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.

Abstract-This paper presents a detailed study of the energy consumption of the different Java Collection Framework (JFC) implementations. For each method of an implementation in this framework, we present its energy consumption when handling different amounts of data. Knowing the greenest methods for each implementation, we present an energy optimization approach for Java programs: based on calls to JFC methods in the source code of a program, we select the greenest implementation. Finally, we present preliminary results of optimizing a set of Java programs where we obtained 6.2% energy savings.

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 language. 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. CCS CONCEPTS • Software and its engineering → Software performance; General programming languages;

The design of new advanced materials and technologies is essential for the development of smart windows for the next generation of energy-efficient buildings. Here, it is demonstrated that the functionalization of glucose-derived carbon dots with 1-butyl-3-methylimidazolium chloride results in a self-standing, water-soluble, viscous, reusable nanofluid with self-improving conductivity, thermotropy around 30-40 °C, and ultraviolet blocking ability. Its synthesis is straightforward, clean, fast, and cheap. At 36 °C (hot summer day), a sunactuated thermotropic (TT) device incorporating a 95% w/w nanofluid aqueous solution exhibits a transmittance variation (ΔT) of 9% at 550/1000 nm, which is amplified to 47/31% via the surface plasmon resonance effect. An integrated self-healing system enabling independent sun-actuated TT and voltage-actuated electrochromic (EC) operation is also produced. The low-energy EC device offers bright hot and dark cold modes (ΔT = 68/64%), excellent cycling stability, unprecedented coloration efficiency values (−1.73 × 10 6 /−1.67 × 10 6 cm 2 C −1 (coloring) and +1.12 × 10 7 /+1.08 × 10 7 cm 2 C −1 (bleaching) at ±2.5 V), and impressive memory effect. The disruptive design and sustainable synthesis of the new nanofluid proposed here will foster the agile development of novel products with improved ecological footprint.

This paper briefly proposes a technique to detect energy inefficient fragments in the source code of a software system. Test cases are executed to obtain energy consumption measurements, and a statistical method, based on spectrum-based fault localization, is introduced to relate energy consumption to the system's source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code.

This paper compares a large set of programming languages regarding their efficiency, including from an energetic point-of-view. Indeed, we seek to establish and analyze different rankings for programming languages based on their energy efficiency. The goal of being able to rank languages with energy in mind is a recent one, and certainly deserves further studies.We have taken 19 solutions to well defined programming problems, expressed in (up to) 27 programming languages, from well know repositories such as the Computer Language Benchmark Game and Rosetta Code. We have also built a framework to automatically, and systematically, run, measure and compare the efficiency of such solutions. Ultimately, it is based on such comparison that we propose a serious of efficiency rankings, based on multiple criteria.Our results show interesting findings, such as, slower/faster languages consuming less/more energy, and how memory usage influences energy consumption. We also show how to use our results to provide software engineers support to decide which language to use when energy efficiency is a concern.

A sustainable use of energy in buildings demands energy‐efficient windows. A new design concept for electrochromic (EC) smart windows, easy to implement at the industrial level, is introduced here. It enables simultaneous control of visible and near‐infrared (NIR) solar radiation, thus contributing to reduce heating and cooling loads especially in buildings located in areas experiencing wide daily temperature ranges. The EC device comprises amorphous indium zinc oxide, a conducting oxide transparent in the visible and NIR spectral regions, as nonactive layer, and a sol–gel protonic ionic liquid‐doped di‐ureasil electrolyte displaying high transparency and proton conductivity. The device offers three voltage‐operated modes: bright hot (+3.0 V: transmittances of 70/83% at 555/1000 nm), semi‐bright warm (−2.0 V: transmittances of 37/35% at 555/1000 nm), and dark cold (−2.5 V: transmittances of 6/4% at 555/1000 nm). Its main figures of merit are: high switching efficiency (transmittance variations of 64/79% at 555/1000 nm), high optical density modulation (1.1/1.3 at 555/1000 nm), high optical contrast ratio in the visible region (lightness variation of ≈43), good cycling stability, and unprecedented coloration efficiency (−12538/−14818 cm2 C−1 and +2901/+3428 cm2 C−1 at 555/1000 nm), outstanding optical memory (transmittance variation loss of only 24% more than 4 months after coloration), and self‐healing ability following mechanical stress.

Although hardware is generally seen as the main culprit for a computer's energy usage, software too has a tremendous impact on the energy spent. Unfortunately, there is still not enough support for software developers so they can make their code more energy-aware.This paper proposes a technique to detect energy inefficient fragments in the source code of a software system. Test cases are executed to obtain energy consumption measurements, and a statistical method, based on spectrumbased fault localization, is introduced to relate energy consumption to the source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code. This technique was implemented in the SPELL toolkit.Finally, in order to evaluate our technique, we conducted an empirical study where we asked participants to optimize the energy efficiency of a software system using our tool, while also having two other groups using no tool assistance and a profiler, respectively. We showed statistical evidence that developers using our technique were able to improve the energy efficiency by 43% on average, and even out performing a profiler for energy optimization.