Energy Efficiency across Programming Languages: How does energy, time, and memory relate? – Rui Pereira, Marco Couto, Francisco Ribeiro, Rui Rua, Jácome Cunha, João Paulo Fernandes, João Saraiva

This paper presents a study of the runtime, memory usage and energy consumption of twenty seven well-known software languages. We monitor the performance of such languages using ten different programming problems, expressed in each of the languages. Our results show interesting findings, such as, slower/faster languages consuming less/more energy, and how memory usage influences energy consumption. We show how to use our results to provide software engineers support to decide which language to use when
energy efficiency is a concern.

Towards a Green Ranking for Programming Languages – Marco Couto, Rui Pereira, Francisco Ribeiro, Rui Rua, João Saraiva

While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. Additionally, a growing number of developers wish to become more energy-aware when programming and feel a lack of tools and the knowledge to do so.
In this paper we define a ranking of energy efficiency in programming languages. We consider a set of computing problems implemented in ten well-known programming languages, and monitored the energy consumed when executing each langauge. Our preliminary results show that although the fastest languages tend to be the lowest consuming ones, there are other interesting cases where slower languages are more energy efficient than faster ones.

 

Products go Green: Worst-Case Energy Consumption in Software Product Lines -Marco Couto, Rui Pereira, Paulo Borba, Jácome Cunha, João Paulo Fernandes and João Saraiva

The optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly.

In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide software developers with techniques and tools to reason about the energy consumption of all products in a line, without having to produce, run and measure the energy in every combination.

Our technique combines classic static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyses all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product.

We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a realistic product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products.

An Economic Energy Approach For Queries On Data Centers – João Saraiva, Miguel Guimarães, Orlando Belo

Energy consumption is an issue that involves all of us, both as individuals and as members of a society, and covers all our areas of activity. It is something so broad that its impact has important reflections on our social, cultural and financial structures. The domain of software, and in particular database systems, is not an exception. Although it seems to be a little bit strange to study the energy consumption of just one query, when we consider the execution of a a few thousand queries per second, quickly we see the importance of the querying consumption in the monthly account of any company that has a conventional data center.

To demonstrate the energy consumption of queries in data centers, we idealized a small dashboard for monitoring and analyzing the sales of a company, and implemented all the queries needed for populating it and ensuring its operation. The queries were organized into two groups, oriented especially to two distinct database management systems: one relational (MySQL) and one non relational (Neo4J).

The goal is to evaluate the energy consumption of different types of queries, and at the same time compare it in terms of relational and non-relational database approaches. This paper relates the process we implemented to set up the energy consumption application scenario, measure the energy consumption of each query, and present our first preliminary results.

Leafactor: Improving Energy Efficiency of Android Apps via Automatic Refactoring – Luis Cru, Rui Abreu and Jean-Nöel Rouvignac

Leafactor is a tool to automatically improve the energy consumption of Android apps. It does so by refactoring the source code to follow a set of patterns known to be energy efficient. The toolset was validated using 222 refactorings in 140 open-source apps. Changes were submitted to the original apps by creating pull requests to the official projects.