OP here, sorry for not logging in for my original post.
Thank you all for your helpful comments and suggestions.
I wrote a threaded solution along the lines of
bot403's example. One of the hurdles I had to overcome was that my original program was quite sequential: the order of data subset creation also determined the order in which they were processed and dumped into tables. This was simple to code and for this scheme, the statistics part did not need to know which data subset it was working on.
However, in the multithreaded program data processing is asynchronous, so I had to label each dataset to mark where they belong.
BrowserUk's suggestion about using ssh to communicate with R processes on other machines was also helpful, as this is quite easy to implement. There is one practical problem with this: I can only use computers where I have shell access - so my colleagues' Windows boxes are not eligible.
About the other type of solutions outlined above - I might implement something like that if the present version dows not prove to be efficient enough.
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