Runtime power usage estimation of HPC servers for various classes of real-life applications

• We propose a general method for software estimation of power usage.
• We classify real-life applications and provide specialized power usage estimation models for all classes.
• We enable application clustering and power usage estimation at runtime.
• Presented models are accurate within a mean error of 4%.

In this paper we propose a methodology underlying a development of system-wide energy consumption models for servers, which is based on the analysis of performance counters. It enables to estimate the power usage of a machine under any load at runtime. By clustering applications we extract groups of programs having similar characteristics. This allows us to create more specialized and accurate power usage models. By using decision trees it is possible to automatically select an appropriate model to current system load. Training and test sets of programs were used to test the estimates. The presented models are accurate within an error of 4% as verified on servers from different vendors, including the latest pre-production one.