Evaluation of the DAG Ready Tasks Maximization Algorithms in Multi-core Computing Platforms

Zheng, Wei; Bao, Wen; Xu, Chao; Zhang, Dongzhan

doi:10.1109/cbd.2016.029

Cited by 2 publications

(1 citation statement)

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“…Problem Description: The problem of task scheduling onto heterogeneous HPC systems has been extensively studied [2,[10][11][12]14,15], with several works focusing on multicore processors [1,3,8,10,16]. Some of the algorithms focus on optimizing application performance and execution time [10,12], while other works also consider energy and power optimization [1,8].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Scientific Workflow Execution on an Asymmetric Multicore Processor

Pietri¹,

Zhuang

Casas

et al. 2018

Euro-Par 2017: Parallel Processing Workshops

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Asymmetric multicore architectures that integrate different types of cores are emerging as a potential solution for good performance and power efficiency. Although scheduling can be improved by utilizing an appropriate set of cores for the execution of the different jobs, determining frequency configurations is also crucial to achieve both good performance and energy efficiency. This challenge may be more profound with scientific workflow applications that consist of jobs with data dependency constraints. The paper focuses on deploying and evaluating the Montage scientific workflow on an asymmetric multicore platform with the aim to explore CPU frequency configurations with different trade-offs between execution time and energy efficiency. The proposed approach provides good estimates of workflow execution time and energy consumption for different frequency configurations with an average error of less than 8.63% for time and less than 9.69% for energy compared to actual values.

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