Juleps
JET.jl
Juleps | JET.jl | |
---|---|---|
2 | 13 | |
67 | 693 | |
- | - | |
0.0 | 9.0 | |
almost 4 years ago | 23 days ago | |
Julia | ||
GNU General Public License v3.0 or later | MIT License |
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Juleps
JET.jl
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Prospects of utilising Rust in scientific computation?
An informative discussion on julia forum. Have you tried using https://github.com/aviatesk/JET.jl to minimize type instabilities?
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Julia v1.9.0 has been released
For instance, https://github.com/aviatesk/JET.jl is still in its relative infancy, but it's played a big role in detecting quite a few potential bugs that had never been reported to use by users or caught in our testing infrastructure. There's also been a lot developments like interfaces to RR the time travelling debugger https://rr-project.org/ which helps us better understand and catch some very hard to debug non-deterministic bugs.
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Julia Computing Raises $24M Series A
Have you seen Shuhei Tadowaki's work on JET.jl (?)
If you're curious: https://github.com/aviatesk/JET.jl
This may seem more about performance (than IDE development) but Shuhei is one of the driving contributors behind developing the capabilities to use compiler capabilities for IDE integration -- and indeed JET.jl contains the kernel of a number of these capabilities.
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I Hate Programming Language Advocacy (2000)
This is sort of being done right now, as dynamic languages have begun to adopt gradual typing... at least Python and Julia, that I know of.
If something like [JET.jl](https://github.com/aviatesk/JET.jl) become ubiquitous in Julia, one could add a function that pointed out all the places in the code where types are not fully inferred by the compiler.
It'll never be quite the same level of safety as a static language, however.
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From Julia to Rust
- Pattern matching (sometimes you don't want the overhead of a method lookup)
[1]: https://github.com/aviatesk/JET.jl
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Julia is the best language to extend Python for scientific computing
You can use the `@code_warntype` macro to check for type stability, which is very helpful for detecting such performance pitfalls on single function level. In the future, https://github.com/aviatesk/JET.jl may give a more powerful way to do it.
- Jet.jl: experimental type checker for Julia
- Jet.jl: A WIP compile time type checker for Julia
What are some alternatives?
DIPs - D Improvement Proposals
julia - The Julia Programming Language
Catlab.jl - A framework for applied category theory in the Julia language
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
IRTools.jl - Mike's Little Intermediate Representation
Metatheory.jl - General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
SumTypes.jl - An implementation of Sum types in Julia
StaticArrays.jl - Statically sized arrays for Julia
glow - Compiler for Neural Network hardware accelerators
HTTP.jl - HTTP for Julia
proposals - ✍️ Tracking the status of Babel's implementation of TC39 proposals (may be out of date)
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia