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Modeling the impact of run-time uncertainty on optimal computation scheduling using feedback
Conference proceeding

Modeling the impact of run-time uncertainty on optimal computation scheduling using feedback

R.D Dietz, T.L Casavant, T.E Scheetz, T.A Braun and M.S Andersland
Proceedings of the 1997 International Conference on Parallel Processing (Cat. No.97TB100162), pp.481-488
1997
DOI: 10.1109/ICPP.1997.622683

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Abstract

Increasingly, feedback of measured run-time information is being used in the optimization of computation execution. This paper introduces a model relating the static view of a computation to its run-time variance that is useful in this context. A notion of uncertainty is then used to provide bounds on key scheduling parameters of the run-time computation. To illustrate the relationship between fidelity in measured information and minimum schedulable, grain size, we apply the bounds to three existing parallel architectures for the case of run-time variance caused by monitoring intrusion. We also outline a hybrid static-dynamic scheduling paradigm-SEDIA-that uses the model of uncertainty to optimize computation for execution in the presence of run-time variance from sources other than monitoring intrusion.
Concurrent computing Context modeling Delay Dynamic scheduling Feedback Monitoring Optimal scheduling Processor scheduling Runtime Uncertainty

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