Paper

Bayesian inference enables greater scientific insight into simulation models, determining model parameters and meaningful confidence regions from observed data. With hierarchical methods like Multilevel Delayed Acceptance (MLDA) drastically reducing compute cost, sampling Bayesian posteriors for computationally intensive models becomes increasingly feasible. Pushing MLDA towards the strong scaling regime (i.e. high compute resources, short time-to-solution) remains a challenge: Even though MLDA only requires a moderate number of high-accuracy simulation runs, it inherits the sequential chain structure and need for chain burn-in from Markov chain Monte Carlo (MCMC). We present fully asynchronous parallel prefetching for MLDA, adding an axis of scalability complementary to forward model parallelization and parallel chains. A thorough scaling analysis demonstrates that prefetching is advantageous in strong scaling scenarios. We investigate the behavior of prefetching MLDA in small-scale test problems. A large-scale geophysics application, namely parameter identification for non-linear earthquake modelling, highlights interaction with coarse-level quality and model scalability.

Agent-based simulation provide a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). A typical approach to UQ within these types of models is to run multiple realizations of the model, then compute aggregate statistics upon completion. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population, yielding accurate estimates of the spread of a disease to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Our method addresses two primary limitations of ABMs: lack of UQ and inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with the rigorous UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.