Exploring Scientific Discovery with Large-Scale Parallel Scripting

Scientists and the organizations that fund scientific research frequently face difficult questions about how to allocate scarce resources. Should they pursue safe avenues of investigation that incrementally extend current knowledge? Or should they pursue ideas that are far off the beaten track, which are less likely to bear fruit, but more likely to provide revolutionary insights? One group at the University of Chicago [4] is trying to provide some insight by developing a model of scientific discovery and exploring what parameters match real world data, and what parameters maximize knowledge production. This demo will describe the methods we used to take a a sequential simulation of scientific discovery and build an optimization algorithm that scales to thousands of cores. 2 Computing Requirements We use a simulated annealing algorithm to optimize parameters for the model. The objective function being evaluated is a measure of scientific innovation that is determined by running an ensemble of randomized simulations and averaging the results. At each step of the algorithm a parameter is perturbed randomly, and the model rerun to determine the new value of the objective function, as shown in Figure 1a. The new parameter value may be accepted or rejected based o