| toast | ideas | |
|---|---|---|
| 3 | 81 | |
| 43 | 1,656 | |
| - | 0.5% | |
| 1.5 | 7.3 | |
| 10 days ago | 2 days ago | |
| C++ | ||
| GNU General Public License v3.0 or later | - |
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toast
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How Many Lines of C It Takes to Execute a and B in Python?
I have a real life example in this commit: https://github.com/hpc4cmb/toast/pull/380/commits/a38d1d6dbc...
Replacing 2 lines of python code (with tens of glue code in Numba) with hundreds lines of C++ with glue code.
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C++ is making me depressed / CUDA question
If you just want to do a matrix multiplication with CUDA (and not inside some CUDA code), you should use cuBLAS rather than CUTLASS (here is some wrapper code I wrote and the corresponding helper functions if your difficulty is using the library rather than linking it / building), it is a fairly straightforward BLAS replacement (it can be a pain to install but that is life with C++/nvidia).
- A new programming language for high-performance computers
ideas
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Type information for faster Python C extensions
Lower latency native calls in Python would be extremely useful, thank you for your work! Is the following GitHub issue the right place to monitor progress? https://github.com/faster-cpython/ideas/issues/546
I'm open to doing some benchmarking. Several of my libraries have pure CPython bindings (StringZilla, UCall, SimSIMD), and all perform low-latency SIMD-accelerated ops, so might be a good testing ground :)
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How Many Lines of C It Takes to Execute a and B in Python?
Recent CPython development has been towards optimizations and addressing use cases that benefit from optimizations, some coming from the faster CPython initiative. You might just get your JIT[1].
[1] https://github.com/faster-cpython/ideas/wiki/Workflow-for-3....
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GIL removal and the Faster CPython project
The faster-cpython folks seem to be working towards a JIT (https://github.com/faster-cpython/ideas/tree/main/3.13) and both pyston and cinder have JITs. So I don't think anyone has ruled one out.
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Our Plan for Python 3.13
faster-cpython team has done a lot of work to experiment on it: https://github.com/faster-cpython/ideas/issues/485#issuecomm...
It kind of sounds like migration to register based is a foregone conclusion, but it's not very clear to me.
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Faster CPython at PyCon, part two
lots of big ideas are still remaining to be done. One example is the register based interpreter, see https://github.com/faster-cpython/ideas/issues/485
A previous plan called for the beginning of a JIT in 3.12, seen as "Trace optimized interpreter" here: https://github.com/faster-cpython/ideas/wiki/Workflow-for-3....
- EdgeDB – A graph-relational database built on top of Postgres
- Python 3.12 Nogil Benchmark
What are some alternatives?
anydsl - Meta project to quickly build dependencies
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
Rust-CUDA - Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.
faster-cpython - How to make CPython faster.
atl - A Tensor Language
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
nalgebra - Linear algebra library for Rust.
pyenv-virtualenv - a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)
Halide - a language for fast, portable data-parallel computation
jnumpy - Writing Python C extensions in Julia within 5 minutes.
verified-scheduling
nogil - Multithreaded Python without the GIL