Modeling, Prediction and Optimization of Energy Consumption of MPI Applications using SimGrid

Abstract : The High-Performance Computing (HPC) community is currently undergoingdisruptive technology changes in almost all fields, including a switch towardsmassive parallelism with several thousand compute cores on a single GPU oraccelerator and new, complex networks. Powering a massively parallel machinebecomesThe energy consumption of these machines will continue to grow in the future,making energy one of the principal cost factors of machine ownership. This explainswhy even the classic metric "flop/s", generally used to evaluate HPC applicationsand machines, is widely regarded as to be replaced by an energy-centric metric"flop/watt".One approach to predict energy consumption is through simulation, however, a pre-cise performance prediction is crucial to estimate the energy faithfully. In this thesis,we contribute to the performance and energy prediction of HPC architectures. Wepropose an energy model which we have implemented in the open source SimGridsimulator. We validate this model by carefully and systematically comparing itwith real experiments. We leverage this contribution to both evaluate existingand propose new DVFS governors that are part*icularly designed to suit the HPCcontext.
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Franz Heinrich. Modeling, Prediction and Optimization of Energy Consumption of MPI Applications using SimGrid. Modeling and Simulation. Université Grenoble Alpes, 2019. English. ⟨NNT : 2019GREAM018⟩. ⟨tel-02269894⟩

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