artiq
StaticCompiler.jl
artiq | StaticCompiler.jl | |
---|---|---|
2 | 16 | |
403 | 471 | |
1.0% | - | |
9.6 | 6.9 | |
8 days ago | about 1 month ago | |
Python | Julia | |
GNU Lesser General Public License v3.0 only | GNU General Public License v3.0 or later |
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artiq
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Senior FPGA Engineer in quantum computing startup, Oxfordshire UK
At Oxford Ionics we're looking for a senior FPGA engineer to work on our ARTIQ-based experimental control system and build our FPGA team. We're using Migen HDL and Python and software engineering knowledge are highly desirable. No prior quantum computing knowledge is required!
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Show HN: prometeo โ a Python-to-C transpiler for high-performance computing
No, I mean nanosecond and picosecond precision real-time systems. Exhibit A: https://github.com/m-labs/artiq
StaticCompiler.jl
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Potential of the Julia programming language for high energy physics computing
Yes, julia can be called from other languages rather easily, Julia functions can be exposed and called with a C-like ABI [1], and then there's also various packages for languages like Python [2] or R [3] to call Julia code.
With PackageCompiler.jl [4] you can even make AOT compiled standalone binaries, though these are rather large. They've shrunk a fair amount in recent releases, but they're still a lot of low hanging fruit to make the compiled binaries smaller, and some manual work you can do like removing LLVM and filtering stdlibs when they're not needed.
Work is also happening on a more stable / mature system that acts like StaticCompiler.jl [5] except provided by the base language and people who are more experienced in the compiler (i.e. not a janky prototype)
[1] https://docs.julialang.org/en/v1/manual/embedding/
[2] https://pypi.org/project/juliacall/
[3] https://www.rdocumentation.org/packages/JuliaCall/
[4] https://github.com/JuliaLang/PackageCompiler.jl
[5] https://github.com/tshort/StaticCompiler.jl
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Julia App Deployment
PackageCompiler, but it' s a fat runtime and not cross compile. A thin runtime is currently not possible without sacrifices for feature as https://github.com/tshort/StaticCompiler.jl.
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JuLox: What I Learned Building a Lox Interpreter in Julia
https://github.com/tshort/StaticCompiler.jl/issues/59 Would working on this feasible?
- Making Python 100x faster with less than 100 lines of Rust
- What's Julia's biggest weakness?
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Size of a "hello world" application
I just read the project's documentation at https://github.com/tshort/StaticCompiler.jl. It does produce a "hello world" application that is only 8.4k in size ๐. I do like that it can work on Mac OS. Hopefully Windows support will come soon.
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Why Julia 2.0 isnโt coming anytime soon (and why that is a good thing)
See https://github.com/tshort/StaticCompiler.jl
- My Experiences with Julia
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Julia for health physics/radiation detection
You're probably dancing around the edges of what [PackageCompiler.jl](https://github.com/JuliaLang/PackageCompiler.jl) is capable of targeting. There are a few new capabilities coming online, namely [separating codegen from runtime](https://github.com/JuliaLang/julia/pull/41936) and [compiling small static binaries](https://github.com/tshort/StaticCompiler.jl), but you're likely to hit some snags on the bleeding edge.
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We Use Julia, 10 Years Later
using StaticCompiler # `] add https://github.com/tshort/StaticCompiler.jl` to get latest master
What are some alternatives?
quantumcat - quantumcat is a platform-independent, open-source, high-level quantum computing library, which allows the quantum community to focus on developing platform-independent quantum applications without much effort.
julia - The Julia Programming Language
py2many - Transpiler of Python to many other languages
PackageCompiler.jl - Compile your Julia Package
acados - Fast and embedded solvers for nonlinear optimal control
PhysAI - PhysAI is an open-source AI project that aims to link quantum mechanics and general relativity by generating, testing, and improving physical equations. It leverages machine learning, integrates with existing research, generates LaTeX documents, and encourages collaborative learning. It relies on community-driven contributions to improve accuracy.
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
cqasm_development_interface - Framework for writing and running cQASM files against any Quantum Inspire's emulator backend via their API
oneAPI.jl - Julia support for the oneAPI programming toolkit.
qutrunk - QuTrunk is free, open source, cross platform quantum computing programming framework, including quantum programming API, quantum command translation, quantum computing back-end interface, etc
LoopVectorization.jl - Macro(s) for vectorizing loops.