Investigating the Energy Consumption of C++ and Java Solutions Mined from a Programming Contest Site

TL;DR

This study analyzes energy consumption of C++ and Java solutions from a programming contest site, confirming that faster programs generally consume less energy and developing models to predict and classify energy profiles.

Contribution

It provides empirical evidence linking execution speed to energy efficiency in contest solutions and introduces a linear model for energy prediction and abnormality detection.

Findings

Faster programs tend to consume less energy.

A linear model accurately relates execution time to energy consumption.

Energy profiles can help classify solutions by problem.

Abstract

The concern about global warming increased the interest in the energy efficiency of computer applications. Assuming power is constant, the general trend is that faster programs consume less energy, thus optimizing a program for speed would also improve its energy efficiency. We investigate this tendency in a set of C++ and Java solutions mined from Code Submission Evaluation System (CSES), a popular programming competition site, where each solution must give the correct answer under a given time limit. In such context, we can consider that all correct solutions for a problem were written with a speed concern, but not with energy efficiency in mind. We selected 15 problems from CSES and for each of them we mined at least 30 C++ and Java solutions, evaluating time and energy efficiency of each solution in at least two different machines. In our scenario, where there is a great diversity of programming styles, execution speed, and memory usage, we could confirm the general trend: faster programs consume less energy. Moreover, we were able to use ordinary least squares to fit a linear function, with good precision, that relates energy consumption of a program based on its execution time, as well as to automatically identify programs with abnormal energy consumption. A manual analysis of these programs revealed that often they perform a different amount of allocation and deallocation operations when compared to programs with similar execution times. We also calculated the energy consumption profile of sets of random C++ solutions for these 15 CSES problems, and we tried to associate each set with its corresponding CSES problem by using the energy consumption profiles previously computed for each one of them. With this approach, we restricted, for each set of random C++ solutions, the classification task to a subset of 7 CSES problems, a reduction of more than 50% in the search space.