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• W2020958036 abstract "In this article, we use an analytical energy model based on frequency scaling to model the energy consumption of tasks in a fork-join pattern of parallelism. In particular, tasks that may be executed concurrently to each other are considered, and the resulting energy consumption for different processor assignments is investigated. Frequency scaling factors that lead to a minimum energy consumption are derived and used in task-based scheduling algorithms. An experimental evaluation provides simulations for a large number of randomly generated task sets as well as energy measurements on a Intel Sandy Bridge architecture using a complex application from numerical analysis." @default.
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• W2020958036 date "2012-08-01" @default.
• W2020958036 modified "2023-10-07" @default.
• W2020958036 title "Energy-Aware Execution of Fork-Join-Based Task Parallelism" @default.
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• W2020958036 doi "https://doi.org/10.1109/mascots.2012.35" @default.
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