Decentralized Thermal-Aware Task Scheduling for Large-Scale Many-Core Systems

Abstract

Technology scaling has enabled fast increase in the number of cores integrated in many-core systems. However, feature size shrinking also makes large-scale many-core systems vulnerable to thermal failures. Thermal-aware task scheduling is an efficient technique to reduce the run-time temperatures of many-core processors. Most existing thermal-aware task scheduling algorithms leverage centralized scheduling schemes to gather the overall information and generate the task schedule at a center scheduler. Although that scheme can achieve the optimal temperature reduction, however, it faces severe computation bottleneck and communication congestion when the many-core processors evolve to large-scale with hundreds or thousands of cores. In this paper, we propose a decentralized thermal-aware scheduling algorithm to address this problem in large-scale systems. Experiment results on various benchmarks show that our decentralized algorithm achieves significant improvement on scalability (up to 84.3% reduction in monitoring traffic) and similar benefits on temperature reduction (by 5%) when compared with the state-of-the-art thermal-aware scheduling algorithm.