Wachter, Eduardo Weber and de Bellefroid, Cedric and Basireddy, Karunakar Reddy and Singh, Amit Kumar and Al-Hashimi, Bashir M and Merrett, Geoff (2019) Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27 (6). pp. 1404-1415. DOI https://doi.org/10.1109/tvlsi.2019.2896776
Wachter, Eduardo Weber and de Bellefroid, Cedric and Basireddy, Karunakar Reddy and Singh, Amit Kumar and Al-Hashimi, Bashir M and Merrett, Geoff (2019) Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27 (6). pp. 1404-1415. DOI https://doi.org/10.1109/tvlsi.2019.2896776
Wachter, Eduardo Weber and de Bellefroid, Cedric and Basireddy, Karunakar Reddy and Singh, Amit Kumar and Al-Hashimi, Bashir M and Merrett, Geoff (2019) Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27 (6). pp. 1404-1415. DOI https://doi.org/10.1109/tvlsi.2019.2896776
Abstract
Current multicore platforms contain different types of cores, organized in clusters (e.g., ARM's big.LITTLE). These platforms deal with concurrently executing applications, having varying workload profiles and performance requirements. Runtime management is imperative for adapting to such performance requirements and workload variabilities and to increase energy and temperature efficiency. Temperature has also become a critical parameter since it affects reliability, power consumption, and performance and, hence, must be managed. This paper proposes an accurate temperature prediction scheme coupled with a runtime energy management approach to proactively avoid exceeding temperature thresholds while maintaining performance targets. Experiments show up to 20% energy savings while maintaining high-temperature averages and peaks below the threshold. Compared with state-of-the-art temperature predictors, this paper predicts 35% faster and reduces the mean absolute error from 3.25 to 1.15 °C for the evaluated applications' scenarios.
Item Type: | Article |
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Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 12 Jun 2019 08:40 |
Last Modified: | 23 Sep 2022 19:33 |
URI: | http://repository.essex.ac.uk/id/eprint/24796 |
Available files
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