Implement merge shuffle algorithm for Numba

[zengraf]

This is an implementation of a parallel shuffling algorithm called Merge Sort and is a follow-up to #964.

The algorithm

1. It splits the input in halves until the slices have at most 0x100000 (1048576) elements
2. For each slice it runs in parallel the original Fisher-Yates algorithm, which has been moved to a separate function
3. It runs in parallel the merge operation from the paper

Please, note that merging step requires n * Math.ceil(log2(n / 1048576)) more random numbers

[slayoo]

Thank you, @zengraf!
CC: @piotrbartman

I suggest to:

• make it an option to use MargeShuffle (Collision dynamic __init__ method seems a good place to set it)
• raise a NotImplementedError() in the GPU backend if this option is chosen
• add a test which checks how it works with and without the new option set

[slayoo]

While looking at the changes today with @abulenok (trying to understand the reasons for the breakup test failures), we came up with the idea to create a unit test aimed at checking if the generated permutations have indeed a uniform distribution - would be a great addition!

[abulenok]

Hey @piotrbartman,
Originally in shuffle_local, we selected a random element from the whole array in each iteration.
https://github.com/atmos-cloud-sim-uj/PySDM/blob/ffea070f312b5e5491e988c97d3ee7924c9f80ea/PySDM/backends/impl_numba/methods/index_methods.py#L29
Should we select a random element from the i + 1 - start subarray, similar to how it is done in shuffle_global ?https://github.com/atmos-cloud-sim-uj/PySDM/blob/ffea070f312b5e5491e988c97d3ee7924c9f80ea/PySDM/backends/impl_numba/methods/index_methods.py#L21

[piotrbartman]

Yes, definitely. The current implementation of shuffle_local seems to be buggy and we don't have a uniform distribution there.

[slayoo]

@piotrbartman Thanks for following up!
@abulenok Thanks for spotting it and clarifying the issue here!
@zengraf Thanks for the new implementation - without it, the old bug would not be spotted!
I suggest to first add a new unit test that will fail for non-uniform permutation distributions.

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