Energy consumption reduction for asynchronous message-passing applications


It is widely accepted that the asynchronous parallel methods are more suitable than the synchronous ones on a grid architecture. Indeed, they outperform the synchronous methods, because they overlap the communications of the synchronous methods with computations. However, they also usually execute more iterations than the synchronous ones and thus consume more energy. To reduce the energy consumption of the CPUs executing such methods, the Dynamic voltage and frequency scaling technique can be used. It lowers the frequency of a CPU to reduce its energy consumption, but it also decreases its computing power. Therefore, the frequency that gives the best trade-off between energy consumption and performance must be selected. This paper presents a new online frequency selecting algorithm for parallel iterative asynchronous methods running over grids. It selects a vector of frequencies that gives the best trade-off between energy consumption and performance. New energy and performance models were used in this algorithm to predict the execution time and the energy consumption of synchronous, asynchronous, or hybrid iterative applications running over grids. The proposed algorithm was evaluated on the SimGrid simulator. The experiments showed that synchronously applying the proposed algorithm to the asynchronous version of the application reduces on average its energy consumption by 22% and speeds it up by 5.72%. Finally, the proposed algorithm was also compared to a method that uses the well-known energy and delay product and the comparison results showed that it outperforms this method in terms of energy consumption and performance.

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This work has been partially supported by the Labex ACTION project (contract “ANR-11-LABX-01-01”). Computations have been performed on the supercomputer facilities of the Mésocentre de calcul de Franche-Comté. As a Ph.D. student, Mr. Ahmed Fanfakh, would like to thank the University of Babylon (Iraq) for supporting his work.

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Correspondence to Jean-Claude Charr.

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Fanfakh, A., Charr, J., Couturier, R. et al. Energy consumption reduction for asynchronous message-passing applications. J Supercomput 73, 2369–2401 (2017).

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  • Energy optimization
  • DVFS
  • Asynchronous computing
  • Grid computing