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Future large scale high performance supercomputer systems require high energy ef?ciency to achieve exa?ops computational power and beyond. Despite the need to understand energy ef?ciency in high-performance systems, there are few techniques to evaluate energy ef?ciency at scale. In this paper, we propose a system-level iso-energy-ef?ciency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy ef?ciency and isolate ef?cient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-ef?ciency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making.
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