egg
Metatheory.jl
egg | Metatheory.jl | |
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25 | 5 | |
1,239 | 334 | |
2.7% | 1.2% | |
6.8 | 8.1 | |
10 days ago | 3 days ago | |
Rust | Julia | |
MIT License | MIT License |
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egg
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An Introduction to Graph Theory
Maybe program optimization?
https://egraphs-good.github.io/
- The E-graph extraction problem is NP-complete
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What is the state of the art for creating domain-specific languages (DSLs) with Rust?
For semantic analyzers, check out egg and egglog. They're custom data structures for representing compiler rewrite rules in a non-destructive way.
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Ask HN: What is new in Algorithms / Data Structures these days?
E-graphs are pretty awesome, and worth keeping in your back pocket. They're like union-find structures, except they also maintain congruence relations (i.e. if `x` and `y` are in the same set, then `f(x)` and `f(y)` must likewise be in the same set).
https://egraphs-good.github.io/
(Incidentally, union-find structures are also great to know about. But they're not exactly "new".)
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What are the current hot topics in type theory and static analysis?
I would add that Equality saturation/E-graphs has become quite a hot topic recently, since their POPL21 paper, with workshops dedicated to applications of e-graphs. They have even recently been added to Cranelift as an IR for optimizations.
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Compiler Optimizations Are Hard Because They Forget
Egraphs solve the rewrite ordering problem quite nicely. https://egraphs-good.github.io/
Note that one solution to this problem is to use equality saturation (which, coincidentally, has a great implementation in rust!).
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Modularity in IR representation and modification
Have you thought about trying to parallelize e-graphs? This way you can do a bunch of rewrite rules in parallel and then extract your desired graph at the end instead of having conflicts.
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Any recommendations for good resources that show how algorithms and data structures are converted into fpga circuits
I think the equality saturation papers are a good start. A good start is egg. They have a presentation, a research paper and code you can play with. I think ultimately you want to translate arithmetic operations into logical operation that can be understood by the fpga. So I think it would be good to research how adders and multipliers are implemented in logic and ultimately include equalities between adders/multipliers with their logical counterpart. Note the this translation also depends on the representations of your numbers and their bit width.
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Strategies for doing symbolic integration algorithmically
For rewriting, you may also find interesing equality saturation: https://egraphs-good.github.io/
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
What are some alternatives?
prose - Microsoft Program Synthesis using Examples SDK is a framework of technologies for the automatic generation of programs from input-output examples. This repo includes samples and sample data for the Microsoft Program Synthesis using Example SDK.
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
Symbolics.jl - Symbolic programming for the next generation of numerical software
Dagger.jl - A framework for out-of-core and parallel execution
Catlab.jl - A framework for applied category theory in the Julia language
MacroTools.jl - MacroTools provides a library of tools for working with Julia code and expressions.
acados - Fast and embedded solvers for nonlinear optimal control
glow - Compiler for Neural Network hardware accelerators
Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
StaticArrays.jl - Statically sized arrays for Julia
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R