Exploration of Load Balancing Thresholds to Save Energy on Iterative Applications

The power consumption of High Performance Computing systems is an increasing concern as large-scale systems grow in size and, consequently, consume more energy. In response to this challenge, we proposed two variants of a new energy-aware load balancer that aim at reducing the energy consumption of parallel platforms running imbalanced scientific applications without degrading their performance. Our research combines Dynamic Load Balancing with Dynamic Voltage and Frequency Scaling techniques in order to reduce the clock frequency of underloaded computing cores which experience some residual imbalance even after tasks are remapped. This work presents a trade-off evaluation between runtime, power demand and total energy consumption when applying these two energy-aware load balancer variants on real-world applications. In this way, we can define which is the best threshold value for each application under the total energy consumption, total execution time or the average power demand focus.