Note that the author himself is working on a framework for distributed differential dataflow computations (dataflow computations in which it is very cheap to make small updates to the inputs and see the differences in the outputs), and he has plans to apply the same metrics (comparing to a single-threaded implementation) to his own work once he gets a little further:

https://github.com/frankmcsherry/blog/blob/master/posts/2017...

I think that answering the "fastest way to do X in a cluster" question is only particularly interesting if X is something that you need to do in a cluster. But clearly they are testing X that is capable of being done on a mid-range laptop. There's a real question of whether the X that they are testing will actually scale to larger datasets than the laptop (or a decently beefy single node) could handle; it's entirely possible that the cluster solution will blow up at that point. And of course, if the single-threaded implementation is faster, then you could presumably just do "X in a cluster" by doing "X on a single node" and using the rest of the nodes to mine your CPU bound cryptocurrency of choice.