> Not quite. It builds a dependancy graph of your code and can figure out what definitions depend on others. So depending on what you change, maybe only one or two cells need to be rerun. Or in other circumstances, the whole notebook will have to re-run. It just depends on what changes.
But the effect is still that any changes up-file will be always reflected down-file? If so, I don't care how it's implemented (given it's fast enough and doesn't break), the semantics is the point.
> I think the main trouble with the caching is that the native code you cache can depend very strongly on the exact combination of packages you have loaded. This means you can hit a combinatorial explosion of different methods to cache pretty quickly, so you'd need to find a very clever way to find the right methods to keep and which ones to delete once the cache gets too big.
Yes, I think this is a problem for a clean solution. But for a big fat ugly hack that isn't too picky on wasting disk space or occasionally recompiling stuff needlessly it's probably less so.
For a lot of cases very rough invalidation would probably suffice. E.g. invalidate all definitions from all files that are changed from the last run (i.e. like Make does). And invalidate all definitions for any name that gets any definition. I'd guess accomplishing this would cut the startup time greatly; the end-user code rarely redefines (at least intentionally) anything that's in the packages, and vast majority of time is spent (re)compiling the packages themselves.
I'm sure there are complications with type inference. But I'd be willing to pepper some explicit typing in my code if it means I don't have to recompile it every time I run it. Binary of a method with concrete types should at least be trivially cacheable (given no library changes between runs).
> This is being actively worked on though.
It's been worked on for as long as I've known of Julia. AFAIK there's still absolutely zero logic on caching compilations of "end-user-stuff" (as opposed to stuff like package precompilation). I don't think this is necessarily due to technical issues, but because the community says that REPL (or notebook) is the only way of using Julia, and those don't suffer from the problem that much (Revise.jl breakage notwithstanding).
Technically it's probably very difficult to do "perfectly", and I'm thinking this is how the compiler devs want to do it. I'm not sure they even mean persisting-between-runs caching when they say "caching" in compiler related discussions. It may well be just some run-time caching of some compilation artefacts that are now compiled multiple times. And that would probably not have that dramatic performance gains for the re-run case.
For an AOT compiler Julia is clearly fast enough. There are probably no easy tricks left to make it a lot faster. But re-run performance doesn't need faster AOT, it just needs the compiler not to recompile the same identical stuff every time.