Thermal aware automated load balancing for HPC applications

As we move towards the exascale era, power and energy have become major challenges. Some of the supercomputers draw more than 10 megawatts, leading to high energy bills. A significant portion of this energy is spent in cooling. In this paper, we propose an adaptive control system that minimizes the cooling energy by using Dynamic Voltage and Frequency Scaling to control the temperature and performing load balancing. This framework, which is a part of the adaptive runtime system, monitors the system and application characteristics and triggers mechanism to limit the temperature. It also performs load balancing whenever imbalance is detected and load balancing is beneficial. We demonstrate, using a set of applications and benchmarks, that the proposed framework can control the temperature of the cores effectively and reduce the timing penalty automatically without any support from the user.