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

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]