Embedding optimization in computational science workflows

Abramson, David, Bethwaite, Blair, Enticott, Colin, Garic, Slavisa, Peachey, Tom, Michailova, Anushka and Amirriazi, Saleh (2010) Embedding optimization in computational science workflows. Journal of Computational Science, 1 1: 41-47. doi:10.1016/j.jocs.2010.04.002

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Author Abramson, David
Bethwaite, Blair
Enticott, Colin
Garic, Slavisa
Peachey, Tom
Michailova, Anushka
Amirriazi, Saleh
Title Embedding optimization in computational science workflows
Journal name Journal of Computational Science   Check publisher's open access policy
ISSN 1877-7503
1877-7511
Publication date 2010-05-01
Sub-type Article (original research)
DOI 10.1016/j.jocs.2010.04.002
Volume 1
Issue 1
Start page 41
End page 47
Total pages 7
Place of publication Oxford, United Kingdom
Publisher Elsevier
Language eng
Abstract Workflows support the automation of scientific processes, providing mechanisms that underpin modern computational science. They facilitate access to remote instruments, databases and parallel and distributed computers. Importantly, they allow software pipelines that perform multiple complex simulations (leveraging distributed platforms), with one simulation driving another. Such an environment is ideal for computational science experiments that require the evaluation of a range of different scenarios "in silico" in an attempt to find ones that optimize a particular outcome. However, in general, existing workflow tools do not incorporate optimization algorithms, and thus whilst users can specify simulation pipelines, they need to invoke the workflow as a stand-alone computation within an external optimization tool. Moreover, many existing workflow engines do not leverage parallel and distributed computers, making them unsuitable for executing computational science simulations. To solve this problem, we have developed a methodology for integrating optimization algorithms directly into workflows. We implement a range of generic actors for an existing workflow system called Kepler, and discuss how they can be combined in flexible ways to support various different design strategies. We illustrate the system by applying it to an existing bio-engineering design problem running on a Grid of distributed clusters.
Keyword Cardiac models
Design optimization
Grid computing
Workflows
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 21 times in Scopus Article | Citations
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Created: Wed, 23 Oct 2013, 00:05:52 EST by Ms Diana Cassidy on behalf of Research Computing Centre