Thermal analysis of stochastic DVFS-enabled multicore real-time systems

Abstract

This paper considers a multicore system, equipped with dynamic voltage/frequency scaling (DVFS), which runs real-time jobs with probabilistic characteristics. The DVFS policy directly affects the performance of this system as well as its power consumption through impacting both dynamic and leakage powers. While power consumption determines the processor thermal behavior, the temperature in turn affects the processor power consumption by impacting the leakage power. Additionally, temperature variation of a core is coupled with the thermal behaviors of the other cores, namely thermal effects of the surrounding components. These inter-effects make sophisticated relations between the nature of the stochastic real-time system and its performance, power consumption, and temperature behavior. In this paper, we first present an exact thermal analysis approach for the specified system considering these inter-effects. We then propose a scalable approximate method for thermal analysis of the system based on the exact analysis. The proposed analytical method outperforms the traditional simulation-based methods in terms of time complexity, by approximately three orders of magnitude, while introducing a relative error of less than 0.8 % for the mentioned setups. Also, we show the efficacy of the proposed analytical method in temperature-aware power minimization, resulting in significant reductions in the system-wide power consumption.