This research investigates techniques for efficient simulation of large scale agent-based models (ABMs). ABMs are increasingly being used to understand complex multi-scale behaviors in many natural, built and social systems. Although ABMs have the necessary structure to capture complex model characteristics in these systems, this very structure makes them computationally challenging. Current techniques for desktop computing and extensions to traditional high performance computing are incapable of efficiently handling this computational complexity. This has severely limited the applicability of ABMs. This research investigates novel techniques designed to leverage the massive computing power available on commodity graphics processing units (GPUs). It greatly expands the availability and applicability of agent-based modeling by effectively democratizing super computing for ABM simulation. Furthermore, it enables virtual testing of "what-if" scenarios in public policy, contingency planning for disaster relief, drug therapy design etc., on inexpensive desktop computers at realistic levels of detail.
The main challenge in this research is the re-formulation of ABM computation tofit the data-parallel model of GPUs. Specific research topics include representation of agent data, functions for agent motion, replication, decimation, communication, representation and manipulation of non-spatial agent networks, adaptive behaviors, run-time user interaction, fast visualization, hardware and model-aware automatic code optimization, and multi-level parallelism for multi-GPU platforms. The techniques developed are being applied to two specific problems in medicine: simulation of tuberculosis and systemic inflammatory response syndrome. These models enable efficient simulation of disease pathology and in-silico testing of novel therapeutic drug protocols. Educational topics include development of courses, outreach to K-12 students through development of ABM themed video games, undergraduate involvement in research, and the development of a comprehensive dissemination web page.
