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Hi @TimSiebert1!
I stumbled upon your JuliaCon abstract and it made me very excited about your package! I'm the lead developer (with @adrhill) of DifferentiationInterface.jl, a common standard for calling autodiff backends in Julia. If you want to work with us, we'd be thrilled to try and add ADOLC.jl to the list of supported libraries. As long as you can provide functions for pushforwards (JVPs) and pullbacks (VJPs), that's enough to get started!
What's in it for you?
- Access to a wide potential user base. DifferentiationInterface.jl will soon be a dependency of a fairly large number of packages, including the whole SciML ecosystem. It's easier for users to scitch to ADOLC.jl if they don't have to learn a new syntax or dive into your docs.
- Robust testing. DifferentiationInterface.jl is the ultimate autodiff bugfinder: we have uncovered errors or suboptimalities in nearly every autodiff library we support, and we don't plan to stop.
- Easy benchmarking. DifferentiationInterfaceTest.jl, the companion package, is designed to allow seamless comparison between backends. Wanna see if ADOLC.jl beats the competition? That's the way to go.
- Sparse autodiff. With SparseConnectivityTracer.jl and SparseMatrixColorings.jl, we're taking cues from the work of Andrea Walther and giving Julia a sparse autodiff ecosystem that is unique among high-level scientific programming languages. We'd love to see how it compares to the well-tested C counterparts.
So what do you say?
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