Abstract
Increasingly, feedback of measured run-time information is being used in the optimization of computation execution. Because of this, the need for a model relating the static view of a computation to its runtime variance is becoming more important. Recently, we have described such a model which uses the notion of uncertainty to provide bounds on key scheduling parameters of the run-time computation. In this paper, we demonstrate how our model provides a foundation for robust parallel scheduling, i.e., scheduling that optimizes for computation execution in the presence of run-time variance. While this work was inspired by our previous study of uncertainty due to measurement intrusion, the scheduling paradigm presented here represents a broader, more general application of the uncertainty concept.
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© 1997 Springer-Verlag Berlin Heidelberg
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Dietz, R.D., Casavant, T.L., Scheetz, T.E., Braun, T.A., Andersland, M.S. (1997). Using run-time uncertainty to robustly schedule parallel computation. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 1997. Lecture Notes in Computer Science, vol 1277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63371-5_3
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DOI: https://doi.org/10.1007/3-540-63371-5_3
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