Data farming: discovering surprise

The development of models and analysis of modeling results usually requires that models be run many times. Very few modelers are satisfied with the computing resources available to do sensitivity studies, validation and verification, measurement of effectiveness analysis, and related necessary activities. Fortunately, high performance computing, in the form of distributed computing capabilities and commodity node systems, is becoming more pervasive and cost effective. In this paper the authors describe the concept and methods of data farming, the study and development of methods, interfaces, and tools that make high performance computing readily available to modelers and allows analysts to explore the vast amount of data that results from exercising models.