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Keynote. Fortress Features and Lessons Learned | Guy Steele | JuliaCon 2016

The Julia Programming LanguageCited 3× on HN, 2016-2022
YouTube
15K views · 10 years ago · talk · evergreen

Guy Steele's JuliaCon 2016 keynote on the Fortress language, its features and the lessons learned from the project.

Cited as the authoritative first-hand account of the Fortress experience and of numerical computing language design.

Programming language design#fortress#guysteele#julia#parallelism#numericalcomputing

3 comments from Hacker News

@cmrdporcupine2022 · in “Beyond Functional Programming: The Verse Programming Language [pdf]”

I think in the constellation of languages there's still more intersection of "feature dots" between the two than there is disjunction. Multimethods & numerics emphasis alone makes them uniquely similar, as the latter especially is not a feature that has been mined well in other languages. Some of the influence is quite conscious, too, from my reading. And here's a talk by Guy Steele about the Fortress experience, at JuliaCon: ▶ this video

CitesView on HN
@neolog2021 · in “From Julia to Rust”

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] ▶ this video [1] link [2] link [3] link [4] link

CitesView on HN
@pjmlp2016 · in “Why physicists still use Fortran”

> It got shelved after Oracle bought Sun. Actually the actual real history is little more complex than that. JuliaCon 2016 (Keynote) | Fortress Features and Lessons Learned | Guy Steele ▶ this video It would be good if people on HN would separate a bit the Oracle hate with the facts.

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