OPTIMIZATION/PARALLELIZATION OF BIOWAR

点击翻译按钮获取中文摘要

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).