awesome-machine-learning-in-compilers
egg
awesome-machine-learning-in-compilers | egg | |
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5 | 25 | |
1,336 | 1,239 | |
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5.7 | 6.8 | |
19 days ago | 10 days ago | |
Rust | ||
Creative Commons Zero v1.0 Universal | MIT License |
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awesome-machine-learning-in-compilers
- Awesome research papers on ML in Compilers
- Research Papers on ML in Compilers
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Compiler Optimizations Are Hard Because They Forget
This repo is a great collection of various papers & resources on the subject: https://github.com/zwang4/awesome-machine-learning-in-compilers
- Can artificial neural networks make better artificial neural networks than humans can make yet?
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I spent 5 years writing my own operating system
The list goes on.
Genode, unikernels like MirageOS, TempleOS, Singularity OS / Sing#, compiler services like Roslyn and Kotlin, MILEPOST GCC, Tensorflow / TPUs, GPT-3, all of the machine learning in compilers [1] and so much more. I truly think Deep Learning Compilers will be huge.
[1] https://github.com/zwang4/awesome-machine-learning-in-compil...
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/
What are some alternatives?
kernel-ml - Machine Learning Framework for Operating Systems - Brings ML to Linux kernel
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.
winix - A UNIX-style Operating System for the Waikato RISC Architecture Microprocessor (WRAMP)
Symbolics.jl - Symbolic programming for the next generation of numerical software
ZenithOS - The Zenith Operating System is a modernized, professional fork of the 64-bit Temple Operating System.
Catlab.jl - A framework for applied category theory in the Julia language
rexsimulator - a forked copy of https://sourceforge.net/projects/rexsimulator/
Dagger.jl - A framework for out-of-core and parallel execution
awesome-tensor-compilers - A list of awesome compiler projects and papers for tensor computation and deep learning.
glow - Compiler for Neural Network hardware accelerators
Parallel-Computing-Guide - Parallel Computing Guide
StaticArrays.jl - Statically sized arrays for Julia