Doing computations at scale allows a researcher to test many different variables at once, thereby shorter time to outcomes, and also provides the ability to ask larger, more complex problems (i.e., larger data sets, longer simulation time, more degrees of freedom, etc.).  Researchers can take advantage of the scale of the cluster by setting up workflows to split many different tasks into large batches, which are scheduled across the cluster at the same time.