HotGauge: A Methodology for Characterizing Advanced Hotspots in Modern and Next Generation Processors

Hankin, Alexander; Werner, David; Amiraski, Maziar; Sébot, Julien; Vaidyanathan, Kaushik; Hempstead, Mark

doi:10.1109/iiswc53511.2021.00025

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…HotSpot has been applied by Glocker et al [22] to study the temperature distribution in a 16-core system and by Florea et al [23] to provide thermal analysis for enhancing the Sniper multicore simulator. HotSpot and 3D-ICE have been integrated into the performance-power-thermal simulation toolchains, such as HotSniper [24], HotGauge [25] and CoMeT [26]. The efficiency of the thermal circuit model is however accomplished by using large lumped elements that are not able to adequately account for distributed heat transfer and may lead to an inaccurate prediction [27].…”

Section: B Thermal Circuit Modelmentioning

confidence: 99%

PODTherm-GP: A Physics-Based Data-Driven Approach for Effective Architecture-Level Thermal Simulation of Multi-Core CPUs

Lin

Dowling

Cheng

et al. 2023

IEEE Trans. Comput.

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A thermal simulation methodology derived from the proper orthogonal decomposition (POD) and the Galerkin projection (GP), hereafter referred to as PODTherm-GP, is evaluated in terms of its efficiency and accuracy in a multi-core CPU. The GP projects the heat transfer equation onto a mathematical space whose basis functions are generated from thermal data enabled by the POD learning algorithm. The thermal solution data are collected from FEniCS using the finite element method (FEM) accounting for appropriate parametric variations. The GP incorporates physical principles of heat transfer in the methodology to reach high accuracy and efficiency. The dynamic power map for the CPU in FEM thermal simulation is generated from gem5 and McPACT, together with the SPLASH-2 benchmarks as the simulation workload. It is shown that PODTherm-GP offers an accurate thermal prediction of the CPU with a resolution as fine as the FEM. It is also demonstrated that PODTherm-GP is capable of predicting the dynamic thermal profile of the chip with a good accuracy beyond the training conditions. Additionally, the approach offers a reduction in degrees of freedom by more than 5 orders of magnitude and a speedup of 4 orders, compared to the FEM.

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Section: B Thermal Circuit Modelmentioning

confidence: 99%

PODTherm-GP: A Physics-Based Data-Driven Approach for Effective Architecture-Level Thermal Simulation of Multi-Core CPUs

Lin

Dowling

Cheng

et al. 2023

IEEE Trans. Comput.

View full text Add to dashboard Cite

show abstract

“…Since the advent of dark silicon, computing architecture needs both optimized heat dissipation solutions and run-time thermal control policies to operate reliably and efficiently despite the steady increase in power density and related issues such as hot spots [12]. Thermal control policies in the state of the art include: Linear Quadratic Regulators (LQR) [21]; Model Predictive Control (MPC) [20]; and Event-based control solutions [15].…”

Section: Thermal Control In Textarossamentioning

confidence: 99%