Metatheory.jl
prometeo
Metatheory.jl | prometeo | |
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5 | 11 | |
334 | 610 | |
1.2% | - | |
8.1 | 0.0 | |
7 days ago | almost 2 years ago | |
Julia | Python | |
MIT License | BSD 2-clause "Simplified" License |
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Metatheory.jl
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[ANN] E-graphs and equality saturation: hegg 0.1
I'd love to see something in the lines of Julia's https://juliasymbolics.github.io/Metatheory.jl/dev/
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Twitter Thread: Symbolic Computing for Compiler Optimizations in Julia
From that example you can see how this makes some rather difficult compiler questions all be subsumed in the e-graph saturation solve. That solve itself isn't easy, it's an NP-hard problem that requires good heuristics and such, and that's what Metatheory.jl, and that's what chunks of the thesis are about. But given a good enough solver, the ability to write such transformation passes becomes rather trivial and you get an optimal solution in the sense of the chosen cost function. So problems like enabling automatic FMA on specific codes is rather simple with this tool: just declare a*b + c = fma(a,b,c), the former is a cost of 2 the latter is a cost of one, and let it rip.
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
Well IMO it can definitely be rewritten in Julia, and to an easier degree than python since Julia allows hooking into the compiler pipeline at many areas of the stack. It's lispy an built from the ground up for codegen, with libraries like (https://github.com/JuliaSymbolics/Metatheory.jl) that provide high level pattern matching with e-graphs. The question is whether it's worth your time to learn Julia to do so.
You could also do it at the LLVM level: https://github.com/JuliaComputingOSS/llvm-cbe
For interesting takes on that, you can see https://github.com/JuliaLinearAlgebra/Octavian.jl which relies on loopvectorization.jl to do transforms on Julia AST beyond what LLVM does. Because of that, Octavian.jl beats openblas on many linalg benchmarks
- From Julia to Rust
- Algebraic Metaprogramming in Julia with Metatheory.jl
prometeo
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Borgo is a statically typed language that compiles to Go
Not impossible but I guess you might end up with an extra runtime layer and some more dynamic operations will not be very fast. Or you restrict it to a subset of Python like this project does: https://github.com/zanellia/prometeo
You could of course write a bytecode VM in Golang but I guess that defeats the purpose.
- Are there any libraries that can easily convert Python to C/C#/or C++? Ones where a person doesn't have to "calibrate" it, just, pip install library and then they can have their Python code in C,C#,or C++?
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I made a Python compiler, that can compile Python source down to fast, standalone executables.
Honest question: How does pycom compare to similar tools like Nuitka, prometeo, or mypyc?
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Profiling and Analyzing Performance of Python Programs
If you don't mind switching to a little different syntax of Python, then you also might want to take a look at prometeo - an embedded domain specific language based on Python, specifically aimed at scientific computing. Prometeo programs transpile to pure C code and its performance can be comparable with hand-written C code.
- GitHub - zanellia/prometeo: An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
- Show HN: Prometeo – a Python-to-C transpiler for high-performance computing
- An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
This is awesome! The direction of using a subset of python, while leveraging the user base and static typing to accomplish some other everyday task in a different language is very legit IMO.
I took a cursory look at:
https://github.com/zanellia/prometeo/blob/master/prometeo/cg...
It seems quite similar in spirit to
https://github.com/adsharma/py2many/blob/main/pyrs/transpile...
I'm not spending much time on py2many last few months (started a new job). Let me know if any of it sounds useful - especially the ability to transpile to 7-8 languages including Julia, C++ and Rust.
What are some alternatives?
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
Dagger.jl - A framework for out-of-core and parallel execution
llvm-cbe - resurrected LLVM "C Backend", with improvements
MacroTools.jl - MacroTools provides a library of tools for working with Julia code and expressions.
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
acados - Fast and embedded solvers for nonlinear optimal control
textX - Domain-Specific Languages and parsers in Python made easy http://textx.github.io/textX/
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
MatrixEquations.jl - Solution of Lyapunov, Sylvester and Riccati matrix equations using Julia