HN Debrief

Stealing from Biologists to Compile Haskell Faster

  • Programming
  • Developer Tools
  • Algorithms

The post takes a specific compile-time bottleneck in GHC, the Glasgow Haskell Compiler, and recasts it in terms borrowed from biology and fast matrix-style algorithms. The claim is not that Haskell compilation suddenly gets much faster in practice. It is that an “optimal path” problem inside the compiler can be pushed below cubic time in theory by using techniques related to min-plus algebra and sequence-style dynamic programming. The author’s own follow-up made the practical verdict clear: you probably would not want to ship this in GHC, because the best bound still comes with ugly constants and would drag a lot of linear algebra machinery into a compiler for little real-world gain.

Treat this as a reminder that asymptotic wins do not automatically belong in production compilers. If you work on build systems, compilers, or dependency scheduling, check whether your real bottleneck is algorithmic complexity or whether simpler graph methods already get you close enough.

Discussion mood

Interested but skeptical. People liked the cross-disciplinary algorithmic trick, but the mood was that this is a neat theoretical result rather than something a compiler team should rush to implement.

Key insights

  1. 01

    Author says the result is not deployable

    The subcubic bound lands as a proof of possibility, not a roadmap for GHC. Pulling heavy linear algebra into the compiler would cost a lot of complexity, and the remaining constant factors wipe out the headline speedup for practical use.

    Do not read this as an imminent compiler optimization. If you are evaluating similar papers, ask early what new machinery must enter the codebase and whether the workload is large enough for asymptotics to dominate.

      Attribution:
    • iand675 #1
  2. 02

    Monotone min-plus may improve the bound further

    The post may be leaving performance on the table because the underlying problem has more structure than plain min-plus. If the dependency values are bounded by a small k, a commenter says you can sometimes make the running time depend on k instead of n, which is exactly the kind of parameterized improvement that can matter in real inputs.

    When a hard problem in your system maps to a known algebraic primitive, check whether extra structure like monotonicity or small bounded weights changes the algorithm choice. Parameterized bounds often matter more than the generic worst case.

      Attribution:
    • chaoxu #1

Against the grain

  1. 01

    Plain graph algorithms may already be enough

    The fancy formulation may be solving a harder or more abstract problem than the compiler needs. Both comments reframe the task as ordinary dependency processing, where topological sorting, level assignment, or Tarjan's strongly connected components can expose the same compile order with far less conceptual and implementation overhead.

    Before adopting an exotic algorithm, restate the problem in basic graph terms and benchmark the obvious solution. A simpler O(n²) approach can easily beat a theoretically faster method once engineering cost and constants enter the picture.

      Attribution:
    • jaggederest #1
    • SkiFire13 #1

In plain english

GHC ↩
Glasgow Haskell Compiler, the main open source compiler for the Haskell programming language.
min-plus ↩
A mathematical operation used in some dynamic programming and shortest-path algorithms where addition and minimum replace ordinary multiplication and addition.
monotone min-plus ↩
A structured version of min-plus problems where the values follow an ordered pattern, which can sometimes be exploited for faster algorithms.
subcubic ↩
An algorithm that runs in less than cubic time, meaning its runtime grows slower than n cubed as input size increases.