The way to find other use cases is to look through the citations. Generally there will be a pattern to it. For cases which reduce to (mass matrix) ODEs FBDF generally (but not always) outperforms CVODE's BDF these days, so those cases have mostly converted over to using the pure Julia solvers. This includes not just ODEs but also other DAEs which are defined through ModelingToolkit, as the index reduction process generates ODEs and generally the ODE form ends up more efficient than using the original DAE form (though not always of course). It's in the fully implicit DAE form that the documentation (as of May 1st 2023, starting somewhere back in 2017 according to the historical docs) recommends using Sundials' IDA as the most efficient method for that case (https://docs.sciml.ai/DiffEqDocs/stable/solvers/dae_solve/) (yes, the docs recommend non-Julia solvers when appropriate. There's more than a few of such recommendations in the documentation). Power systems is such a case with Index-1 DAEs written in the fully implicit form which are difficult in many instances to write in mass matrix form and not already written in ModelingToolkit, hence its use of IDA here. By the same reasoning you can also search around in the citations for other use cases of IDA.
> If you use special routines (BLAS/LAPACK, ...), use them everywhere as the respective community does.
It tests with and with BLAS/LAPACK (which isn't always helpful, which of course you'd see from the benchmarks if you read them). One of the key differences of course though is that there are some pure Julia tools like https://github.com/JuliaLinearAlgebra/RecursiveFactorization... which outperform the respective OpenBLAS/MKL equivalent in many scenarios, and that's one noted factor for the performance boost (and is not trivial to wrap into the interface of the other solvers, so it's not done). There are other benchmarks showing that it's not apples to apples and is instead conservative in many cases, for example https://github.com/SciML/SciPyDiffEq.jl#measuring-overhead showing the SciPyDiffEq handling with the Julia JIT optimizations gives a lower overhead than direct SciPy+Numba, so we use the lower overhead numbers in https://docs.sciml.ai/SciMLBenchmarksOutput/stable/MultiLang....
> you must compile/write whole programs in each of the respective languages to enable full compiler/interpreter optimizations
You do realize that a .so has lower overhead to call from a JIT compiled language than from a static compiled language like C because you can optimize away some of the bindings at the runtime right? https://github.com/dyu/ffi-overhead is a measurement of that, and you see LuaJIT and Julia as faster than C and Fortran here. This shouldn't be surprising because it's pretty clear how that works?
I mean yes, someone can always ask for more benchmarks, but now we have a site that's auto updating tons and tons of ODE benchmarks with ODE systems ranging from size 2 to the thousands, with as many things as we can wrap in as many scenarios as we can wrap. And we don't even "win" all of our benchmarks because unlike for you, these benchmarks aren't for winning but for tracking development (somehow for Hacker News folks they ignore the utility part and go straight to language wars...).
If you have a concrete change you think can improve the benchmarks, then please share it at https://github.com/SciML/SciMLBenchmarks.jl. We'll be happy to make and maintain another.
You must be a truly special kind of stupid to be questioning Chris' knowledge.