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I completely disagree. The experience with Julia points in a different direction. Julia performance has has little to do with LLVM. If performance was that eas

I completely disagree. The experience with Julia points in a different direction. Julia performance has has little to do with LLVM. If performance was that easy, you could just slap LLVM in the back of any dynamic language and poff by magic you have super performance. Most dynamic languages that have reached decent performance have done so using trace-JIT compilers. These are complex and require a lot of man hours to make. PyPy is a similar approach. It is hard problem to solve. Yet Julia spending considerably less man hours than either project and with smaller and simpler code base has managed to run circles around these two projects in terms of performance. Why? Because of smart language design. The pervasive multiple-dispatch design tailored towards JIT code generation has been key. It allowed Julia to get great performance with a very simple method-JIT compiler. These are much easier to make than trace-JIT compilers. But Julia is not alone. Go is another example of a language which kicks above its weight. It is a relatively simple implementation, yet has good performance and is enjoyable for most people to work with. Except those who really hate Go of course ;-) My bottom line is: LANGUAGE DESIGN MATTERS!!! You you design a smart and simple language you can get away with simple implementations and still get good performance. Sure LLVM is an important piece of the puzzle, but it serves no more important role than C does as the backend for Haskell IMHO.

josch 7y ago
"pervasive multiple-dispatch design tailored towards JIT code generation" sounds interesting. What is the relationship between the two?
- pansa 7y ago
  Julia is built around multiple dispatch - when making a call, the method to execute is chosen based on the run-time types of all of the arguments. Once this choice has been made, Julia compiles the method, specialised for the exact types of the arguments. So, the dispatch process and the JIT compiler are linked - both are reliant on type information every time a function is called. This specialisation is the only way the Julia JIT uses runtime type information. Unlike JavaScript JITs, Julia does not track things like the types of local variables during execution (although it may do some static inference). Therefore type annotations for local variables can improve performance.
  - StefanKarpinski 7y ago
    Mostly spot on, although it's very rare for local variables to require type annotation—type inference can easily figure it out for locals. Unless, of course, they are assigned from an untyped external source the compiler couldn't possibly know. You could, for example read something from a JSON source knowing that it should be an number, annotate it with `json["field"]::Float64` and then the compiler will emit code to check the type and throw an error unless the type is actually a Float64.
    - pansa2 7y ago
      Thanks for the reply - I wasn’t sure what Julia did in terms of type inference. So, if not on local variables, where are explicit types necessary (other than function parameters)?
      - ChrisRackauckas 7y ago
        You actually don't need explicit types in function parameters unless you're trying to control dispatching. Otherwise the compiler will auto-specialize on call. This blog post might be a better explanation on when it's necessary: https://www.stochasticlifestyle.com/type-dispatch-design-post-object-oriented-programming-julia/ https://www.stochasticlifestyle.com/type-dispatch-design-pos...
- socialdemocrat 7y ago
  pansa had a great answer so I will just add to that. Multiple dispatch is rare among programming languages and the few which do it only has it as an add-on. I believe Julia is the only programming language where multiple dispatch is the way all function calls work. There are no exceptions. It is also the only language where it was used to aid a JIT compiler. Once you dig into multiple dispatch and see how it affects everything from performance to Julia package design and how packages integrate with each other, you will just be blown away. It is hard to convey how ingenious this solution is. Even the creators themselves have admitted they did not realize how clever this would end up being. They only realized after people started actively using Julia what a gold mine they had hit.

josch 7y ago

"pervasive multiple-dispatch design tailored towards JIT code generation" sounds interesting. What is the relationship between the two?

pansa 7y ago
Julia is built around multiple dispatch - when making a call, the method to execute is chosen based on the run-time types of all of the arguments. Once this choice has been made, Julia compiles the method, specialised for the exact types of the arguments. So, the dispatch process and the JIT compiler are linked - both are reliant on type information every time a function is called. This specialisation is the only way the Julia JIT uses runtime type information. Unlike JavaScript JITs, Julia does not track things like the types of local variables during execution (although it may do some static inference). Therefore type annotations for local variables can improve performance.
- StefanKarpinski 7y ago
  Mostly spot on, although it's very rare for local variables to require type annotation—type inference can easily figure it out for locals. Unless, of course, they are assigned from an untyped external source the compiler couldn't possibly know. You could, for example read something from a JSON source knowing that it should be an number, annotate it with `json["field"]::Float64` and then the compiler will emit code to check the type and throw an error unless the type is actually a Float64.
  - pansa2 7y ago
    Thanks for the reply - I wasn’t sure what Julia did in terms of type inference. So, if not on local variables, where are explicit types necessary (other than function parameters)?
    - ChrisRackauckas 7y ago
      You actually don't need explicit types in function parameters unless you're trying to control dispatching. Otherwise the compiler will auto-specialize on call. This blog post might be a better explanation on when it's necessary: https://www.stochasticlifestyle.com/type-dispatch-design-post-object-oriented-programming-julia/ https://www.stochasticlifestyle.com/type-dispatch-design-pos...
socialdemocrat 7y ago
pansa had a great answer so I will just add to that. Multiple dispatch is rare among programming languages and the few which do it only has it as an add-on. I believe Julia is the only programming language where multiple dispatch is the way all function calls work. There are no exceptions. It is also the only language where it was used to aid a JIT compiler. Once you dig into multiple dispatch and see how it affects everything from performance to Julia package design and how packages integrate with each other, you will just be blown away. It is hard to convey how ingenious this solution is. Even the creators themselves have admitted they did not realize how clever this would end up being. They only realized after people started actively using Julia what a gold mine they had hit.