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Modular implementation of adaptive decisions in stochastic simulations
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Symposium on Applied Computing archive
Proceedings of the 2009 ACM symposium on Applied Computing table of contents
Honolulu, Hawaii
SESSION: Computational sciences track table of contents
Pages 995-1001  
Year of Publication: 2009
ISBN:978-1-60558-166-8
Authors
Pilsung Kang  Virginia Tech, VA
Yang Cao  Virginia Tech, VA
Naren Ramakrishnan  Virginia Tech, VA
Calvin J. Ribbens  Virginia Tech, VA
Srinidhi Varadarajan  Virginia Tech, VA
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

We present a modular approach to implement adaptive decisions with existing scientific codes. Using a sophisticated system software tool based on the function call interception technique, an external code module is transparently combined with the given program without altering the original code structure, resulting in a newly composed application with extended behavior. This is useful for generalizing codes into using different parameter values or to switch algorithms or implementations at runtime. Applying the proposed method on a biochemical stochastic simulation software package to implement a set of exemplary use cases, which includes changing program parameters, substituting random number generators, and dynamically changing the stochastic simulation method, we demonstrate how effectively new code modules can be plugged in to construct an application with enhanced capabilities.


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Collaborative Colleagues:
Pilsung Kang: colleagues
Yang Cao: colleagues
Naren Ramakrishnan: colleagues
Calvin J. Ribbens: colleagues
Srinidhi Varadarajan: colleagues