Thermal-Aware Servers for Real-Time Tasks on Multi-Core GPU-Integrated Embedded Systems

Abstract

The recent trend in real-time applications raises the demand for powerful embedded systems with GPU-CPU integrated systems-on-chips (SoCs). This increased performance, however, comes at the cost of power consumption and resulting heat dissipation. Heat conduction interferes the execution time of tasks running on adjacent CPU and GPU cores. The violation of thermal constraints causes timing unpredictability to real-time tasks due transient performance degradation or permanent system failure. In this paper, we propose a thermal-aware server framework to safely upper bound the maximum temperature of GPU-CPU integrated systems running real-time sporadic tasks. Our framework supports variants of real-time server policies for CPU and GPU cores to satisfy both thermal and timing requirements. In addition, the framework incorporates two mechanisms, miscellaneous-operation-time reservation and pre-ordered scheduling of GPU requests, which significantly reduce task response time. We present analysis to design thermal-server budget and to check the schedulability of CPU-only and GPU-using sporadic tasks. The thermal properties of our framework have been evaluated on a commercial embedded platform. Experimental results with randomly-generated tasksets demonstrate the performance characteristics of our framework with different configurations.