Huh, I didn’t know Julia had that feature. Personally, I wouldn’t find it very useful, since I’ve never wanted to hand-optimize generated assembly code. Besides, what would you do once you know what assembly is being generated? I guess you could use it to refactor your Julia code to run faster, but you can’t really tell what changes will make the assembly code better.
Personally, I find the easy FFI a lot more interesting, since it means you can hook into existing C libraries or into C code you’ve written yourself. For instance, I’m a contributor to a Julia audio library (https://github.com/ssfrr/AudioIO.jl), which uses the PortAudio and libsndfile libraries. Julia’s FFI means we don’t have to write a lot of boilerplate C code just to interface with the Julia runtime.
I really like this guy’s writing style, but I doubt that my interests will align with his. I’ve never needed that kind of raw compute power - the JVM or CLR has always been good enough. Regardless, I could see this being a good replacement for R, if the statistician community were to accept it.
Fortunately with Julia, there are a lot more reasons to like the language than just raw performance. Having an FFI that doesn’t give the FFI programmer a headache is pretty nice. Also, it has a good type system, homoiconicity, and all those other language features that the author doesn’t really care about.
Huh, I didn’t know Julia had that feature. Personally, I wouldn’t find it very useful, since I’ve never wanted to hand-optimize generated assembly code. Besides, what would you do once you know what assembly is being generated? I guess you could use it to refactor your Julia code to run faster, but you can’t really tell what changes will make the assembly code better.
Personally, I find the easy FFI a lot more interesting, since it means you can hook into existing C libraries or into C code you’ve written yourself. For instance, I’m a contributor to a Julia audio library (https://github.com/ssfrr/AudioIO.jl), which uses the PortAudio and libsndfile libraries. Julia’s FFI means we don’t have to write a lot of boilerplate C code just to interface with the Julia runtime.
I really like this guy’s writing style, but I doubt that my interests will align with his. I’ve never needed that kind of raw compute power - the JVM or CLR has always been good enough. Regardless, I could see this being a good replacement for R, if the statistician community were to accept it.
Fortunately with Julia, there are a lot more reasons to like the language than just raw performance. Having an FFI that doesn’t give the FFI programmer a headache is pretty nice. Also, it has a good type system, homoiconicity, and all those other language features that the author doesn’t really care about.
This is also one of the things I like a lot about CMUCL and SBCL:
I’ve often found it useful for answering “Why are my Lisp functions so slow?”
Is there a good web framework for Julia? I’d be interested in trying it out.