Efficient execution of computation modules in a model with massive data

Models and simulations for analyzing difficult real-world problems often deal with massive amounts of data. The data problem is compounded when the analysis must be repeatedly run to perform what-if analyses. One such model is the Integrated Consumable Item Support (ICIS) Model developed for the U.S. Defense Logistics Agency (DLA). It models DLA´s ability to satisfy future wartime requirements for parts, fuel, and food. ICIS uses a number of computation modules to project demands, model sourcing, and identify potential problem items for various commodities and military services. These modules are written in a variety of computer languages and must work together to generate an ICIS analysis.