prysm
nogil
Our great sponsors
prysm | nogil | |
---|---|---|
28 | 31 | |
234 | 2,853 | |
- | - | |
8.3 | 5.7 | |
15 days ago | 2 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
prysm
-
How to generate realistic PSFs for camera lenses?
My current concept is to just combine zernike polynomials with a random factor and calculate the PSF from that, which can be somewhat easily be done with the prysm library. These PSFs can then be convolved with circular and gaussian kernels for modelling additional defocus and accounting for other stuff like the AA filter. Then I'd add chromatic aberration by offseting/scaling the PSFs for each channel. Some generated kernels already look pretty good when comparing them to stars in astrophotography images, but others not so much.
- Prysm is a Python 3.6 library for numerical optics
-
Books/ other resources to learn about Fraunhofer diffraction farfield model using MATLAB/python?
https://github.com/brandondube/prysm (caveat emptor: mine)
- Demonstrations of laser optics/Fourier optics and diffraction simulations
-
Python raytracer optimizations and improvements
You can trace about 1 billion raysurfaces per second in pure python with CuPy, or a few million raysurfaces per second on CPU.
-
Exascale integrated modeling of low-order wavefront sensing and control for the Roman Coronagraph instrument
New paper from /u/BDube_Lensman using prysm to model NASA's Roman Coronagraph
-
Reccomended textbooks/reading for learning Thin Films
This free book is what this free code is based on
-
Options for free optical simulation?
Prysm Originally for diffraction type optics but seems to able to handle...everything? Performance as a priamary concern, GPU acceleration, proven JPL heritage :) Raytracing is however still experimental and without docs, generally whilst the library looks excellent if you're an optics person already I think I lack a bit of the base fundamental knowledge to really use it powerfully from just the API reference. I can see BDube has some raytracing example code in some of the issues I could probably adapt and muddle my way through at least. No guis is mildly annoying for a noob like myself, but I can work my way around matplotlib-ing just fine instead i'm sure.
-
Options for GPU accelerated python experiments?
You may want to steal my shim set since it lets you hot swap Numpy<-->cupy at runtime
-
Anaconda is so fucking broken!
I do computational diffraction with large manycore servers and GPUs at a FFRDC. The difference between MKL and not MKL is the difference between hitting enter and getting a result in an hour or two vs tomorrow.
nogil
- Proof-of-Concept Multithreaded Python Without the GIL
-
Our Plan for Python 3.13
This might be a dumb question, but why would removing the GIL break FFI? Is it just that existing no-GIL implementations/proposals have discarded/ignored it, or is there a fundamental requirement, e.g. C programs unavoidably interact directly with the GIL? I know that the C-API is only stable between minor releases [0] compiled in the same manner [1], so it's not like the ecosystem is dependent upon it never changing.
I cannot seem to find much discussion about this. I have found a no-GIL interpreter that works with numpy, scikit, etc. [2][3] so it doesn't seem to be a hard limit. (That said, it was not stated if that particular no-GIL implementation requires specially built versions of C-API libs or if it's a drop-in replacement.)
[0]: https://docs.python.org/3/c-api/stable.html#c-api-stability
[1]: https://docs.python.org/3/c-api/stable.html#platform-conside...
[2]: https://github.com/colesbury/nogil
[3]: https://discuss.python.org/t/pep-703-making-the-global-inter...
-
Real Multithreading Is Coming to Python
https://github.com/colesbury/nogil does manage to get rid of the GIL, but it's not certain to make it into Python core. The main problem is the amount of existing libraries that depend on the existence of the GIL without realizing it - breaking those would be extremely disruptive.
-
[D] The hype around Mojo lang
CPython is also investigating the removal of the GIL (PEP703, nogil). I think requiring the GIL is a wider thing that libraries will need to address anyway. But also, for the same reason as above I'd be surprised if the Modular team thought that saying "you can run all your python code unchanged" was a good idea if there was a secret "except for code that uses numpy" muttered under the breath.
- PEP 684 was accepted – Per-interpreter GIL in Python 3.12
- PEP 703 – Making the Global Interpreter Lock Optional in CPython
-
Python 3.11.0 final is now available
I'm worried about the speedup
My understanding is that it's based on the most recent attempt to remove the GIL by Sam Gross
https://github.com/colesbury/nogil
In addition to some ways to try to not have nogil have as much overhead he added a lot of unrelated speed improvements so that python without the gil would still be faster not slower in single thread mode. They seem to have merged those performance patches first that means if they add his Gil removal patches in say python 3.12 it will still be substantially slower then 3.11 although faster then 3.10. I hope that doesn't stop them from removing the gil (at least by default)
- Removed the GIL back in 1996 from Python 1.4, primarily to create a re-entrant Python interpreter.
- I Tried Removing Python's GIL Back in 1996
-
Faster CPython 3.12 Plan
Looks like it's still active to me:
https://github.com/colesbury/nogil/
What are some alternatives?
OpticSim.jl - Optical Simulation software
hpy - HPy: a better API for Python
poppy - Physical Optics Propagation in Python
mypyc - Compile type annotated Python to fast C extensions
numpy - The fundamental package for scientific computing with Python.
go-tfhe - 🐿️ Pure go implementation of TFHE Fully Homomorphic Encryption Scheme
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
pymae - Materials for the book "Python for Mechanical and Aerospace Engineering"
python-feedstock - A conda-smithy repository for python.
warp - A Python framework for high performance GPU simulation and graphics
sbcl - Mirror of Steel Bank Common Lisp (SBCL)'s official repository