RecursiveArrayTools.jl
dex-lang
RecursiveArrayTools.jl | dex-lang | |
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3 | 25 | |
202 | 1,539 | |
2.5% | 0.5% | |
9.4 | 8.5 | |
7 days ago | 2 days ago | |
Julia | Haskell | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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RecursiveArrayTools.jl
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Julia's latency: Past, present and future
You're not really supposed to be using StaticArraysCore anymore, but here's a somewhat older PR that shows the siginificance of moving StaticArray functionality on a smaller library, moving it from 6228ms to 292ms load time (https://github.com/SciML/RecursiveArrayTools.jl/pull/217).
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Julia 1.8 has been released
> > This gives the package authors a tool to basically "profile" the loading time of their package, which will help them optimize the loading time. So there _will_ be downstream improvement to package loading for us users too.
It lead to https://github.com/SciML/RecursiveArrayTools.jl/pull/217 . 6228.5 ms to 292.7 ms isn't too shabby.
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“Why I still recommend Julia”
The load times on some core packages were reduced by an order of magnitude this month. For example, RecursiveArrayTools went from 6228.5 ms to 292.7 ms. This was due to the new `@time_imports` in the Julia v1.8-beta helping to isolate load time issues. See https://github.com/SciML/RecursiveArrayTools.jl/pull/217 . This of course doesn't mean load times have been solved everywhere, but we now have the tooling to identify the root causes and it's actively being worked on from multiple directions.
dex-lang
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Thinking in an Array Language
A really nice approach to this I've seen recently is Google's research on [Dex](https://github.com/google-research/dex-lang).
- Function Composition in Programming Languages – Conor Hoekstra – CppNorth 2023 [video]
- Dex Lang: Research language for array processing in the Haskell/ML family
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Dex
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[D] PyTorch 2.0 Announcement
Have you tried Dex? https://github.com/google-research/dex-lang It is in a relatively early stage, but it is exploring some interesting parts of the design space.
- Mangle, a programming language for deductive database programming
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Looking for languages that combine algebraic effects with parallel execution
I think [Dex](https://github.com/google-research/dex-lang) might be along the lines of what you're looking for, although its focus is on SIMD GPU-style parallelism rather than thread-level parallelism.
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“Why I still recommend Julia”
Dex proves indexing correctness without a full dependent type system, including loops.
See: https://github.com/google-research/dex-lang/pull/969
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Haskell for Artificial Intelligence?
In case you want to see one research direction that's combining practical machine learning and functional programming, one of the authors of JAX (and the main author of its predecessor, Autograd) is writing Dex (https://github.com/google-research/dex-lang), a functional language for array processing. The compiler itself is written in Haskell. JAX is one of the most popular libraries for doing a lot of machine learning these days, along with Tensorflow and PyTorch. You might also want to see the bug in the JAX repo about adding Haskell support, for some context: https://github.com/google/jax/issues/185
What are some alternatives?
arrow-julia - Official Julia implementation of Apache Arrow
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
SciMLStyle - A style guide for stylish Julia developers
futhark - :boom::computer::boom: A data-parallel functional programming language
Lux.jl - Explicitly Parameterized Neural Networks in Julia
julia - The Julia Programming Language
ProtoStructs.jl - Easy prototyping of structs
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
ObjectOriented.jl - Conventional object-oriented programming in Julia without breaking Julia's core design ideas
hasktorch - Tensors and neural networks in Haskell
SciMLSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
CIPs