I am working with small data sets of N elements, usually with N = 8, 16, or 32 elements; all are positive 64-bit float numbers.  I need to identify the smallest N/2 elements.  It is not required that the smallest N/2 elements be sorted, just that I identify which ones they are from among the group of N.  Each program run must perform this sort up to a billion times, so sort speed improvements could generate useful returns for me.  Right now I am using a simple Quicksort that orders all N elements just to prove out my concept.  It is quite likely that my data are not randomly distributed, but I am not yet at the point where I can get a handle on that.Eventually, the grand design is that this will find its way into the real world via an X-code implementation.Any recommendations as to what my best sorting strategy ought to be?
Best sort approach for small data sets
algorithms;sorting;efficiency