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F. Heinrich, T. Cornebize, A. Degomme, A. Legrand, A. Carpen-Amarie, S. Hunold, A. Orgerie, M. Quinson:
"Predicting the Energy-Consumption of MPI Applications at Scale Using a Single Node";
Talk: IEEE International Conference on Cluster Computing (CLUSTER 2017), Honolulu, Hawaii, USA; 2017-09-05 - 2017-09-08; in: "Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER 2017)", IEEE, (2017), ISBN: 978-1-5386-2326-8; 92 - 102.

Monitoring and assessing the energy efficiency of supercomputers and data centers is crucial in order to limit and reduce their energy consumption. Applications from the domain of High Performance Computing (HPC), such as MPI applications, account for a significant fraction of the overall energy consumed by HPC centers. Simulation is a popular approach for studying the behavior of these applications in a variety of scenarios, and it is therefore advantageous to be able to study their energy consumption in a cost-efficient, controllable, and also reproducible simulation environment. Alas, simulators supporting HPC applications commonly lack the capability of predicting the energy consumption, particularly when target platforms consist of multi-core nodes. In this work, we aim to accurately predict the energy consumption of MPI applications via simulation. Firstly, we introduce the models required for meaningful simulations: The computation model, the communication model, and the energy model of the target platform. Secondly, we demonstrate that by carefully calibrating these models on a single node, the predicted energy consumption of HPC applications at a larger scale is very close (within a few percents) to real experiments. We further show how to integrate such models into the SimGrid simulation toolkit. In order to obtain good execution time predictions on multi-core architectures, we also establish that it is vital to correctly account for memory effects in simulation. The proposed simulator is validated through an extensive set of experiments with wellknown HPC benchmarks. Lastly, we show the simulator can be used to study applications at scale, which allows researchers to save both time and resources compared to real experiments.

http://dx.doi.org/10.1109/CLUSTER.2017.66