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Thermal Model Identification of Computing Nodes in High-Performance Computing Systems
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DOI：10.1109/TIE.2019.2945277.png)
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Abstract
En 中文
Thermal-aware design and online optimization of the cooling effort are becoming increasingly important in current and future high-performance computing (HPC) systems. A fundamental requirement to effectively develop such techniques is the availability of distributed and compact models representing the system thermal behavior. System identification algorithms allow to extract models directly from the thermal response of the target device. This article proposes a novel thermal identification approach for real, in-production HPC systems, which is capable of extracting thermal models from a computing node affected by quantization noise on the temperature measurements as well as operating in the free-cooling mode, with variable ambient temperature. The approach allows also to identify the physical floorplan of the CPU dies in supercomputing nodes. The effectiveness of the proposed methodology has been tested on a node of the CINECA Galileo Tier-1 supercomputer system.
Keywords:
Autoregressive processes
Computational modeling
Temperature measurement
Thermal noise
Temperature sensors
High-performance computing (HPC) systems
system identification
supercomputing nodes
thermal modeling
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