An online temperature-aware scheduling technique to avoid thermal emergencies in multiprocessor systems

Bashir, Qaisar; Shehzad, Muhammad Naeem; Awais, Muhammad Naeem; Baig, Shams Ali; Dogar, Muhammad Ghaffar; Rashid, Aamir

doi:10.1016/j.compeleceng.2018.06.002

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…The proposed prediction-based early wake-up (PEW) was compared with our previous work that uses a non-early wakeup technique (NoEW) [19] and with the state-of-the-art a fixed threshold early wake-up (FEW) [21] that uses a fixed threshold to wake up the dark cores. Moreover, for a fair comparison, the dark cores are switched to a C1 power state instead of an idle state in FEW.…”

Section: B Comparative Results and Analysismentioning

confidence: 99%

“…However, this technique is not suitable for uncharacterized applications. In another work, Bashir et al [21] proposed an improved technique suitable for run-time performance optimization. In this technique, the temperature is sensed at run-time, and task migration is to move the tasks to dark cores to address the thermal violation.…”

Section: Related Workmentioning

confidence: 99%

“…In a dark core state, all core components are off and need time to be back to operate normally. Studies in [20], [21] proposed an early wake-up to address the wakeup latency of the dark cores. However, these studies use a fixed wake-up threshold, which may not suit high thermal fluctuating applications.…”

Section: Introductionmentioning

confidence: 99%

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PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems

Mohammed¹,

Paraman

Rahman

et al. 2021

IEEE Access

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Future many-core systems need to address the dark silicon problem, where some cores would be turned off to control the chip's thermal and power density, which effectively limits the performance gain from having a large number of processing cores. Task migration technique has been previously proposed to improve many-core system performance by moving tasks between active and dark cores. As task migration imposes system performance overhead due to the large wake-up latency of the dark cores, this paper proposes a prediction-based early wake-up (PEW) to reduce the dark cores' wake-up latency during task migration. A window-based online ridge regression (RR) is used as the prediction model. The prediction model uses the past window's thermal, power, and core status (i.e., active or dark) to predict the future core temperatures at run-time. If task migration is predicted in the next control period, the proposed PEW puts the dark cores in a power state with low wake-up latency. Thus, the proposed PEW reduces the time for the dark cores to start executing the tasks. The comparison results show that our proposed PEW reduces the completion time by up to 7.9% and 4.1% compared to non-early wake-up (NoEW) and a fixed threshold wake-up (FEW), respectively. It also shows that the proposed PEW increases the MIPS/Watt by up to 5.5% and 2.3% over NoEW and FEW, respectively. These results show that the proposed PEW improves the many-core system's overall performance in terms of reducing dark cores' wake-up latency and increasing the number of executed instructions per Watt.INDEX TERMS Dark silicon, many-core systems, task migration, dynamic voltage frequency scaling (DVFS), ridge regression, early wake-up, dark core wake-up latency.

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Section: B Comparative Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems

Mohammed¹,

Paraman

Rahman

et al. 2021

IEEE Access

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“…High temperature decreases the reliability and life of the chips. Therefore, providing a solution for proper temperature and performance management in the multi-core processor is inevitable [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Temperature and performance evaluation of multiprocessors chips by optimal control method

2023

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Multi-core processors support all modern electronic devices nowadays. However, temperature and performance management are one of the most critical issues in the design of today’s microprocessors. In this paper, we propose a framework by using an optimal control method based on fan speed and frequency control of the multi-core processor. The goal is to optimize performance and at the same time avoid violating an expected temperature. Our proposed method uses a high-precision thermal and power model for multi-core processors. This method is validated on asymmetric ODROID-XU4 multi-core processor. The experimental results show the ability of the proposed method to achieve the adequate trade-off between performance and temperature control.

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“…The authors modified this static method into a dynamic technique where the temperature is measured at run time. 14 This online temperature measurement is consequently used for prediction of temperature for reducing the switching time as well as the thermal emergencies.…”

Section: Introductionmentioning

confidence: 99%

Thermal-aware resource allocation in earliest deadline first using fluid scheduling

Shehzad

Bashir

Ahmad

et al. 2019

International Journal of Distributed Sensor Networks

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Thermal issues in microprocessors have become a major design constraint because of their adverse effects on the reliability, performance and cost of the system. This article proposes an improvement in earliest deadline first, a uni-processor scheduling algorithm, without compromising its optimality in order to reduce the thermal peaks and variations. This is done by introducing a factor of fairness to earliest deadline first algorithm, which introduces idle intervals during execution and allows uniform distribution of workload over the time. The technique notably lowers the number of context switches when compare with the previous thermal-aware scheduling algorithm based on the same amount of fairness. Although, the algorithm is proposed for uni-processor environment, it is also applicable to partitioned scheduling in multi-processor environment, which primarily converts the multi-processor scheduling problem to a set of uni-processor scheduling problem and thereafter uses a uni-processor scheduling technique for scheduling. The simulation results show that the proposed approach reduces up to 5% of the temperature peaks and variations in a uni-processor environment while reduces up to 7% and 6% of the temperature spatial gradient and the average temperature in multi-processor environment, respectively.

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An online temperature-aware scheduling technique to avoid thermal emergencies in multiprocessor systems

Cited by 9 publications

References 5 publications

PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems

PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems

Temperature and performance evaluation of multiprocessors chips by optimal control method

Thermal-aware resource allocation in earliest deadline first using fluid scheduling

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