Advances across many fields of study are driving changes in the basic nature of scientific computing applications. Scientists have recognized a growing need to study phenomena by explicitly modeling interactions among individual entities, rather than by simply modeling approximate collective behavior. This entity-level approach has emerged as a promising new direction in a number of scientific fields.

One of the challenges inhibiting the entity-level approach are the substantial resource requirements it entails. Unfortunately, such applications exhibit characteristics and behaviors which render traditional parallel computing techniques ineffective. Well-defined methodologies for achieving scalable performance on distributed computing platforms are needed. As an important first step, we present an abstract application model for entity-level applications, and we instantiate it for a case-study immunology application. Our experiments confirm that this model tracks application performance trends sufficiently well to study scheduling issues pertaining to entity-level applications. We identify a scalability problem inherent to the entity-level approach and use our model to quantify the potential performance improvements that remapping strategies may yield.