Lifetime Reliability Enhancement in Multiprocessor Systems Through a Fine-Grained System-level Approach

In this paper, a fine-grained scheduling approach to enhance lifetime reliability of multiprocessor systems is presented. Lifetime reliability is an important and emerging concern arising with advances in technology due to the increase in power density. As a result, temperature variation accelerates wear-out, leading to system failures. The antagonistic relation of lifetime reliability with other design parameters of multiprocessor systems, such as power consumption and temperature, makes its improvement more challenging. Lifetime reliability enhancement approaches are considered at different levels of abstractions and for various system components. Our proposed scheduling method extracts the precise low-level information of lifetime reliability from determined blocks of processing cores and utilizes them at system-level to study the system state criticality at a low-performance cost. Based on the online periodic monitoring, our proposed scheduling approach applies control actions to improve lifetime reliability of the system according to its effective parameters. To demonstrate the effectiveness of our proposed scheduling approach in improving lifetime reliability and compare it to the previous related research, several experiments are considered. To simulate the target multiprocessor system and the proposed approach, the Enhanced Super ESCalar (ESESC) simulator for computer architecture tool is utilized. The experimental results show that employing our proposed scheduling method improves lifetime reliability at about 54%. Moreover, it causes 14% and 12% enhancement in temperature and power consumption. Furthermore, we perform a Monte Carlo-based simulation to validate the proposed scheduling approach and generalize it to other applications at very low-performance overhead. Experimental results show that Monte Carlo simulation extremely decreases the execution time rather than ESESC which makes utilizing our scheduling approach reasonable in large applications.