Data-parallel techniques for simulating a mega-scale agent-based model of systemic inflammatory response syndrome on graphics processing units

Authored by Samuel Alberts, Michael K. Keenan, Roshan M. D'Souza

Date Published: 2012-08

DOI: 10.1177/0037549711425180

Sponsors: United States National Science Foundation (NSF)

Platforms: C++ NetLogo

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Agent-based modeling is increasingly being used for computer simulation of complex biological systems. An agent-based model (ABM) is a bottom-up simulation where the bulk dynamics of the model result from the local interactions of its individual constituents or agents. However, due to emergent qualities of ABMs, bulk behaviors may be sensitive to the size of the model as determined by the population of individuals. Therefore, in certain circumstances it may be critical to closely match the simulation size with the actual system. This may be particularly true in biological systems, where multiple large-scale heterogeneous populations can range into millions or even billions of individual cells/agents. Most existing ABM simulation toolkits are designed for serial computing and cannot effectively simulate such mega-scale systems from a run-time standpoint. In this paper, we investigate data-parallel ABM implementations on graphics processing units to address the scalability issue of ABMs. As an example, we have implemented an abstracted version of the Systemic Inflammatory Response Syndrome ABM. We also implemented a serial version to confirm statistical accuracy. Our results show that parallelization on graphics processing units offers a substantial gain in performance without a loss in accuracy.

Tags

Agent-based modeling systems biology GPGPU