What does this have to do with garbage collection?
If your concern is about latency in real time systems, Garbage collection isn't the problem, heap allocating memory is. Julia makes it easy to never allocate anything on the heap (unlike most garbage collected langauges).
I think the above poster is referring to languages like Go/Java/C#/Nim/whatever. Like Julia, these languages have a significant runtime. Unlike Julia, their apps can be compiled AOT into standalone executables.
I equate garbage collection with larger static binaries. Julia's static binaries are especially large since they package the entire sysimage. I'm excited about StaticCompiler.jl because, by cutting out the sysimage, it promises to make Julia more competitive with other garbage-collected languages like Nim, Go, Crystal, etc., all of which produce relatively small static binaries.
However, like these other garbage-collected languages, Julia is unlikely to ever compete with the likes of Zig, Rust, C++, etc. which produce even smaller static binaries since they don't have to ship a GC runtime. That's why I specified garbage-collected languages as, in my opinion, the addition of proper static compilation will allow Julia to completely supersede other garbage-collected general programming languages but not manually managed systems programming languages.
I think with StaticCompiler.jl, if you create a binary where the compiled code knows there won't be any GC, for instance, say you only ever work with Tuples and floating point numbers, I don't think there will be any GC runtime in the compiled binary.
Julia can stack allocate all sorts of values. I think it currently stack allocates all immutable values and also every mutable value that it proves do not escape their function.
Kind of besides the point, though, because the GC is probably not heavy anyway. Lots of fear of GC is not based on benchmarks.
Julia doesn't make it easy to guarantee that you won't allocate anything on the heap. The semantics of the language don't include really any control over that, yet. I hope one day it does. It seems like a tough problem, because the allocation strategy can depend on the results of inference and compilation, which is another thing that is hard to control since the language try hard to have the semantics not depend on inference.
Of course, you can measure the allocations, and you can see as a matter of fact whether some code allocates, but I would say that it's hard to predict what allocates and what doesn't, and that this is still changing (e.g. https://github.com/JuliaLang/julia/pull/32448)
Look under Static Julia Compiler here [1]. I've successfully done it in Linux, don't know about Windows. It doesn't work when you have binary dependencies like GTK, etc.
