I have a large number of $N$ (20770) measurements. I need to perfom a calculation on all of them, but this is computationally too expensive. Therefore, I am looking for a way to select a subset of $p$ (~300) measurements. Then, I can use any of those $N$ measurements, look which one of the subset $p$ is closest, and use the calculation for this reference measurement; like a lookup-table. The aim of the subset is thus not to be representative of the full set, but to span the entire range in each relevant dimension.
I can think of several ways to establish this subset (such as using EOFs to maximise variability, or something along the lines of Chevallier et al. (2006) but the latter lacks algorithm details), but I don't want to spend months on this, as it is not essential that it is done in the best possible way; a simpler but sub-optimal way is highly preferrable. A simple way I can think of is to bucket my measurements based on $d$ dimensions (say $d=6$). Then, I can lookup which reference measurement fits in the same $d$-dimensional bucket as the current measurement, and use the calculation for that reference measurement.
My fear is that I am reinventing the wheel and spending time on developing a poor homegrown solution to a problem that has well-established solutions along with implementation in popular languages. That brings me to my question:
Is the problem described above one encountered commonly enough so that there exists: (1) a name and literature on alternative solutions, and (2) open-source implementations of those solutions in popular packages, in particular for Python?