From Conditional Independence to Parallel Execution in Hierarchical Models

Abstract

Hierarchical models describe phenomena by grouping data into multiple levels. Due to the size of these models, parallel execution is required to avoid prohibitively long computing time. While it is occasionally possible to specify some of these models using parallel building blocks, this limits expressivity. Therefore, a more general generative specification is preferred. To leverage parallel computing capacity, these specifications can be annotated, but doing so effectively assumes that the modeler has expertise from computer science. This paper outlines how to identify parallel parts automatically by leveraging the conditional independence property in the graphical model extracted from the dataflow graph of model specifications. Computation related to random variables with the same depth in the graphical model are identified as candidates for parallel execution. Since subsequent proposals in the parameter space exploration of the model are clustered together, the results show that the well known longest processing time scheduling heuristic deals adequately with load imbalance. The proposed parallelization is evaluated on two pharmacometrics models, a domain where hierarchical models with load imbalance are common due to the numeric simulation of pharmacokinet-ics and pharmacodynamics of human subjects. The varying number of measurements taken per subject further exacerbates load imbalance.