TERAGRID ENABLED NATIONAL SCALE COMPUTATIONAL MODELS OF EPIDEMICS FOR MIDAS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 我们将开发和演示支持TeraGrid的流行病计算模型。该项目将允许在传染病代理研究(MIDAS)模型下开发的模型映射到大规模并行和分布式计算平台上，如TeraGrid。在早些时候的一项提案中，我们使用TeraGrid设施来支持国土安全部确定的政策研究，以准备应对流行病。目前的提议寻求开发的方法将大幅增加可模拟的实例的大小，同时保持相同的分辨率水平。为此，我们将使用TeraGrid环境开发第一个启用网格的EpiSims模拟实现。其目的是将区域流行病学模拟纳入国家规模的模型，强调适当使用网格技术。拟议的工作将使用Globus为网格计算提供低级原语。EpiSims是一个高度可扩展的分布式流行病学模拟系统。它已经成功地在各种HPC平台上进行了扩展，包括具有1000多个处理器的并行机。美国国立卫生研究院赞助的MIDAS项目的任务是开发全国范围的大流行预防建模工具。开发这类模型的一个重要问题是所需的分辨率和细节水平。EpiSims代表了一种非常详细的流行病学建模方法。它也是模块化的，并自然地映射到分布式计算体系结构上。这项研究提供了机会来探索非常详细、非常大型的模型的有用性，以及网格计算对这类模型的适用性。

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.