As computational capabilities increasingly outpace disk speeds on leading supercomputers, scientists will, in turn, be
increasingly unable to save their simulation data at its native resolution. One solution to this problem is to compress these
data sets as they are generated and visualize the compressed results afterwards. We explore this approach, specifically
subsampling velocity data and the resulting errors for particle advection-based flow visualization. We compare three
techniques: random selection of subsamples, selection at regular locations corresponding to multi-resolution reduction,
and introduce a novel technique for informed selection of subsamples. Furthermore, we explore an adaptive system which
exchanges the subsampling budget over parallel tasks, to ensure that subsampling occurs at the highest rate in the areas that
need it most. We perform supercomputing runs to measure the effectiveness of the selection and adaptation techniques.
Overall, we find that adaptation is very effective, and, among selection techniques, our informed selection provides the most
accurate results, followed by the multi-resolution selection, and with the worst accuracy coming from random subsamples.