We propose a new framework for providing information to help optimize domain-specific application codes. Its design addresses problems that derive from the widening gap between the domain problem statement by domain experts and the architectural details of new and future high-end computing systems. The design is particularly well suited to program execution models that incorporate dynamic adaptive methodologies for live tuning of program performance and resource utilization. This new framework, which we call 'structured hints', couples a vocabulary of annotations to a suite of performance metrics. The immediate target is development of a process by which a domain expert describes characteristics of objects and methods in the application code that would not be readily apparent to the compiler; the domain expert provides further information about what quantities might provide the best indications of desirable effect; and the interactive preprocessor identifies potential opportunities for the domain expert to evaluate. Our development of these ideas is progressing in stages from case study, through manual implementation, to automatic or semi-automatic implementation. In this paper we discuss results from our case study, an examination of a large simulation of a neural network modeled after the neocortex.