NGS: A Model-Based Framework for Adaptive Algorithm Design

University Of Southern California, Los Angeles CA

Investigators

Abstract

Rapidly increasing performance requirements of applications have spurred the next generation of complex, dynamic, heterogeneous, parallel/distributed computing system architectures. The emerging computational grid, tightly-coupled petaflop grids-in-a-box (GiBs), distributed sensor networks, System-on-Chip (SoC) and polymorphous computing (PCA) architectures are examples of such systems. To exploit the full potential of this new computing architecture, applications, as they execute, must be able to adapt to the continuously changing system. Although some support for adaptive application development is available in the form of programming languages and runtime systems, there is a lack of high level system abstractions that model the dynamic behavior and runtime adaptivity. The proposed research will address fundamental issues in modeling these dynamic, complex architectures and the design and evaluation of adaptive algorithms for such architectures. The focus of the proposed research will be on creating a formal framework to reason about adaptivity at an abstract level. A direct educational impact of the proposed activity will be the introduction of new curriculum in academia, to impart knowledge on algorithm design aspects for dynamic system architectures. This will include initiating new course-work along with traditional courses offered on analysis of algorithms and architectures. One of the broader impacts we foresee is the preparation of future Grid/GiBs/SoC/PCA application developers.