Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores

TY - JOUR

T1 - Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores

AU - Wächter, E.W.

AU - De Bellefroid, C.

AU - Basireddy, K.R.

AU - Singh, A.K.

AU - Al-Hashimi, B.M.

AU - Merrett, G.

N1 - cited By 1

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

U2 - 10.1109/TVLSI.2019.2896776

DO - 10.1109/TVLSI.2019.2896776

M3 - Article

VL - 27

SP - 1404

EP - 1415

JO - IEEE Transactions on Very Large Scale Integration (VLSI) Systems

JF - IEEE Transactions on Very Large Scale Integration (VLSI) Systems

IS - 6

ER -