Award Abstract #0615181
CSR-AES: The Adaptive Code Kitchen: Flexible Approaches to Dynamic Application Composition
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NSF Org: |
CNS
Division of Computer and Network Systems
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Initial Amendment Date: |
June 15, 2006 |
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Latest Amendment Date: |
July 8, 2008 |
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Award Number: |
0615181 |
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Award Instrument: |
Continuing grant |
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Program Manager: |
Anita J. LaSalle
CNS Division of Computer and Network Systems
CSE Directorate for Computer & Information Science & Engineering
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Start Date: |
August 1, 2006 |
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Expires: |
July 31, 2009 (Estimated) |
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Awarded Amount to Date: |
$642736 |
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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)
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Sponsor: |
Virginia Polytechnic Institute and State University
1880 Pratt Drive
BLACKSBURG, VA 24060 540/231-5281
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NSF Program(s): |
COMPUTER SYSTEMS
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Field Application(s): |
0000912 Computer Science, 0116000 Human Subjects
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Program Reference Code(s): |
HPCC, 9218, 2884
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Program Element Code(s): |
7354
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ABSTRACT
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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
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(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).
Please report errors in award information by writing to: awardsearch@nsf.gov.
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