DescriptionEnergy efficiency has become increasingly important in high performance computing (HPC), as power constraints and costs escalate. Workload and system characteristics form a complex optimization search space in which optimal settings for energy efficiency and performance often diverge. Thus, we must identify trade-off options to find the desired balance. We present an innovative statistical model that accurately predicts the Pareto optimal trade-off options using only user-controllable parameters. Our approach can also tolerate both measurement and model errors. We study model training and validation using several HPC kernels, then with more complex workloads, including AMG and LAMMPS. We can calibrate an accurate model from as few as 12 runs, with prediction error of less than 10%. Our results identify trade-off options allowing up to 40% energy efficiency improvement at the cost of under 20% performance loss. For AMG, we reduce the required sample measurement time from 13 hours to 74 minutes.