prysm VS ideas

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.

https://github.com/brandondube/prysm (caveat emptor: mine)

You can trace about 1 billion raysurfaces per second in pure python with CuPy, or a few million raysurfaces per second on CPU.

New paper from /u/BDube_Lensman using prysm to model NASA's Roman Coronagraph

This free book is what this free code is based on

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.

You may want to steal my shim set since it lets you hot swap Numpy<-->cupy at runtime

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.

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 :)

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....

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.

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.

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....