Granular cloning: Intra-object parallelism in ensemble studies

Many runs of a computer simulation are needed to model uncertainty and evaluate alternate design choices. Such an ensemble of runs often contains many commonalities among the different individual runs. Simulation cloning is a technique that capitalizes on this fact to reduce the amount of computation required by the ensemble. Granular cloning is proposed that allows the sharing of state and computations at the scale of simulation objects as small as individual variables, offering savings in computation and memory, increased parallelism and improved tractability of sample path patterns across multiple runs. The ensemble produces results that are identical to separately executed runs. Whenever simulation objects interact, granular cloning will resolve their association to subsets of runs though binary operations on tags. Algorithms and computational techniques required to efficiently implement granular cloning are presented. Results from an experimental study using a cellular automata-based transportation simulation model and a coupled transportation and land use model are presented providing evidence the approach can yield significant speed ups relative to brute force replicated runs.