Behavioral decision research faces countless combinations of theoretical, empirical, and methodological considerations. For example, there are numerous competing rational or heuristic theories of decision making under risk. Some theories feature many refinements, such as different mathematical utility functions. Some approaches treat variability in behavior as a nuisance, whereas others leverage it as a source of information. Scholars may consider individual, grouped, or pooled data, as well as apply different statistical methods. Modern computing resources can accelerate through-put of behavioral decision research by considering a huge number of such combinations in an effort to parse out a multitude of important theoretical questions on the same set of data. The presentation reviews the largest-scale such project to date, carried out on the National Science Foundation supported Extreme Science and Engineering Discovery Environment (XSEDE).