Dynamic power management for UML modeled applications on multiprocessor SoC

Kukkala, Petri; Arpinen, Tero; Setälä, Mikko; Hännikäinen, Marko; Hämäläinen, Timo D.

doi:10.1117/12.707328

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…In the time dimension, applications are scheduled to run together at high speed, so that cores can be in an idle and low power consuming state in other times. Such workload dependent dynamic power management involves observing the current core utilization and workload statistics, reevaluating the number of required cores to achieve the projected performance, re-mapping the workload to the minimized set of active cores, and setting other inactive cores to a power-saving state [18]. It has been observed that such idle-maximization can reduce average power consumption [13].…”

Section: Related Workmentioning

confidence: 99%

Improving Multicore Server Performance and Reducing Energy Consumption by Workload Dependent Dynamic Power Management

2016

IEEE Trans. Cloud Comput.

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The technique of using workload dependent dynamic power management (i.e., variable power and speed of processor cores according to the current workload) to improve system performance and to reduce energy consumption is investigated. Typically, the power supply and the core speed are increased when there are more tasks in a server, such that tasks can be processed faster and the average task response time is reduced. On the other hand, the power supply and the core speed are decreased when there are less tasks in a server, such that energy consumption can be reduced without significant performance degradation. A queueing model of multicore server processors with workload dependent dynamic power management is established. Several speed schemes are proposed and it is demonstrated that for the same average power consumption, it is possible to design a multicore server processor with workload dependent dynamic power management, such that its average task response time is shorter than a multicore server processor of constant speed (i.e., without workload dependent dynamic power management). It is shown that given certain application environment and average power consumption, there is an optimal speed scheme that minimizes the average task response time. For two-speed schemes, the problem of optimal design of a two-speed scheme for given power supply and power consumption model is formulated and solved. It is pointed out that power consumption reduction subject to performance constraints can be studied in a similar way as performance improvement (i.e., average task response time reduction) subject to power consumption constraints. To the best of our knowledge, this is the first work on analytical study of workload dependent dynamic power management.Index Terms-Dynamic power management, energy consumption, multicore server processor, queueing model, response time. !• K. Li is with the Keqin Li is a SUNY Distinguished Professor of computer science. His current research interests include parallel computing and high-performance computing, distributed computing, energy-efficient computing and communication, heterogeneous computing systems, cloud computing, big data computing, CPU-GPU hybrid and cooperative computing, multicore computing, storage and file systems, wireless communication networks, sensor networks, peer-to-peer file sharing systems. He has published over 330 journal articles, book chapters, and refereed conference papers, and has received several best paper awards. He is an IEEE Fellow.

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Section: Related Workmentioning

confidence: 99%

Improving Multicore Server Performance and Reducing Energy Consumption by Workload Dependent Dynamic Power Management

2016

IEEE Trans. Cloud Comput.

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Dynamic power management for UML modeled applications on multiprocessor SoC

Cited by 1 publication

References 13 publications

Improving Multicore Server Performance and Reducing Energy Consumption by Workload Dependent Dynamic Power Management

Improving Multicore Server Performance and Reducing Energy Consumption by Workload Dependent Dynamic Power Management

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