Managing power demand and load imbalance to save energy on systems with heterogeneous CPU speeds

Different simulations of real problems have been executed in High Performance Computing systems. However, the power consumption of these systems is an increasing concern once more energy are consumed to large simulations. In this context, load balancers emerge as a promising alternative for supporting the computational science methods. In response to this challenge, we developed a new heterogeneous energy-aware load balancer called H-ENERGYLB to reduce the average power demand of systems with heterogeneous processors and save energy when scientific applications with imbalanced load are executed. Our new load balancing strategy combines dynamic load balancing with DVFS techniques to mitigate the imbalanced workloads in order to reduce the clock frequency of underloaded computing cores which experience some residual imbalance even after tasks are remapped. Experiments with three applications on two different heterogeneous architectures show that H-ENERGYLB results in power reductions of 7.14% in average with the energy saving of 36.6% in average compared to others load balancers.