• @[email protected]
    link
    fedilink
    98 months ago

    I’m not familiar with any special LLVM instructions for Haskell. Regardless, LLVM is not actually a commonly used backend for Haskell (even though you can) since it’s not great for optimizing the kind of code that Haskell produces. Generally, Haskell is compiled down to native code directly.

    Haskell has a completely different execution model to imperative languages. In Haskell, almost everything is heap allocated, though there may be some limited use of stack allocation as an optimization where it’s safe. GHC has a number of aggressive optimizations it can do (that is, optimizations that are safe in Haskell thanks to purity that are unsafe in other languages) to make this quite efficient in practice. In particular, GHC can aggressively inline a lot more code than compilers for imperative languages can, which very often can eliminate the indirection associated with function calls entirely. https://gitlab.haskell.org/ghc/ghc/-/wikis/commentary/compiler/generated-code goes into a lot more depth about the execution model if you’re interested.

    As for languages other than Haskell without such an execution model (especially imperative languages), it’s true that there can be the overhead you describe, which is why the vast majority of them use iterators to achieve the effect, which avoids the overhead. Rust (which has mapping/filtering, etc. as a pervasive part of its ecosystem) does this, for example, even though it’s a systems programming language with a great deal of focus on performance.

    As for the advantage, it’s really about expressiveness and clarity of code, in addition to eliminating the bugs so often resulting from mutation.

    • @[email protected]
      link
      fedilink
      38 months ago

      Interesting.

      So it basically enables some more compiler magic. As an embedded guy I’ll stay away from it, since I like my code being translated a bit more directly, but maybe I’ll look into the generated code and see if I can apply some of the ideas for optimizations in the future.