The thermal profile of multicore systems vary both within an application's execution (intra) and also when the sys tem switches from one application to another (inter). In this paper, we propose an adaptive thermal management approach to improve the lifetime reliability of multicore sys tems by considering both inter-and intra-application ther mal variations. Fundamental to this approach is a reinforce ment learning algorithm, which learns the relationship be tween the mapping of threads to cores, the frequency of a core and its temperature (sampled from on-board ther mal sensors). Action is provided by overriding the oper ating system's mapping decisions using affinity masks and dynamically changing CPU frequency using in-kernel gover nors. Lifetime improvement is achieved by controlling not only the peak and average temperatures but also thermal cycling, which is an emerging wear-out concern in modern systems. The proposed approach is validated experimen tally using an Intel quad-core platform executing a diverse set of multimedia benchmarks. Results demonstrate that the proposed approach minimizes average temperature, peak temperature and thermal cycling, improving the mean-time to-failure (MTTF) by an average of 2x for intra-application and 3x for inter-application scenarios when compared to ex isting thermal management techniques. Furthermore, the dynamic and static energy consumption are also reduced by an average 10% and 11% respectively. A major challenge of modern multicore systems is decreasing lifetime reliability, threatened by high power densities and hence elevated operating temperatures. This leads to an ac celeration of device wear-out. Thermal management has at tracted significant attention both in industry and academia. Examples include dynamic thermal management using volt age and frequency scaling [7] , slack time management [101, peak temperature management through system-level task scheduling [3] and thermal stress management through ap plication task mapping [2] (refer to Section 2 for a summary of related works). These approaches, however, suffer from the following limitations. First, modern multicore systems switch between appli cations exhibiting wide performance and workload varia tions, and therefore the thermal behavior of these systems vary both within (intra) and across (inter) applications. Al though intra-application thermal variations are considered in existing studies, inter-application variations are not ad dressed. Second, the existing studies focus on average tem perature reduction; thermal cycling is not accounted. Last, the existing adaptive techniques are either implemented on a simulator or rely on time-consuming thermal prediction from the HotSpot tool [14], limiting their scalability. In this paper, we address the above gaps and present a dynamic thermal management approach for multicore sys tems that adapts to thermal variations within (intra) and across (inter) applications. Fundamental to this approach is a run-time system, which interfaces with the on-board thermal sensors and uses reinforcement learning to learn the relationship between the mapping of threads to cores, the frequency of a core and its temperature. The aim is to control the average temperature and the thermal cycling to achieve an extended mean time to failure (MTTF). This paper makes the following contributions: 1. inter-and intra-application thermal management using thread-to-core allocation (through CPU affinity) and dynamic frequency control (through CPU governors) 1 ; 2. separation of the temperature sampling interval from the decision interval of the conventional reinforcement learning algorithm to accurately model (and hence con trol) the average temperature and thermal cycling; and 3. implementation of the run-time system incorporating the machine learning algorithm on a real platform. The proposed approach is implemented on an Intel quad core platform running Linux kernel 3.8.0. A set of multime dia applications from the ALPBench suite [11] are executed on the platform. Results demonstrate that the proposed ap proach minimizes average temperature and thermal cycling, leading to a significant improvement in MTTF as demon strated in Section 6. The remainder of this paper is organized as follows. A brief introduction to related works is presented in Section 2 and the motivation in Section 3. This is followed by the pre liminaries on reliability in Section 4 and an overview of the reinforcement learning-based approach in Section 5. Evalua tion of the proposed technique is presented next in Section 6 and the paper is concluded in Section 7. The existing studies on thermal optimization can be classi fied into two categories -static and dynamic.
Das et al. REF proposed a machine learning-based algorithm to handle inter-and intraapplication variations and reduce peak temperature and TC.
Reinforcement learning-based inter- and intra-application thermal optimization for lifetime improvement of multicore systems
2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC)
2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC)
Computer Science