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.

This work was supported through a collaboration grant from HiPEAC (www.hipeac.net), the RoMoL ERC Advanced Grant (GA 321253), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272).

Peer Reviewed