Regarding all the questions about Julia:

There's ongoing work to reduce runtime dependencies of Julia (for example in 1.8, you can strip out the compiler and metadata), but then it's only approaching Go/Swift and other static languages with runtimes.

Generating standalone runtime free LLVM is another path, that is actually already pretty mature as it's what is being done for the GPU stack.

Someone just has to retarget that to cpu LLVM, and there's a start here: https://github.com/tshort/StaticCompiler.jl/issues/43

zanellia

That's quite cool. Maybe the whole thing can be rewritten in Julia too at some point. I just know too little about Julia to judge.

amkkma

Well IMO it can definitely be rewritten in Julia, and to an easier degree than python since Julia allows hooking into the compiler pipeline at many areas of the stack. It's lispy an built from the ground up for codegen, with libraries like (https://github.com/JuliaSymbolics/Metatheory.jl) that provide high level pattern matching with e-graphs. The question is whether it's worth your time to learn Julia to do so.

You could also do it at the LLVM level: https://github.com/JuliaComputingOSS/llvm-cbe

One cool use case is in https://github.com/JuliaLinearAlgebra/Octavian.jl which relies on loopvectorization.jl to do transforms on Julia AST beyond what LLVM does. Because of that, Octavian.jl. a pure julia linalg library, beats openblas on many benchmarks