Non-uniform structors of arrays in numba

[jpmorgan98]

Optimizations for how cross section data, tally meshes, and cells are stored. Right now with the np.ndarry the size of allocated memory for each item is that of the largest array and the same for all others. numba.jitclass is a remedy for this but is not GPU operable. Some initial ideas are

• numba.jitclass with a puller function for GPU runs to nd.arrays
• An offset scheme where data is stored as a single dim array with offsets
• Use of other c-type data structures like pytorch or cupy

[jpmorgan98]

@ilhamv how urgent is you're need to move towards jitclass?

[ilhamv]

@jpmorgan98 Per our discussion with Braxton yesterday, it sounds like it is easier for the GPU-Numba work, if we stick with the numpy structured array than if we move to use Jitclass, at least for now, perhaps until the first working GPU merge. But what do you think, @braxtoncuneo ?

[ilhamv]

Has this been resolved, @jpmorgan98 ?