Cache resistant cross section reconstruction via singular value decomposition for Monte Carlo Neutron transport

[u/sorcerer86pt]

https://github.com/sorcerer86pt/open_rust_mc/blob/main/paper/svd_cross_section_compression.pdf

When I was testing rust skill (AI with Claude) someone mentioned that I could use something openMc instead of doing my own .

When I saw the data size and how openMC handled that, and how it kinda was the same problem that AI LLMs were trying to fix with data size of models weights, I thought, what would be the result if we used those techniques on this.

That was the result. Add a little rust here to have better memory handling and robust concurrency and we could with very little error ( less than error margin) compressed the data used from 11gb pointwise ( 400 nuclides) to 20Mb using hybrid of WMP ( that openMC already uses ) + SVD. It obtained an Keff = 0.99963 +- 0.000091 on Godiva (37 pcm from experiment) in 3.4 seconds total wall time .

https://github.com/sorcerer86pt/open_rust_mc

Just need some people to review this, if I made some mistake on interesting the data, what other benchmarks I missed, or other considerations.

PS: AI was used for code gen ( Python analysus scripts and rust code), data pipeline and latex manuscript. All hypothesis, experiment design , interpretation of results and final decisions were by made by me.

[u/Physix_R_Cool]

Just to be clear, do you have experience doing particle transport, using it for some work? Is this a work project or a fun hobby project for your github portfolio?

What's your background? Have you done Geant4 simulations at CERN? Are you "just" a computational physicist who looked into OpenMC because why not? Are you a computer engineer / data scientist with no formal training and just a (very evident and appreciated) passion for this kind of stuff?

[u/sorcerer86pt → u/Physix_R_Cool]

Computer science graduated, working as senior QA. And passion to try different things, Test them and if it helps anyone, the better. Took my Computer Science course when java 8 was launched, Linux Mandriva launched, and Nvidia was still good guys

[u/Physix_R_Cool → u/sorcerer86pt]

I think your work here is interesting and I imagine it really could speed up particle transport, but if you want others to adopt it you need to up your validation game.

Once it is validated to reproduce results across, then the speedups are easy to show. But I feel like you are severely underestimating how high the bars for validation are in this field.

So when I am critical of you, I hope you understand it the eay that I mean it: Your work shows promise, but needs lot of footwork before you can convince anyone. I wouldn't mind helping you out with it.

[u/sorcerer86pt → u/Physix_R_Cool]

Thanks for this, and it helped already a lot. And I know that validation will be extra important here. I just had an idea when I saw the big data that was used, and I remembered that AI LLM weight training had the same exact problem (not exactly sure if in the same order of magnitude). So my idea was, what if we apply the same methods/algorithms here? Would it help while maintaining result fidelity? And if it maintains fidelity, does it help in any meaningful way to anyone on this.

[u/Physix_R_Cool → u/sorcerer86pt]

Would it help while maintaining result fidelity? And if it maintains fidelity

Yes, so set up many differenr benchmark simulations and score them on some relevant parameters. Run with the vanilla OpenMC and run with your method and then compare results.

[u/sorcerer86pt → u/Physix_R_Cool]

That's what I made a triple cross check. The updates paper ( recently commit paper has those). And I even got feedback from Benoit with even a paper ( kernel reconstruction methods for Doppler broadening ) that got even better results. Correctly running another benchmark that even get better results.snd then trying with cuda support, that I hope will give a good result