Dagger.jl
JET.jl
Dagger.jl | JET.jl | |
---|---|---|
4 | 13 | |
581 | 692 | |
1.7% | - | |
8.9 | 9.0 | |
4 days ago | 14 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Dagger.jl
- Dagger: a new way to build CI/CD pipelines
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DTable a new distributed table implementation in Julia using Dagger.jl
Firstly, I'll say that we already have work started to implement out-of-core directly in Dagger: https://github.com/JuliaParallel/Dagger.jl/pull/289.
With that PR in place, it should be possible to define a "storage device" which is backed by a database. I haven't had a chance to actually try this, since the PR still needs quite some work and testing, but it's definitely something on my radar!
- From Julia to Rust
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Cerebras’ New Monster AI Chip Adds 1.4T Transistors
I'm not sure that's necessarily the domain of a low-level package like CUDA.jl though (which I assume you're referring to). That kind of interface is more the domain of higher-level packages like https://github.com/JuliaParallel/Dagger.jl/ and to a lesser extent https://juliagpu.github.io/KernelAbstractions.jl/stable/. Moreover, the jury is still out on whether the built-in Distributed module is an ideal abstraction for every use-case (clusters, heterogeneous compute, etc.)
WRT Nx, my biggest question is how they'll crack the problem of still needing big balls of C++ and the shims everywhere to get acceleration. Creating a compiler that generates efficient GPU or other accelerator code is a massive research project with no clear winners, never mind the challenge of reconciling the very mutation-heavy needs of GPU compute with a mostly immutable language model.
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?
earthly - Super simple build framework with fast, repeatable builds and an instantly familiar syntax – like Dockerfile and Makefile had a baby.
julia - The Julia Programming Language
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
DuckDB.jl
Metatheory.jl - General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
StaticArrays.jl - Statically sized arrays for Julia
HTTP.jl - HTTP for Julia
Symbolics.jl - Symbolic programming for the next generation of numerical software
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia