mpl
cakeml
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mpl | cakeml | |
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7 | 14 | |
285 | 912 | |
16.8% | 2.1% | |
8.4 | 9.8 | |
about 2 months ago | 3 days ago | |
Standard ML | Standard ML | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mpl
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Garbage Collection for Systems Programmers
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!
- MPL: Automatic Management of Parallelism
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Good languages for writing compilers in?
Maple is a fork of MLton: https://github.com/MPLLang/mpl
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Comparing Objective Caml and Standard ML
Some of us are still using SML for research and teaching, e.g. https://github.com/mpllang/mpl
- MaPLe Compiler for Parallel ML v0.3 Release Notes
- MPL-v0.3 Release Notes
cakeml
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The Deep Link Equating Math Proofs and Computer Programs
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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The future of Clang-based tooling
> A single IR with multiple passes is a good way to build a compiler
https://mlir.llvm.org/, which is using, is largely claiming the opposite. Most passes more naturally are not "a -> a", but "a -> b". data structures and data structures work hand in hand, it is very nice to produce "evidence" for what is done in the output data structure.
This is why https://cakeml.org/, which "can't cheat" with partial functions, has so many IRs!
Using just a single IR was historically done for cost-control, the idea being that having many IRs was a disaster in repetitive boilerplate. MLIR seeks to solve that exact problem!
- CakeML – A Verified Implementation of ML
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Tools for Verifying a Language and its Semantics
You may want to look at [CakeML](https://cakeml.org) done in HOL4, there is also a nice proof pearl about a more .. minimalistic verified bootstrapped compiler also in HOL4.
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old languages compilers
CakeML
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Is there a formally-proven real-time language/computing env. or operating system?
There is also Cake ML which is a formally verified functional programming language compiler and runtime.
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CakeML: A Verified Implementation of ML
There is also a CakeML -> Standard ML compiler though it seems to have been built to translate benchmarks and sort of old so I'm not sure how comprehensive it is: https://github.com/CakeML/cakeml/tree/master/unverified/front-end
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The λ-Cube
> One guess is that lisps cope with being minimal through use of macros and metaprogramming, it's difficult for a typed language to support that level of metaprogramming while maintaining the various guarantees that one wants from such a system.
Difficult, but certainly not impossible [0].
[0] https://cakeml.org/
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Two Mechanisations of WebAssembly 1.0
If this interests you, I'd highly recommend checking out CompCert (docs here) and CakeML.
- VLISP: A Verified Implementation of Scheme [pdf]
What are some alternatives?
LunarML - The Standard ML compiler that produces Lua/JavaScript
Daikon - Dynamic detection of likely invariants
1ml - 1ML prototype interpreter
hardware - Verilog development and verification project for HOL4
HPCInfo - Information about many aspects of high-performance computing. Wiki content moved to ~/docs.
CompCert - The CompCert formally-verified C compiler
ppci - A compiler for ARM, X86, MSP430, xtensa and more implemented in pure Python
Checker Framework - Pluggable type-checking for Java
mlton - The MLton repository
checkedc - Checked C is an extension to C that lets programmers write C code that is guaranteed by the compiler to be type-safe. The goal is to let people easily make their existing C code type-safe and eliminate entire classes of errors. Checked C does not address use-after-free errors. This repo has a wiki for Checked C, sample code, the specification, and test code.
install-mlkit - Action for installing MLKit
smlpkg - Generic package manager for Standard ML libraries and programs