Improvement of unbalanced datasets in multiprocessing

[maxgalli]

As it was noticed during the last benchmark tests run, the treatment of unbalanced datasets is suboptimal when running with multiprocessing enabled if one of the RDataFrames is built on top of a dataset whose size is much bigger than the others, the worker that process it end up creating a bottleneck for the entire analysis. Several ways (to be investigated and implemented separately) can fix this issue:

• combine the usage of multiprocessing and multithreading: detect in advance the larger datasets and split the workers that get to process these into multiple threads; in order not to increase the number of cores used, the overall number of workers decreases;
• using only multiprocessing: detect in advance the larger datasets and split them into different RDataFrames, so that they are taken by different workers; the results can be easily merged at the end to get the proper histograms; this solution also requires something to check that the largest RDataFrames are the first ones sent to the workers.