| Abstract |
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Individual-based models in evolutionary biology easily
lead to multi-parameter applications that need global
computing power to exploit their full potential. Mainly
due to varying population size parameters, they easily
generate computational complexities from less than a
second to more than 100 years in case of the
Simulator005 of evolution@home. The poorly
understood biology of the system leads to automated
predictions that may be way off. This report describes
first experiences of a global computing system, where
users can choose between tasks of different complexity.
Besides theoretical complexity limits of tasks that fit
global computing, choices of users are analyzed.
Potential of incomplete results to increase prediction
accuracy is discussed as well as benchmarking computer
systems that vary nearly 2 orders of magnitude in their
idle processing power. Finally, prediction accuracy is
analyzed with the help of a newly defined parameter:
error of magnitude. It is concluded, that global
computing has great potential for projects with poorly
predictable single-run-complexities, if frameworks are
designed to allow users to choose their commitment,
and if they make use of incomplete results to improve
predictions.
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 Additional Information
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Citation:
Laurence Loewe,
"evolution@home: Experiences with Work Units That Span More than 7 Orders of Magnitude in Computational Complexity,"
ccgrid,
p. 425,
2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02),
2002
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