Bill Fellows has an interesting approach to bucketing tables into groups of similar size:
You need to do something to all of the tables in SQL Server. That something can be anything: reindex/reorg, export the data, perform some other maintenance—it really doesn’t matter. What does matter is that you’d like to get it done sooner rather than later. If time is no consideration, then you’d likely just do one table at a time until you’ve done them all. Sometimes, a maximum degree of parallelization of one is less than ideal. You’re paying for more than one processor core, you might as well use it. The devil in splitting a workload out can be ensuring the tasks are well balanced. When I’m staging data in SSIS, I often use a row count as an approximation for a time cost. It’s not perfect – a million row table 430 columns wide might actually take longer than the 250 million row key-value table.
Click through for the script. For the R version, this Stack Overflow post shows how to do it with cumulative sums and the cut function.