artiq
prometeo
artiq | prometeo | |
---|---|---|
2 | 11 | |
403 | 610 | |
1.0% | - | |
9.6 | 0.0 | |
8 days ago | almost 2 years ago | |
Python | Python | |
GNU Lesser General Public License v3.0 only | BSD 2-clause "Simplified" License |
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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 – 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
prometeo
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Borgo is a statically typed language that compiles to Go
Not impossible but I guess you might end up with an extra runtime layer and some more dynamic operations will not be very fast. Or you restrict it to a subset of Python like this project does: https://github.com/zanellia/prometeo
You could of course write a bytecode VM in Golang but I guess that defeats the purpose.
- Are there any libraries that can easily convert Python to C/C#/or C++? Ones where a person doesn't have to "calibrate" it, just, pip install library and then they can have their Python code in C,C#,or C++?
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I made a Python compiler, that can compile Python source down to fast, standalone executables.
Honest question: How does pycom compare to similar tools like Nuitka, prometeo, or mypyc?
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Profiling and Analyzing Performance of Python Programs
If you don't mind switching to a little different syntax of Python, then you also might want to take a look at prometeo - an embedded domain specific language based on Python, specifically aimed at scientific computing. Prometeo programs transpile to pure C code and its performance can be comparable with hand-written C code.
- GitHub - zanellia/prometeo: An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
- Show HN: Prometeo – a Python-to-C transpiler for high-performance computing
- An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
This is awesome! The direction of using a subset of python, while leveraging the user base and static typing to accomplish some other everyday task in a different language is very legit IMO.
I took a cursory look at:
https://github.com/zanellia/prometeo/blob/master/prometeo/cg...
It seems quite similar in spirit to
https://github.com/adsharma/py2many/blob/main/pyrs/transpile...
I'm not spending much time on py2many last few months (started a new job). Let me know if any of it sounds useful - especially the ability to transpile to 7-8 languages including Julia, C++ and Rust.
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.
Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
py2many - Transpiler of Python to many other languages
llvm-cbe - resurrected LLVM "C Backend", with improvements
acados - Fast and embedded solvers for nonlinear optimal control
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
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.
textX - Domain-Specific Languages and parsers in Python made easy http://textx.github.io/textX/
cqasm_development_interface - Framework for writing and running cQASM files against any Quantum Inspire's emulator backend via their API
MatrixEquations.jl - Solution of Lyapunov, Sylvester and Riccati matrix equations using Julia