Future supercomputers will consume enormous amounts of energy. These very large scale systems will gather many homogeneous clusters. In this paper, we analyze the power consumption of the nodes from different homogeneous clusters during different workloads. We classically observe that these nodes exhibit the same level of performance. But we also show that different nodes from a homogeneous cluster may exhibit heterogeneous idle power energy consumption even if they are made of identical hardware. Hence, we propose an experimental methodology to understand such differences. We show that CPUs are responsible for such heterogeneity which can reach 20% in terms of energy consumption. So energy aware (Green) schedulers must take care of such hidden heterogeneity in order to propose efficient mapping of tasks. To consume less energy, we propose an energy-aware scheduling approach taking into account the heterogeneous idle power consumption of homogeneous nodes. It shows that we are able to save energy up to 17% while exploiting the high power heterogeneity that may exist in some homogeneous clusters.
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