Architecture‑Aware Modeling of Pedestrian Dynamics

Abstract

The spread of infectious diseases arises from complex interactions
between disease dynamics and human behavior. Predicting the
outcome of this complex system is difficult. Consequently, there has
been a recent emphasis on comparing the relative risks of different policy
options rather than precise predictions. Here, one performs a parameter
sweep to generate a large number of possible scenarios for human
behavior under different policy options and identifies the relative risks
of different decisions regarding policy or design choices. In particular,
this approach has been used to identify effective approaches to social
distancing in crowded locations, with pedestrian dynamics used to simulate
the movement of individuals. This incurs a large computational load,
though. The traditional approach of optimizing the implementation of
existing mathematical models on parallel systems leads to a moderate
improvement in computational performance. In contrast, we show that
when dealing with human behavior, we can create a model from scratch
that takes computer architectural features into account, yielding much
higher performance without requiring complicated parallelization efforts.
Our solution is based on two key observations. (i) Models do not capture
human behavior as precisely as models for scientific phenomena
describe natural processes. Consequently, there is some leeway in
designing a model to suit the computational architecture. (ii) The result
of a parameter sweep, rather than a single simulation, is the semantically
meaningful result. Our model leverages these features to perform
efficiently on CPUs and GPUs. We obtain a speedup factor of around 60
using this new model on two Xeon Platinum 8280 CPUs and a factor 125
speedup on 4 NVIDIA Quadro RTX 5000 GPUs over a parallel implementation
of the existing model. The careful design of a GPU implementation
makes it fast enough for real-time decision-making. We illustrate it on an
application to COVID-19.