DaemonMode.jl
JuliaInterpreter.jl
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DaemonMode.jl | JuliaInterpreter.jl | |
---|---|---|
22 | 5 | |
269 | 157 | |
- | 1.9% | |
4.7 | 7.7 | |
4 months ago | 16 days ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
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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.
DaemonMode.jl
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Potential of the Julia programming language for high energy physics computing
Thats for an entry point, you can search `Base.@main` to see a little summary of it. Later it will be able to be callable with `juliax` and `juliac` i.e. `~juliax test.jl` in shell.
DynamicalSystems looks like a heavy project. I don't think you can do much more on your own. There have been recent features in 1.10 that lets you just use the portion you need (just a weak dependency), and there is precompiletools.jl but these are on your side.
You can also look into https://github.com/dmolina/DaemonMode.jl for running a Julia process in the background and do your stuff in the shell without startup time until the standalone binaries are there.
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Julia 1.9.0 lives up to its promise
> If I were to use e.g. Rust with polars, load time would be virtually none.
Because you're compiling...
And if you need to do the same in Julia, you should also pre-compile or some other method like https://github.com/dmolina/DaemonMode.jl (their demo shows loading a database, with subsequent loads after the first one taking roughly ~0.2% of the first)
- Administrative Scripting with Julia
- GNU Octave 8.1
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Ask HN: Why is Julia so underrated?
Well, not nicely certainly, but:
https://github.com/dmolina/DaemonMode.jl
> portable
Neither is python - it just relies on universal availability. Over time…
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Is Julia suitable today as a scripting language?
You can get around a lot of these problems with DaemonMode.jl though.
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Julia performance, startup.jl, and sysimages
You might want DaemonMode.jl
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Can I execute code in Julia REPL if I'm connected to a remote server?
https://github.com/dmolina/DaemonMode.jl can possibly help in the future. Leaving it here so that people know this is planned.
- Ask HN: Why hasn't the Deep Learning community embraced Julia yet?
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Compile for faster execution?
If you strongly prefer to run scripts though, then you can use the package https://github.com/dmolina/DaemonMode.jl in order to re-use a Julia session between multiple scripts, saving you recompilation time.
JuliaInterpreter.jl
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Do you use Julia for general purpose tasks?
The projects page is a list of suggestions of projects that someone has already said they want to run. If you can find a mentor, you can submit a project for anything. For potential performance improvements, I'd look at https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/206, https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/312, and https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/314. I'm not sure if Tim Holy or Kristoffer have time to mentor a project, but if you're interested in doing a gsoc, ask around in the Julia slack/zulip, and you might be able to find a mentor.
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Julia 1.7 has been released
I would not go as far as calling it very naive, there has certainly been some work put into optimizing performance within the current design.
There are probably some gains to be had by using a different storage format for the IR though as proposed in [1], but it is difficult to say how much of a difference that will make in practice.
[1] https://github.com/JuliaDebug/JuliaInterpreter.jl/pull/309
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What's Bad about Julia?
You're right, done some more research and there seems to be an interpreter in the compiler: https://github.com/JuliaDebug/JuliaInterpreter.jl. It's only enabled by putting an annotation, and is mainly used for the debugger, but it's still there.
Still, it still seems to try executing the internal SSA IR in its raw form (which is more geared towards compiling rather than dynamic execution in a VM). I was talking more towards a conventional bytecode interpreter (which you can optimize the hell out of it like LuaJIT did). A bytecode format that is carefully designed for fast execution (in either a stack-based or register-based VM) would be much better for interpreters, but I'm not sure if Julia's language semantics / object model can allow it. Maybe some intelligent people out there can make the whole thing work, is what I was trying to say.
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Julia: faster than Fortran, cleaner than Numpy
It could, but that is a lot more work than it sounds. It might be easier to make it possible to swap out the compiler for one that is much faster (LLVM is slow but does good optimisations, other compilers like cranelift are faster but produce slower code). There is a Julia interpreter but it was written in Julia itself (it was written to support debuggers), so it doesn't really solve the latency issues.
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Julia: Faster than Fortran, cleaner than Numpy
If you need to run small scripts and can't switch to a persistent-REPL-based workflow, you might consider starting Julia with the `--compile=min` option. You can also reduce startup times dramatically by building a sysimg with PackageCompiler.jl
There is also technically an interpreter if you want to go that way [1], so in principle it might be possible to do the same trick javascript does, but someone would have to implement that.
[1] https://github.com/JuliaDebug/JuliaInterpreter.jl
What are some alternatives?
julia - The Julia Programming Language
Diffractor.jl - Next-generation AD
Makie.jl - Interactive data visualizations and plotting in Julia
Tullio.jl - ⅀
HTTP.jl - HTTP for Julia
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
Infiltrator.jl - No-overhead breakpoints in Julia
rust - Empowering everyone to build reliable and efficient software.
DataFramesMeta.jl - Metaprogramming tools for DataFrames
rust - Rust for the xtensa architecture. Built in targets for the ESP32 and ESP8266