OPTIMIZATION/PARALLELIZATION OF BIOWAR

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者的研究机构。 保留字： 城市尺度多智能体网络模型 生化武器攻击 摘要： BioWar结合了来自社会科学、组织科学和人工智能的模型和数据，模拟了武器化疾病在数十万代理人（实际人口规模的20%-100%）中随时间变化的表现和传播。BioWar采用了一个独特而复杂的多代理仿真引擎，以真实地捕捉人口的反应，以用户指定的攻击场景。目前，在4x 1GHz AlphaEV6.7 SMP上使用4个系统线程对280，000个代理人群（60种流行疾病）进行为期2年的模拟可能需要10个挂钟小时。此外，每个模拟都由分布在4个配置文件中的50多个参数驱动，并生成20多列、多GB的数据流作为组成模拟器的随机模型的输出。因此，验证BioWar所需的计算和数据管理资源只有在超级计算级的设施中才是可行的。我们的目标是双重的：（1）了解我们如何使用TeraGrid作为一个计算平台来进行BioWar的持续验证;（2）获得使用TeraGrid存储架构来管理大量数据的经验，这些数据将来自执行数千次模拟（包括SRB和单个站点设施之间的交互）。

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. Keywords: city scale multi-agent network model weaponized biological attacks Abstract: BioWar combines models and data from socal science, organization science and artificial intelligence to simulate the time-varying manifestation and spread of weaponized diseases in a population of several hundred thousand agents (20%-100% of the actual population size). BioWar employs a unique and sophisticated multi-agentsimulation engine to realistically capture the reponse of acity population to a user-specified attack scenario. Currently, a 2-year simulation of a 280,000 agent population with 60 prevalent diseases can take up to 10wall-clock hours using 4 system threads on a 4x1GHz AlphaEV6.7 SMP. Furthermore, each simulation is driven by morethan 50 parameters distributed over 4 configuration files, and generates more than 20 multi-column, multi-gigabyte data streams as output from the stochastic models comprising the simulator. Hence, the computation and data management resourcesrequired to validate BioWar are only viable with supercomputing-class facilities. Our goals for this resource grant are two-fold: (1) to understand how we can use the TeraGrid as a computational platform for ongoing validation of BioWar; and (2) to gain experience using theTeraGrid storage architecture for managing the tremendous amount of data that will result from executing thousands ofsimulations (including the interaction between SRB and the individual site facilities).