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• W2342723672 startingPage "3158" @default.
• W2342723672 abstract "Coping with uncertainties when scheduling task graphs on parallel machines requires to perform non-trivial evaluations. When considering that each computation and communication duration is a random variable, evaluating the distribution of the critical path length of such graphs involves computing maximums and sums of possibly dependent random variables. The discrete version of this evaluation problem is known to be #P-hard. Here, we propose two heuristics, CorLCA and Cordyn, to compute such lengths. They approximate the input random variables and the intermediate ones as normal random variables, and they precisely take into account correlations with two distinct mechanisms: through lowest common ancestor queries for CorLCA and with a dynamic programming approach for Cordyn. Moreover, we empirically compare some classical methods from the literature and confront them to our solutions. Simulations on a large set of cases indicate that CorLCA and Cordyn constitute each a new relevant trade-off in terms of rapidity and precision." @default.
• W2342723672 created "2016-06-24" @default.
• W2342723672 creator A5005797486 @default.
• W2342723672 creator A5040106458 @default.
• W2342723672 date "2016-11-01" @default.
• W2342723672 modified "2023-10-18" @default.
• W2342723672 title "Correlation-Aware Heuristics for Evaluating the Distribution of the Longest Path Length of a DAG with Random Weights" @default.
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• W2342723672 doi "https://doi.org/10.1109/tpds.2016.2528983" @default.
• W2342723672 hasPublicationYear "2016" @default.
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