Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise thermal control strategies as well as automated diagnosis. However, when dealing with large scale systems "in production", the accuracy of learned thermal models depends on the dynamics of the power excitation, which depends also on the executed workload, and measurement nonidealities, such as quantization. In this paper we show that, using an advanced system identification algorithm, we are able to generate very accurate thermal models (average error lower than our sensors quantization step of 1°C) for a large scale HPC system on real workloads for very long time periods. However, we also show that: 1) not all real workloads allow for the identification of a good model; 2) starting from the theory of system identification it is very difficult to evaluate if a trace of data leads to a good estimated model. We then propose and validate a set of techniques based on machine learning and deep learning algorithms for the choice of data traces to be used for model identification. We also show that deep learning techniques are absolutely necessary to correctly choose such traces up to 96 of the times.
Previous works have shown that the relation between the core temperature and the dissipated power can be described by a purely dynamic ARX model @cite_5 @cite_21 . ARX models are widely used in system identification since they constitute the simplest way of representing a dynamic process in the presence of uncertainties @cite_9 . Two important features of these models are the possibility of obtaining asymptotically unbiased estimates of their parameters by means of least squares and the absence of stability problems of the associated optimal one step-ahead predictors @cite_9 . Nevertheless, it has been shown in @cite_21 that the classic MISO ARX model is not able to describe properly the thermal dynamics of the system because the estimated models are characterized by relevant negative poles and or complex conjugate poles. This is in contrast with the physics of thermal systems, where only real positive poles can exist. As explained in @cite_21 , this problem is due to the presence of a significant level of measurement noise.
@cite_5 @cite_9 @cite_21
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Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis und praktische Anwendung der verschiedenen Verfahren zur Identifizierung hat. Da ... The thermal wall for many-core systems on-chip calls for advanced management techniques to maximize performance, while capping temperatures. Distributed and compact thermal models are a cornerstone for such techniques. System identification methodologies allow to extract models directly from the target device thermal response. Unfortunately, standard Auto-Regressive eXogenous models and Least Squares techniques cannot effectively tackle both model approximation and measurement noise typical of real systems. In this work, we propose a novel distributed identification strategy to derive distributed interacting thermal models. The presented method can cope with both process noise and temperature sensor noise affecting inputs and outputs of the adopted models. Online and offline versions are presented, and issues related to model order, sampling time and input stimuli are addressed. The proposed method is applied to the Intel's Single-chip-Cloud-Computer many-core prototype.