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Energy-Awareness and Performance Management with Parallel Dataflow Applications

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Abstract

Applications have traditionally been executed as fast as possible (Race-to-Idle) and mapped to as many cores as possible (Fair scheduling) to minimize the energy consumption. With modern hardware, this method has become inefficient because of the power characteristics of the platforms. Instead, applications should utilize an optimal combination of clock frequency and number of cores to balance the dynamic and static power. Such approaches have been difficult to achieve since resource allocation is based only on CPU utilization. Resources are then allocated to prohibit over utilization rather than following software performance requirements. By adjusting the clock frequency directly according to software requirements and activating CPU cores according to the application parallelism, significant energy can be saved by lowering the average power dissipation. To enforce these recommendations, this paper provides means of expressing performance and parallelism in applications for more tight integration with the power management to balance the execution speed and mapping on multi-core systems. An interface between the applications and the hardware resources is provided in combination with a novel power management runtime system called Bricktop. A signal processing case study demonstrates real-world energy savings up to 50 % without performance degradation.

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Notes

  1. Note that the performance model can be exchanged or manipulated if the user considers Amdahl’s law not accurate enough or if it does not represent the real system behavior in other ways.

  2. E and Q are normalized to the range in which q and c operate.

  3. The upthreshold in Linux is usually set based on best practice for the system in question. Typical settings are around 80–95 % of full workload (100 %).

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Authors and Affiliations

  1. Turku Centre for Computer Science, Joukahaisenkatu 3–5, 20520, Turku, Finland

    Simon Holmbacka

  2. IETR Image Group, INSA de Rennes, Rennes, France

    Erwan Nogues, Maxime Pelcat & Daniel Menard

  3. Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland

    Sébastien Lafond & Johan Lilius

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  1. Simon Holmbacka
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  2. Erwan Nogues
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  3. Maxime Pelcat
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  6. Johan Lilius
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Correspondence to Simon Holmbacka.

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Holmbacka, S., Nogues, E., Pelcat, M. et al. Energy-Awareness and Performance Management with Parallel Dataflow Applications. J Sign Process Syst 87, 33–48 (2017). https://doi.org/10.1007/s11265-015-1059-4

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  • DOI: https://doi.org/10.1007/s11265-015-1059-4

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