Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs

Abstract

In simulation-based studies and analyses of epidemics, a
major challenge lies in resolving the conflict between fidelity of models
and the speed of their simulation. Another related challenge arises
in dealing with the large number of what–if scenarios that need to be
explored. Here, we describe new computational methods that together
provide an approach to dealing with both challenges. A mesoscopic
modeling approach is described that strikes a middle ground between
macroscopic models based on coupled differential equations and microscopic
models built on fine-grained behaviors at the individual entity
level. The mesoscopic approach offers the ability to incorporate complex
compositions of multiple layers of dynamics even while retaining the
potential for aggregate behaviors at varying levels. It also is an excellent
match to the accelerator-based architectures of modern computing
platforms in which graphical processing units (GPUs) can be exploited
for fast simulation via the parallel execution mode of single instruction
multiple thread (SIMT). The challenge of simulating a large number of
scenarios is addressed via a method of sharing model state and computation
across a tree of what–if scenarios that are localized, incremental
changes to a large base simulation. A combination of the mesoscopic
modeling approach and the incremental what–if scenario tree evaluation
has been implemented in the software on modern GPUs. Synthetic
simulation scenarios are presented to demonstrate the computational
characteristics of our approach. Results from the experiments with large
population data, including USA, UK, and India, illustrate the modeling
methodology and computational performance on thousands of synthetically
generated what–if scenarios. Execution of our implementation
scaled to 8192 GPUs of supercomputing platforms demonstrates the
ability to rapidly evaluate what–if scenarios several orders of magnitude
faster than the conventional methods.