jax-md
faster-cpython
| jax-md | faster-cpython | |
|---|---|---|
| 2 | 20 | |
| 1,093 | 937 | |
| - | - | |
| 7.5 | 0.0 | |
| 17 days ago | over 1 year ago | |
| Jupyter Notebook | ||
| Apache License 2.0 | - |
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jax-md
- JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
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PyTorch 2.0
On the other hand, there is just no MD implemented with PyTorch.
[1]: https://github.com/jax-md/jax-md
faster-cpython
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Faster CPython at PyCon, part two
It is unclear to me whether Python 3.12 will receive significant improvements. Based on the information from https://github.com/faster-cpython/benchmarking-public, it appears that there may be a 2% performance enhancement. Is this the anticipated result, or are there additional developments awaiting merger?
Initially, the "Shannon Plan" (https://github.com/markshannon/faster-cpython/blob/master/pl...) aimed for a 50% improvement with each release. Has this goal been deemed unattainable, or are there adjustments being made to the plan?
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Python-based compiler achieves orders-of-magnitude speedups
Yes, that's the JIT part of the plan. Sections of code will be compiled, "at runtime". Those sections of compiled code will be tied together with interpreted code. It will be somewhere between rare to impossible to have a fully compiled program, without interpreter glue.
- Faster-Cpython Plan.md
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A Team at Microsoft is Helping Make Python Faster
see: https://github.com/markshannon/faster-cpython/blob/master/plan.md
- Implementation plan for speeding up CPython
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Does Python plan to add JIT or get rid of the GIL?
Yes, the Shannon plan, which is actively being worked on by a team headed by Guido, includes JIT work in stages 3 and 4
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Python 3.11 is 25% faster than 3.10 on average
The goal with faster cpython is for small compounding improvements with each point release[0]. So in the end it should be much more than a tiny improvement.
[0] https://github.com/markshannon/faster-cpython/blob/master/pl...
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Python 3.11 Performance Benchmarks Are Looking Fantastic
The Shannon Plan. Announced by Guido at the 2021 Python Language summit, funded by Microsoft.
Well, good news then, it's in the planning!
- Why hasn't Python compiled/JIT/AHT projects gained mainstream traction?
What are some alternatives?
torchmd - End-To-End Molecular Dynamics (MD) Engine using PyTorch
cinder - Cinder is Meta's internal performance-oriented production version of CPython.
jaxonnxruntime - A user-friendly tool chain that enables the seamless execution of ONNX models using JAX as the backend.
pyenv-virtualenv - a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
ideas
jax-experiments
jax-md - Differentiable, Hardware Accelerated, Molecular Dynamics [Moved to: https://github.com/jax-md/jax-md]
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
Pyston - A faster and highly-compatible implementation of the Python programming language.
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
chruby - Changes the current Ruby