Award Abstract #0615181
CSR-AES: The Adaptive Code Kitchen: Flexible Approaches to Dynamic Application Composition

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	June 15, 2006
Latest Amendment Date:	July 8, 2008
Award Number:	0615181
Award Instrument:	Continuing grant
Program Manager:	Anita J. LaSalle CNS Division of Computer and Network Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	August 1, 2006
Expires:	July 31, 2009 (Estimated)
Awarded Amount to Date:	$642736
Investigator(s):	Naren Ramakrishnan naren@cs.vt.edu (Principal Investigator) Calvin Ribbens (Co-Principal Investigator) Danesh Tafti (Co-Principal Investigator) Srinidhi Varadarajan (Co-Principal Investigator)
Sponsor:	Virginia Polytechnic Institute and State University 1880 Pratt Drive BLACKSBURG, VA 24060 540/231-5281
NSF Program(s):	COMPUTER SYSTEMS
Field Application(s):	0000912 Computer Science, 0116000 Human Subjects
Program Reference Code(s):	HPCC, 9218, 2884
Program Element Code(s):	7354

ABSTRACT

This project provides next generation programming tools that enable

compositional construction of complex, adaptive, software systems. As the

name connotes, the adaptive code kitchen is a loose collection of capabilities

by which an application scientist can specify and realize "recipes" of

adaptivity around native object codes. These capabilities include function

interception, continuation modification, dynamic process checkpointing and

rollback, and runtime recommendation. The project develops both the

compile-time and runtime infrastructure enabling these capabilities and also

a cookbook of standard adaptivity schemas, which can be instantiated to cover

a wide range of adaptation possibilities. Using the adaptive code kitchen, the

application scientist can incorporate libraries of native object codes,

specify problem/task boundaries, organize his application into clouds

of related functions, instantiate one of the supplied adaptivity schemas,

and track the application's progress through an interactive monitor.

As a domain case study, the project investigates computational fluid dynamics

simulations, especially the modeling of turbulence.

Besides improved understanding of application composition systems, the impacts

of the adaptive code kitchen project are in both the domain context and

in educational outreach. The accurate modeling and prediction of turbulent

flows, especially in airplanes, ships, and automobiles, as supported by the

adaptive code kitchen can have a major impact in reducing energy consumption.

In addition, the investigators will offer short-term courses at Virginia Tech

showcasing the use of the techniques developed here and, in this manner, give

domain scientists across campus the training and expertise they need to

construct their own adaptive applications.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

(Showing: 1 - 2 of 2).

Naren Ramakrishnan and Upinder S. Bhalla.  "Memory Switches in Chemical Reaction Space,"  PLoS Computational Biology,  2008, 

Ying Jin, Naren Ramakrishnan, and T.M. Murali.  "Compositional Mining of Multirelational Biological Datasets,"  ACM Transactions on Knowledge Discovery from Data,  v.2,  2008, 


(Showing: 1 - 2 of 2).

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