Predicting energy consumption relevant indicators of strong scaling HPC applications for different compute resource configurations

Finding the best energy-performance tradeoffs for High Performance Computing (HPC) applications is a major challenge for many modern supercomputing centers. With the increased focus on data center energy efficiency and the emergence of possible data center power constraints, making the right decision at a given time is becoming more important. A real-world situation like "can a given 1000 compute node application be executed at a maximum of'2.7 GHz CPU frequency without going over the energy provider defined power band, or the available monthly energy limit?" is just one example of the types of decisions HPC data centers will face. The previously developed Adaptive Energy and Power Consumption Prediction (AEPCP) model answers this question for the case of a fixed CPU frequency. This paper will extend the AEPCP process to enable the development of analytical models for estimating application execution time, power, and energy consumptions as functions of the number of compute nodes and maximum operating CPU frequency. Based on these analytical models, an adaptive model (Lightweight Adaptive Consumption Prediction (LACP)) is presented that implements the extended prediction process. This information allows for improved estimation of potential energy-performance costs and tradeoffs of applications and thus identifies the optimal resource configuration for specific data center boundary conditions.