Embracing Load Imbalance for Energy Optimizations: a Case-Study

Scientific computing is a significant consumer of supercomputing resources, and, as a consequence, performance optimization has been a long-term goal of the high-performance computing (HPC) community. However, as the complexity and computational demands of modern scientific applications grow, optimizing energy efficiency becomes critical to balance computational throughput with power constraints.To address this challenge, we propose and evaluate a methodology to improve the energy efficiency of large-scale simulations running on multi-node computing systems. Our approach is based on a key observation: when load-imbalance during a large-scale simulation is difficult to avoid or fix, it can at least be exploited to reduce the energy consumption of the simulation. This can be achieved by reducing the CPU frequency of light-loaded nodes to reduce their energy consumption, while incurring minimal overhead and no overall increase in execution time.We demonstrate this approach in practice through a case-study based on HemoCell, a large-scale scientific framework for cell-resolved blood flow simulation. We show that reducing the node frequency to match the workload proportion per node does reduce the overall energy consumption of the simulation, while only causing a negligible increase in its execution time. For our case-study we observe energy reductions of up to 23% and minimal performance loss compared to the same workloads without frequency scaling.