Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms

TL;DR

This study compares the energy consumption of Python and R in machine learning tasks, revealing significant differences that impact environmental sustainability and resource efficiency.

Contribution

It provides the first comprehensive empirical comparison of energy use between Python and R for ML training and inference tasks.

Findings

Python and R show significant energy consumption differences in ML tasks.

Energy efficiency varies up to 99.16% during training and 99.8% during inference.

The choice of programming language can greatly influence the environmental impact of ML.

Abstract

The utilization of Machine Learning (ML) in contemporary software systems is extensive and continually expanding. However, its usage is energy-intensive, contributing to increased carbon emissions and demanding significant resources. While numerous studies examine the performance and accuracy of ML, only a limited few focus on its environmental aspects, particularly energy consumption. In addition, despite emerging efforts to compare energy consumption across various programming languages for specific algorithms and tasks, there remains a gap specifically in comparing these languages for ML-based tasks. This paper aims to raise awareness of the energy costs associated with employing different programming languages for ML model training and inference. Through this empirical study, we measure and compare the energy consumption along with run-time performance of five regression and five classification tasks implemented in Python and R, the two most popular programming languages in this context. Our study results reveal a statistically significant difference in costs between the two languages in 95% of the cases examined. Furthermore, our analysis demonstrates that the choice of programming language can influence energy efficiency significantly, up to 99.16% during model training and up to 99.8% during inferences, for a given ML task.