With the increasing complexity of modern software and the demand for high performance, energy consumption has become a critical factor for developers and researchers. While much of the research community focuses on evaluating the energy consumption of machine learning and artificial intelligence systems, often implemented in Python, a gap exists in tools and frameworks for measuring energy usage in other programming languages. C++, in particular, remains a foundational language for a wide range of software applications, from game development to parallel programming frameworks, yet lacks dedicated energy measurement solutions. To address this, we have developed CPPJoules, a tool built on top of Intel-RAPL to measure the energy consumption of C++ code snippets. We have evaluated the tool by measuring the energy consumption of six standard computational tasks from the Rosetta Code repository. The demonstration of the tool is available at https://youtu.be/6EpoE5COyGI and related artifacts are open-sourced at https://rishalab.github.io/CPPJoules/.

In the dynamic landscape of contemporary data-driven technologies, software systems depend significantly on vast datasets and ongoing data center operations that utilize diverse database systems to facilitate computationally intensive tasks. The management of vast amounts of data also introduces challenges related to energy efficiency. With the growing concern over energy consumption in software systems, the selection of a Green database system for its energy efficiency becomes crucial. While various software components have been scrutinized for their energy consumption, there exists a gap in the software engineering literature concerning the energy efficiency of database management systems. To bridge this gap, we performed an empirical study to investigate the energy consumption of queries associated with popular database systems namely MySQL, PostgreSQL, MongoDB, and Couchbase. Our assessments, performed on three commonly used datasets, uncover substantial variations in the energy consumption of these database systems. The study suggests a potential need for optimizing energy usage in various database systems, enhancing developer awareness of the impact of running queries on energy consumption. This empowers them to make informed, sustainable choices, warranting further research in this area.

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose “RJoules”, a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://youtu.be/yMKFuvAM-DE and related artifacts at https://rishalab.github.io/RJoules/.