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 …
continued below
Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.
Descriptive information to help identify this report.
Follow the links below to find similar items on the Digital Library.
Description
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.
This report is part of the following collection of related materials.
Office of Scientific & Technical Information Technical Reports
Reports, articles and other documents harvested from the Office of Scientific and Technical Information.
Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.
Hereld, M.; Stevens, R.; Sterling, T.; Gao, G. R.; Science, Mathematics and Computer; Tech., California Inst. of et al.Structured hints : extracting and abstracting domain expertise.,
report,
March 16, 2009;
United States.
(https://digital.library.unt.edu/ark:/67531/metadc931577/:
accessed March 26, 2023),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT Libraries Government Documents Department.
