because i want to be able to to go turtles all the way down with my code. julia being hosted on llvm does not provide that. if you want to do optimisations in julia you must learn llvm[0]. in this sense hosted languages are always a front-end. common lisp is not this
> in this sense hosted languages are always a front-end. common lisp is not this
Even self-hosted languages are front-ends for assemblers and microcode. What actually matters is being able to have arbitrary control over how code is executed. Julia has this in spades.
Julia has it's own intermediate representations where you can write custom compiler passes and intercept and modify whatever you like (in pure julia). You can think of these tools as being like macros, except instead of lexical extent they have a completely dynamic extent.
With these passes, you can arbitrarily change the meaning of any code recursively. E.g. You could define an execution context where multiplication and addition have swapped meanings, or you could locate every if-else statement in the entire callstack of a program and switch the if clause with the else clause. And again, this is not limited to the lexical extent of a macro, and it does not disable compilation. It is instead a programmable step in compilation.
A classic example of this would be Zygote.jl [1] which takes a program and recursively generates code to that would give the derivative of that program. Doing this requires you to modify literally every function call in the callstack, and you can do this all in pure julia without ever knowing a lick of LLVM.
Granted, there is something ideologically appealing about the idea of a self-hosted langauge. I'm just arguing that on a practical level, the compiler tools allow total control over code execution without changing languages, just as you'd get in a self-hosted language.
as i said i think that Julia is an exciting project in the scientific computing space. my statements were not directed against julia or any other language. i just happen to think that common lisp also has great potential for scientific computing, one that can appeal to scientists who want to be software engineers also. for those that just want to crunch numbers right now python is a first reference then julia, imho. however, i sincerely hope that julia overtakes python
having the ability to be self hosted is definitely not ideological. [* here is a hypothetical question that can be asked: would a julia programmer be more powerful if llvm was written in julia? i think the answer is clear that they would be *]
common lisp is a language specification. it does not have an official implementation, although by far the most popular one is sbcl. in fact there is an implementation CLASP[0] that is hosted on llvm
Yes, and sorry if I also came off as combative here, it was not my intention either. I've used some Common Lisp before I got into Julia (though I never got super proficient with it) and I think it's an excellent language and it's too bad it doesn't get more attention.
I just wanted to share what I think is cool about julia from a metaprogramming point of view, which I think is actually its greatest strength.
> here is a hypothetical question that can be asked: would a julia programmer be more powerful if llvm was written in julia? i think the answer is clear that they would be
Sure, I'd agree it'd be great if LLVM was written in julia. However, I also don't think it's a very high priority because there are all sorts of ways to basically slap LLVM's hands out of the way and say "no, I'll just do this part myself."
E.g. consider LoopVectorization.jl [1] which is doing some very advanced program transformations that would normally be done at the LLVM (or lower) level. This package is written in pure Julia and is all about bypassing LLVM's pipelines and creating hyper efficient microkernels that are competitive with the handwritten assembly in BLAS systems.
To your point, yes Chris' life likely would have been easier here if LLVM was written in julia, but also he managed to create this with a lot less man-power in a lot less time than anything like it that I know of, and it's screaming fast so I don't think it was such a huge impediment for him that LLVM wasn't implemented in julia.
great! i think that as far as scientific computing is concerned one has every right to be excited about julia. it is impressive how much work is being done in such a short time. i first came across it in 2015/6 and i am very happy about how its ecosystem evolved. i use julia and will continue to use it
this is what i would advise given the current state of affairs in machine learning
* use tensorflow / pytorch / pyro for industrial purposes. otherwise know what you are doing !
* use julia and its ecosystem for academic/research purposes. universities should be at the cutting edge of research
* use anything else if you want to experiment deeply. here is where i see the value of common lisp
[0] https://docs.julialang.org/en/v1/devdocs/llvm/