```julia
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XXXX
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```
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-**Remark.** Automated scalarisation of (linear) range constraints
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