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Sustainability in high performance computing (HPC) is a major challenge notonly for HPC centers and their users, but also for society as the climate goalsbecome stricter. A lot of effort went into reducing the energy consumption ofsystems in general. Even though certain efforts to optimize theenergy-efficiency of HPC workloads exist, most such efforts propose solutionstargeting CPUs. As HPC systems shift more and more to GPU-centricarchitectures, simulation codes increasingly adopt GPU-programming models. Thisleads to an urgent need to increase the energy-efficiency of GPU-enabled codes.However, studies for reducing the energy consumption of large-scale simulationsexecuting on CPUs and GPUs have received insufficient attention. In this work,we enable accurate power and energy measurements using an open-source toolkitacross a range of CPU+GPU node architectures. We use this approach in SPH-EXA,an open-source GPU-centric astrophysical and cosmological simulation framework.We show that with simple code instrumentation, users can accurately measurepower and energy related data about their application, beyond data provided byHPC systems alone. The accurate power and energy data provide significantinsight to users for conducting energy-aware computational experiments andfuture energy-aware code development.

Accurate Measurement of Application-level Energy Consumption for Energy-Aware Large-Scale Simulations