Top 7 Standard ML sml Projects

mlton

9 916 8.3 Standard ML

The MLton repository

Project mention: Flunct: Well-typed, fluent APIs in SML | news.ycombinator.com | 2023-06-25

https://github.com/MLton/mlton/issues/473
Is there sufficient use of MLTon "native" backend out there to consider it mature? or Do people prefer the LLVM or C backend instead in general?

cakeml

14 912 9.8 Standard ML

CakeML: A Verified Implementation of ML

Project mention: The Deep Link Equating Math Proofs and Computer Programs | news.ycombinator.com | 2023-10-11

If I understand what you are asking about correctly, then I do think you are mistaken.
As a sibling comment observed, you would be proving something about a program, but proving things about programs is both possible and done.
This ranges from things like CakeML (https://cakeml.org/) and CompCert (compilers with verified correctness proofs of their optimizations) to something simple like absence of runtime type errors in statically strongly soundly-typed languages.
Of note is that you are proving properties of your program, not proving them perfect in every way. The properties of your program that you prove can vary wildly in both difficulty and usefulness. A sufficiently advanced formally verified compiler like CakeML can transfer a high-level proof about your source code to a corresponding proof about the behavior of the generated machine-executable code.

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mpl

7 285 8.4 Standard ML

The MaPLe compiler for efficient and scalable parallel functional programming

Project mention: Garbage Collection for Systems Programmers | news.ycombinator.com | 2024-03-30

I'm one of the authors of this work -- I can explain a little.
"Provably efficient" means that the language provides worst-case performance guarantees.
For example in the "Automatic Parallelism Management" paper (https://dl.acm.org/doi/10.1145/3632880), we develop a compiler and run-time system that can execute extremely fine-grained parallel code without losing performance. (Concretely, imagine tiny tasks of around only 10-100 instructions each.)
The key idea is to make sure that any task which is *too tiny* is executed sequentially instead of in parallel. To make this happen, we use a scheduler that runs in the background during execution. It is the scheduler's job to decide on-the-fly which tasks should be sequentialized and which tasks should be "promoted" into actual threads that can run in parallel. Intuitively, each promotion incurs a cost, but also exposes parallelism.
In the paper, we present our scheduler and prove a worst-case performance bound. We specifically show that the total overhead of promotion will be at most a small constant factor (e.g., 1% overhead), and also that the theoretical amount of parallelism is unaffected, asymptotically.
All of this is implemented in MaPLe (https://github.com/mpllang/mpl) and you can go play with it now!

smlpkg

4 157 3.9 Standard ML

Generic package manager for Standard ML libraries and programs
install-mlkit

1 5 3.9 Standard ML

Action for installing MLKit
smlnj-viscomp-example

1 4 0.0 Standard ML

An example of how to use SML/NJ's Visible Compiler APIs
sml-parseq

1 4 4.5 Standard ML

parallel sequences library in Standard ML
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NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).