SaaSHub helps you find the best software and product alternatives Learn more →
Top 13 standard-ml Open-Source Projects
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
sml_rules
Bazel SML rules provide the necessary rules to build and test SML (Standard ML) applications using Bazel.
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?
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!
SML pops up now and again on HN, which is always nice to see. I wrote a language server for SML in an attempt to improve the tooling situation around the language: https://azdavis.net/posts/millet/
The main motivator for why I did this is because we use SML as a teaching language at my university and students always seem to struggle with the error messages and tooling from the compiler.
standard-ml related posts
- Garbage Collection for Systems Programmers
- MPL: Automatic Management of Parallelism
- Four Lectures on Standard ML (1989) [pdf]
- Flunct: Well-typed, fluent APIs in SML
- Programming in Standard ML [pdf]
- Bazel Build Rules for Standard ML
- Comparing Objective Caml and Standard ML
-
A note from our sponsor - SaaSHub
www.saashub.com | 19 Apr 2024
Index
What are some of the best open-source standard-ml projects? This list will help you:
Project | Stars | |
---|---|---|
1 | mlton | 912 |
2 | mpl | 284 |
3 | mlkit | 264 |
4 | LunarML | 240 |
5 | sml-compiler | 196 |
6 | millet | 192 |
7 | smlnj | 158 |
8 | smlpkg | 157 |
9 | smlfmt | 61 |
10 | sml-analyzer | 23 |
11 | molasses | 17 |
12 | sml-parseq | 4 |
13 | sml_rules | 0 |