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- W2110483022 abstract "Parallel task graphs (PTGs) arise when parallel programs are combined to larger applications, e.g., scientific workflows. Scheduling these PTGs onto clusters is a challenging problem due to the additional degree of parallelism stemming from moldable tasks. Most algorithms are based on the assumption that the execution time of a parallel task is monotonically decreasing as the number of processors increases. But this assumption does not hold in practice since parallel programs often perform better if the number of processors is a multiple of internally used block sizes. In this article, we introduce the Evolutionary Moldable Task Scheduling (EMTS) algorithm for scheduling static PTGs onto homogeneous clusters. We apply an evolutionary approach to determine the processor allocation of each task. The evolutionary strategy ensures that EMTS can be used with any underlying model for predicting the execution time of moldable tasks. With the purpose of finding solutions quickly, EMTS considers results of other heuristics (e.g., HCPA, MCPA) as starting solutions. The experimental results show that EMTS significantly reduces the make span of PTGs compared to other heuristics for both non-monotonically and monotonically decreasing models." @default.
- W2110483022 created "2016-06-24" @default.
- W2110483022 creator A5071644673 @default.
- W2110483022 creator A5043701575 @default.
- W2110483022 date "2011-09-01" @default.
- W2110483022 modified "2023-10-18" @default.
- W2110483022 title "Evolutionary Scheduling of Parallel Tasks Graphs onto Homogeneous Clusters" @default.
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- W2110483022 doi "https://doi.org/10.1109/cluster.2011.45" @default.
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