Modelling Program's Performance with Gaussian Mixtures for Parametric Statistics

This article is a continuation of our previous research effort on program performance statistical analysis and comparison [1], in the presence of program performance variability. In the previous study, we proposed a formal statistical methodology to analyze program speedups based on mean and median performance metrics: execution time, energy consumption, etc. However, mean and median observed performances do not always reflect the user's feeling of such performances, especially when they are particularly unstable. In the current study, we propose additional precise performance metrics, based on performance modelling using Gaussian mixtures. Our additional statistical metrics for analyzing and comparing program performances give the user more precise decision tools to select best code versions, not necessarily based on mean or median numbers. Also, we provide a new metric to estimate performance variability based on the Gaussian mixture model. Our statistical methods are implemented with R and distributed as open source code.