TERAGRID ENABLED NATIONAL SCALE COMPUTATIONAL MODELS OF EPIDEMICS FOR MIDAS

这个子项目是众多研究子项目之一

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We will develop and demonstrate TeraGrid enabled computational models of epidemics. The project will allow models developed under the Models of Infectious Disease Agent Study (MIDAS) to be mapped onto massively parallel and distributed computing platforms such as TeraGrid. In an earlier proposal, we used TeraGrid facilities to support DHHS defined policy study to prepare against pandemics. The current proposal seeks to develop methods that will substantially increase the size of instances that can be simulated while keeping the same level of resolution. To this end, we will develop the first Grid-enabled implementation of EpiSims simulation using the TeraGrid environment. The purpose is to compose regional epidemiological simulations into national scale models emphasizing the appropriate use of grid technology. The proposed effort will use Globus to provide low level primitives for grid computing. EpiSims is a highly scalable distributed epidemiological simulation system. It has been successfully shown to scale on a variety HPC platforms including parallel machines with 1000+ processors. The NIH sponsored MIDAS program has been tasked to develop national scale modeling tools for pandemic preparedness. An important question in developing such models is the required level of resolution and detail. EpiSims represents a highly detailed approach to epidemiological modeling. It is also modular and maps naturally on distributed computing architectures. This study offers the opportunity to explore both the usefulness of very detailed, very large models, and the appropriateness of grid computing for models of this kind.