Thermal aware learning based CPU governor. (13th February 2022)

• Record Type: Journal Article
• Authors: Kumar, Manoj; Sran, Sukhwinder Singh; Kaur, Lakhwinder; Singh, Jagpreet
• Is Part Of: Concurrency and computation. Volume 34:Number 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-13
• Subjects: CPU governor -- DVFS -- Linux -- multicore -- regression; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6862
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The high temperature of a processor chip reduces its lifetime by many years. Also, higher temperature increases the cooling cost as well. Hence, controlling the peak temperature and maintaining high performance are the two important objectives that are in general trade‐off with each other. The dynamic voltage and frequency scaling is often used to reduce the energy consumption by running the processor on lower voltage and frequencies. This eventually decreases the temperature. In this article, we propose a linear regression‐based online CPU governor to predict the future temperature with high accuracy and later adjust the frequencies of the processor such that the temperature does not increase the given threshold. Doing so controls the peak temperature as well as maintains high performance. Extensive experimentation revealed that the proposed governor decreases the peak temperature by 4%–5% without observing any performance penalty and also reduces the execution cost by 3%–10% compared to the state‐of‐the‐art.