cij VS fbpic

Software from our group (cij[1], qha[2]) were developed when numba seems to be the best option for JIT. It generates more pain in the hindsight. It generates a lot of depreciated warning due to unstable API, locked numpy to a certain version (i remember 1.21) due to compatibility issues, and when M1 Mac comes out, there were for a long time lack of llvmlite porting to the new platform, so cannot run on these new Macs.

If I had to do it again I would just use plain numpy or use the JAX from Google if JIT is really necessary.

[1]: https://github.com/MineralsCloud/cij

[2]: https://github.com/MineralsCloud/qha

When I wrote my bachelor thesis years back I worked on a particle-in-cell code [1] that makes heavy use of numba for GPU kernels. At the time it was the most convenient way to do that from python. I remember spending weeks to optimizing these kernels to eek out every last bit of performance I could (which interestingly enough did eventually involve using atomic operations and introducing a lot of variables[2] instead of using arrays everywhere to keep things in registers instead of slower caches).

I remember the team being really responsive to feature requests back then and I had a lot of fun working with it. IIRC compared to using numpy we managed to get speedups of up to 60x for the most critical pieces of code.

[1]: https://github.com/fbpic/fbpic

We used numba to accelerate the code and most importantly write GPU kernels for the heavy parts. I remember spending hours optimising my code to eek out the most performance possible (which eventually meant using atomics and manually unrolling many loops because somehow this was giving us the best performance) but honestly I was really happy that I didn't need to write cuda kernels in C and generally it was pretty easy to work with. I remember back then the documentation was sometimes a little rough around the edges but the numba team was incredibly helpful and responsive. Overall I had a great time.

[0] https://github.com/fbpic/fbpic