This project systematically addresses the enormous complexity of mapping applications to current and future parallel platforms - both scalable parallel architectures consisting of tens of thousands of processors and distributed systems comprised of collections of these and other resources. By integrating the system layers - domain-specific environment, application program, compiler, run-time environment, performance models and simulation, and workflow manager -- and through a systematic strategy for application mapping, the project will exploit the vast machine resources available in such parallel platforms to dramatically increase the productivity of application programmers.

The key contribution of the project will be a systematic solution for performance optimization and adaptive application mapping -- a large step towards automating a process that is currently performed in an ad hoc way by programmers and compilers -- so that it is feasible to obtain scalable performance on parallel and distributed systems consisting of tens of thousands of processing nodes. The application components will be viewed as dynamically adaptive algorithms for which there exist a set of variants and parameters that can be chosen to develop an optimized implementation. Knowledge representation and machine learning techniques utilize this domain knowledge and past experience to navigate the search space efficiently.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Network Systems (CNS)
Type
Standard Grant (Standard)
Application #
0509326
Program Officer
Frederica Darema
Project Start
Project End
Budget Start
2005-07-01
Budget End
2006-06-30
Support Year
Fiscal Year
2005
Total Cost
$49,999
Indirect Cost
Name
Ohio State University
Department
Type
DUNS #
City
Columbus
State
OH
Country
United States
Zip Code
43210