Physical-aware predictive dynamic thermal management of multi-core processors

The advances in silicon process technology have made it possible to have processors with larger number of cores. The increment of cores number has been hindered by increasing power consumption and heat dissipation due to high power expenditure in a small area die size. The high temperature can cause...

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Bibliographic Details
Published in:Journal of parallel and distributed computing 2016-09, Vol.95, p.42-56
Main Authors: Salami, Bagher, Noori, Hamid, Mehdipour, Farhad, Baharani, Mohammadreza
Format: Article
Language:English
Subjects:
Cooling
Distributed processing
DVFS
Dynamic thermal management
Expenditures
Microprocessors
Multi-core processors
Physical features
Processors
Sensors
Task migration
Thermal management
Thermal properties
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Summary:The advances in silicon process technology have made it possible to have processors with larger number of cores. The increment of cores number has been hindered by increasing power consumption and heat dissipation due to high power expenditure in a small area die size. The high temperature can cause degradation in performance, reliability, transistor aging, transition speed and increase in leakage current. In this paper, we present a method which considers different thermal behavior of cores and uses both physical sensors and performance counters simultaneously to improve thermal management of both SMT multi-core processors with a physical sensor per core and Non-SMT multi-core processors with only one physical sensor for the processor. The experimental results indicate that our technique can significantly decrease the average and peak temperature in most cases compared to Linux standard scheduler, and two well-known thermal management techniques: PDTM, and TAS. •Proposing a thermal-aware scheduling method for both SMT and Non-SMT multi-core processors based on the different thermal behavior of cores due to their core unique thermal behavior.•Experimental results on commercial processors indicate that our proposed approach, under full workloads, outperforms the Linux standard scheduler and two existing DTM techniques.•One of the unique features of the proposed algorithm is that it has an adaptive temperature threshold, unlike previous work in which all of them assume that temperature threshold is a fixed value.
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2016.03.008