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I agree with GP. It's not just about the differing goals of the projects. Julia is developed by numerical computing people. Imho the community really needs hel

by neolog 5y ago

I agree with GP. It's not just about the differing goals of the projects. Julia is developed by numerical computing people. Imho the community really needs help from some PL and systems people.

noobermin 5y ago
Why? EDIT: Expounding on "why": On what aspects specifically does the Julia community need help from system programmers and programming language experts?
- deleted 5y ago
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- deleted 5y ago
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BadInformatics 5y ago
The biggest group outside of numerical computing in Julia land are the PL and systems people though? This includes type theorists [1], database folks [2], distributed systems people ([3] to name just one). There are also a fair number of compiler nuts, hence the existence of multiple projects [4][5] in this space. And this is before getting into things that bridge more than one of the domains above, e.g. [7] or [8]. FTR, I think it's fair to question whether numerical computing should have an outsized influence on the direction of the language. I also think it's a pretty fair comparison to point out how standardized and consistent the Rust governance process is compared to Julia's (the Rust RFC system is an exemplar here). That doesn't mean there is a dearth of PL and systems knowledge in the Julia community though. [1] https://github.com/AlgebraicJulia/Catlab.jl https://github.com/AlgebraicJulia/Catlab.jl [2] https://www.youtube.com/watch?v=tRBl-6uEJJE https://www.youtube.com/watch?v=tRBl-6uEJJE [3] https://github.com/JuliaParallel/Dagger.jl/ https://github.com/JuliaParallel/Dagger.jl/ [4] https://github.com/FluxML/IRTools.jl https://github.com/FluxML/IRTools.jl, https://github.com/FluxML/MacroTools.jl https://github.com/FluxML/MacroTools.jl [5] https://github.com/JuliaCompilerPlugins https://github.com/JuliaCompilerPlugins [6] https://github.com/0x0f0f0f/Metatheory.jl https://github.com/0x0f0f0f/Metatheory.jl [7] https://2020.splashcon.org/details/splash-2020-rebase/13/Non-local-compiler-transformations-in-the-presence-of-dynamic-dispatch https://2020.splashcon.org/details/splash-2020-rebase/13/Non... [8] https://github.com/JuliaSymbolics/Symbolics.jl https://github.com/JuliaSymbolics/Symbolics.jl
- neolog 5y ago
  There are some nice packages, no doubt. How many people with significant prior language design experience are deeply involved in the language design process? what about database dev experience? OS dev? server dev? > I also think it's a pretty fair comparison to point out how standardized and consistent the Rust governance process is compared to Julia's (the Rust RFC system is an exemplar here). Yep. I want the language to be good, but it needs help from communities like the Rust language team.
  - BadInformatics 5y ago
    I can think of at least 5 "core" contributors (out of only 20 or so) off the top of my head who have a PL or systems background. Given some time, I could easily find 10-15 more who are affiliated with the Julia Lab, Julia Computing or community regulars. Hell, most of the Julia Lab's projects are systems stuff.
vcxy 5y ago
As someone who really only does numerical computing, and mostly with Julia, I'm curious. Could Julia use some help from PL and systems people to be noticably better for people like me? Or would you guess it's already on an ideal trajectory if numerical science stuff is all I plan to use it for? I know their goal is to really be an all around good general language, so what you said makes sense for that goal regardless. I'm just curious if you have insight into gains that the numerics people like me could get that we might not know enough to know we're missing.
- miguelraz 5y ago
  What is the focus on numerical computing that you think Julia is missing? The ecosystem seems plenty healthy to me. (Maybe PDEs... but those are so close!) It's not uncommon for good Julia codes to smoke Fortran codebases, like the DiffEq verse or with staged programming, see Steven G. Johnson's keynote at JuliaCon 2019: https://www.youtube.com/watch?v=mSgXWpvQEHE https://www.youtube.com/watch?v=mSgXWpvQEHE
- neolog 5y ago
  Julia is great for numerical computing, but it's not as good as it can be, even in that area. Widening the community's base of expertise will help. I'll just list a few things that come to mind. Many Base functions are currently untyped, which means it's hard to abstract out the key properties that enable reuse, compiler optimizations, and parallelism. See [0] for a language expert's take on numerical computing, which involves defining traits like associativity that enable automatic parallelism by default. A stronger culture of functional programming could make your code faster and easier to understand. It's often (though not always) easier for tooling to optimize pure functions over immutable data structures (and arrays). Arrays are currently mutable, and there is too much emphasis on mutating functions. The iteration protocol can be made more memory-efficient for large collections and simpler, following Rust's implementation [1]. Macros are useful for high-performance computing [4], and Julia's macros can be made more composable [2]. In general, systems languages don't make language decisions lightly. They have committees, discuss how other languages do things, make proposals. This allows more perspectives on each decision. That would be an improvement over the more ad-hoc style of Julia development, as long as Julia can avoid adding every possible feature, which is a risk of expanding the decision-making body [3]. [0] https://www.youtube.com/watch?v=EZD3Scuv02g https://www.youtube.com/watch?v=EZD3Scuv02g [1] https://mikeinnes.github.io/2020/06/04/iterate.html https://mikeinnes.github.io/2020/06/04/iterate.html [2] https://github.com/JuliaLang/julia/issues/37691 https://github.com/JuliaLang/julia/issues/37691 [3] https://www.stroustrup.com/P0977-remember-the-vasa.pdf https://www.stroustrup.com/P0977-remember-the-vasa.pdf [4] https://www.youtube.com/watch?v=mSgXWpvQEHE https://www.youtube.com/watch?v=mSgXWpvQEHE

by neolog 5y ago

I agree with GP. It's not just about the differing goals of the projects. Julia is developed by numerical computing people. Imho the community really needs help from some PL and systems people.

noobermin 5y ago

Why? EDIT: Expounding on "why": On what aspects specifically does the Julia community need help from system programmers and programming language experts?

deleted 5y ago
[deleted]
deleted 5y ago
[deleted]

BadInformatics 5y ago

The biggest group outside of numerical computing in Julia land are the PL and systems people though? This includes type theorists [1], database folks [2], distributed systems people ([3] to name just one). There are also a fair number of compiler nuts, hence the existence of multiple projects [4][5] in this space. And this is before getting into things that bridge more than one of the domains above, e.g. [7] or [8]. FTR, I think it's fair to question whether numerical computing should have an outsized influence on the direction of the language. I also think it's a pretty fair comparison to point out how standardized and consistent the Rust governance process is compared to Julia's (the Rust RFC system is an exemplar here). That doesn't mean there is a dearth of PL and systems knowledge in the Julia community though. [1] https://github.com/AlgebraicJulia/Catlab.jl https://github.com/AlgebraicJulia/Catlab.jl [2] https://www.youtube.com/watch?v=tRBl-6uEJJE https://www.youtube.com/watch?v=tRBl-6uEJJE [3] https://github.com/JuliaParallel/Dagger.jl/ https://github.com/JuliaParallel/Dagger.jl/ [4] https://github.com/FluxML/IRTools.jl https://github.com/FluxML/IRTools.jl, https://github.com/FluxML/MacroTools.jl https://github.com/FluxML/MacroTools.jl [5] https://github.com/JuliaCompilerPlugins https://github.com/JuliaCompilerPlugins [6] https://github.com/0x0f0f0f/Metatheory.jl https://github.com/0x0f0f0f/Metatheory.jl [7] https://2020.splashcon.org/details/splash-2020-rebase/13/Non-local-compiler-transformations-in-the-presence-of-dynamic-dispatch https://2020.splashcon.org/details/splash-2020-rebase/13/Non... [8] https://github.com/JuliaSymbolics/Symbolics.jl https://github.com/JuliaSymbolics/Symbolics.jl

neolog 5y ago
There are some nice packages, no doubt. How many people with significant prior language design experience are deeply involved in the language design process? what about database dev experience? OS dev? server dev? > I also think it's a pretty fair comparison to point out how standardized and consistent the Rust governance process is compared to Julia's (the Rust RFC system is an exemplar here). Yep. I want the language to be good, but it needs help from communities like the Rust language team.
- BadInformatics 5y ago
  I can think of at least 5 "core" contributors (out of only 20 or so) off the top of my head who have a PL or systems background. Given some time, I could easily find 10-15 more who are affiliated with the Julia Lab, Julia Computing or community regulars. Hell, most of the Julia Lab's projects are systems stuff.

vcxy 5y ago

As someone who really only does numerical computing, and mostly with Julia, I'm curious. Could Julia use some help from PL and systems people to be noticably better for people like me? Or would you guess it's already on an ideal trajectory if numerical science stuff is all I plan to use it for? I know their goal is to really be an all around good general language, so what you said makes sense for that goal regardless. I'm just curious if you have insight into gains that the numerics people like me could get that we might not know enough to know we're missing.

miguelraz 5y ago
What is the focus on numerical computing that you think Julia is missing? The ecosystem seems plenty healthy to me. (Maybe PDEs... but those are so close!) It's not uncommon for good Julia codes to smoke Fortran codebases, like the DiffEq verse or with staged programming, see Steven G. Johnson's keynote at JuliaCon 2019: https://www.youtube.com/watch?v=mSgXWpvQEHE https://www.youtube.com/watch?v=mSgXWpvQEHE
neolog 5y ago
Julia is great for numerical computing, but it's not as good as it can be, even in that area. Widening the community's base of expertise will help. I'll just list a few things that come to mind. Many Base functions are currently untyped, which means it's hard to abstract out the key properties that enable reuse, compiler optimizations, and parallelism. See [0] for a language expert's take on numerical computing, which involves defining traits like associativity that enable automatic parallelism by default. A stronger culture of functional programming could make your code faster and easier to understand. It's often (though not always) easier for tooling to optimize pure functions over immutable data structures (and arrays). Arrays are currently mutable, and there is too much emphasis on mutating functions. The iteration protocol can be made more memory-efficient for large collections and simpler, following Rust's implementation [1]. Macros are useful for high-performance computing [4], and Julia's macros can be made more composable [2]. In general, systems languages don't make language decisions lightly. They have committees, discuss how other languages do things, make proposals. This allows more perspectives on each decision. That would be an improvement over the more ad-hoc style of Julia development, as long as Julia can avoid adding every possible feature, which is a risk of expanding the decision-making body [3]. [0] https://www.youtube.com/watch?v=EZD3Scuv02g https://www.youtube.com/watch?v=EZD3Scuv02g [1] https://mikeinnes.github.io/2020/06/04/iterate.html https://mikeinnes.github.io/2020/06/04/iterate.html [2] https://github.com/JuliaLang/julia/issues/37691 https://github.com/JuliaLang/julia/issues/37691 [3] https://www.stroustrup.com/P0977-remember-the-vasa.pdf https://www.stroustrup.com/P0977-remember-the-vasa.pdf [4] https://www.youtube.com/watch?v=mSgXWpvQEHE https://www.youtube.com/watch?v=mSgXWpvQEHE